importance of online education in covid 19

Capturing the benefits of remote learning

How education experts are applying lessons learned in the pandemic to promote positive outcomes for all students

Vol. 52 No. 6 Print version: page 46

Schools and Classrooms

boy sitting in front of a laptop in his bedroom

With schools open again after more than a year of teaching students outside the classroom, the pandemic sometimes feels like a distant memory. The return to classrooms this fall brings major relief for many families and educators. Factors such as a lack of reliable technology and family support, along with an absence of school resources, resulted in significant academic setbacks, not to mention stress for everyone involved.

But for all the downsides of distance learning, educators, psychologists, and parents have seen some benefits as well. For example, certain populations of students found new ways to be more engaged in learning, without the distractions and difficulties they faced in the classroom, and the general challenges of remote learning and the pandemic brought mental health to the forefront of the classroom experience.

Peter Faustino, PsyD, a school psychologist in Scarsdale, New York, said the pandemic also prompted educators and school psychologists to find creative new ways of ensuring students’ emotional and academic well-being. “So many students were impacted by the pandemic, so we couldn’t just assume they would find resources on their own,” said Faustino. “We had to work hard at figuring out new ways to connect with them.”

Here are some of the benefits of distance learning that school psychologists and educators have observed and the ways in which they’re implementing those lessons post-pandemic, with the goal of creating a more equitable, productive environment for all students.

Prioritizing mental health

Faustino said that during the pandemic, he had more mental health conversations with students, families, and teachers than ever. “Because COVID-19 affected everyone, we’re now having mental health discussions as school leaders on a daily and weekly basis,” he said.

This renewed focus on mental health has the potential to improve students’ well-being in profound ways—starting with helping them recover from the pandemic’s effects. In New York City, for example, schools are hiring more than 600 new clinicians, including psychologists , to screen students’ mental health and help them process pandemic-related trauma and adjust to the “new normal” of attending school in person.

Educators and families are also realizing the importance of protecting students’ mental health more generally—not only for their health and safety but for their learning. “We’ve been seeing a broader appreciation for the fact that mental health is a prerequisite for learning rather than an extracurricular pursuit,” said Eric Rossen, PhD, director of professional development and standards at the National Association of School Psychologists.

As a result, Rossen hopes educators will embed social and emotional learning components into daily instruction. For example, teachers could teach mindfulness techniques in the classroom and take in-the-moment opportunities to help kids resolve conflicts or manage stress.

Improved access to mental health resources in schools is another positive effect. Because of physical distancing guidelines, school leaders had to find ways to deliver mental health services remotely, including via online referrals and teletherapy with school psychologists and counselors.

Early in the pandemic, Faustino said he was hesitant about teletherapy’s effectiveness; now, he hopes to continue offering a virtual option. Online scheduling and remote appointments make it easier for students to access mental health resources, and some students even enjoy virtual appointments more, as they can attend therapy in their own spaces rather than showing up in the counselor’s office. For older students, Faustino said that level of comfort often leads to more productive, open conversations.

Autonomy as a key to motivation

Research suggests that when students have more choices about their materials and activities, they’re more motivated—which may translate to increased learning and academic success. In a 2016 paper, psychology researcher Allan Wigfield, PhD, and colleagues make the case that control and autonomy in reading activities can improve both motivation and comprehension ( Child Development Perspectives , Vol. 10, No. 3 ).

During the period of online teaching, some students had opportunities to learn at their own pace, which educators say improved their learning outcomes—especially in older students. In a 2020 survey of more than 600 parents, researchers found the second-most-valued benefit of distance learning was flexibility—not only in schedule but in method of learning.

In a recent study, researchers found that 18% of parents pointed to greater flexibility in a child’s schedule or way of learning as the biggest benefit or positive outcome related to remote learning ( School Psychology , Roy, A., et al., in press).

This individualized learning helps students find more free time for interests and also allows them to conduct their learning at a time they’re most likely to succeed. During the pandemic, Mark Gardner, an English teacher at Hayes Freedom High School in Camas, Washington, said he realized how important student-centered learning is and that whether learning happens should take precedence over how and when it occurs.

For example, one of his students thrived when he had the choice to do work later at night because he took care of his siblings during the day. Now, Gardner posts homework online on Sundays so students can work at their own pace during the week. “Going forward, we want to create as many access points as we can for kids to engage with learning,” he said.

Rosanna Breaux , PhD, an assistant professor of psychology and assistant director of the Child Study Center at Virginia Tech, agrees. “I’d like to see this flexibility continue in some way, where—similar to college—students can guide their own learning based on their interests or when they’re most productive,” she said.

During the pandemic, many educators were forced to rethink how to keep students engaged. Rossen said because many school districts shared virtual curricula during the period of remote learning, older students could take more challenging or interesting courses than they could in person. The same is true for younger students: Megan Hibbard, a teacher in White Bear Lake, Minnesota, said many of her fifth graders enjoyed distance learning more than in-person because they could work on projects that aligned with their interests.

“So much of motivation is discovering the unique things the student finds interesting,” said Hunter Gehlbach, PhD, a professor and vice dean at the Johns Hopkins School of Education. “The more you can facilitate students spending more time on the things they’re really interested in, the better.”

Going forward, Rossen hopes virtual curricula will allow students greater opportunities to pursue their interests, such as by taking AP classes, foreign languages, or vocational electives not available at their own schools.

Conversely, Hibbard’s goal is to increase opportunities for students to pursue their interests in the in-person setting. For example, she plans to increase what she calls “Genius Hours,” a time at the end of the school day when students can focus on high-interest projects they’ll eventually share with the class.

Better understanding of children's needs

One of the most important predictors of a child’s success in school is parental involvement in their education. For example, in a meta-analysis of studies, researchers linked parental engagement in their middle schoolers’ education with greater measures of success (Hill, N. E., & Tyson, D. F., Developmental Psychology , Vol. 45, No. 3, 2009).

During the pandemic, parents had new opportunities to learn about their kids and, as a result, help them learn. According to a study by Breaux and colleagues, many parents reported that the pandemic allowed them a better understanding of their child’s learning style, needs, or curriculum.

James C. Kaufman , PhD, a professor of educational psychology at the University of Connecticut and the father of an elementary schooler and a high schooler, said he’s had a front-row seat for his sons’ learning for the first time. “Watching my kids learn and engage with classmates has given me some insight in how to parent them,” he said.

Stephen Becker , PhD, a pediatric psychologist at Cincinnati Children’s Hospital Medical Center, said some parents have observed their children’s behavior or learning needs for the first time, which could prompt them to consider assessment and Individualized Education Program (IEP) services. Across the board, Gehlbach said parents are realizing how they can better partner with schools to ensure their kids’ well-being and academic success.

For example, Samantha Marks , PsyD, a Florida-based clinical psychologist, said she realized how much help her middle school daughter, a gifted and talented student with a 504 plan (a plan for how the school will offer support for a student’s disability) for anxiety, needed with independence. “Bringing the learning home made it crystal clear what we needed to teach our daughter to be independent and improve executive functioning” she said. “My takeaway from this is that more parents need to be involved in their children’s education in a healthy, helpful way.”

Marks also gained a deeper understanding of her daughter’s mental health needs. Through her 504 plan, she received help managing her anxiety at school—at home, though, Marks wasn’t always available to help, which taught her the importance of helping her daughter manage her anxiety independently.

Along with parents gaining a deeper understanding of their kids’ needs, the pandemic also prompted greater parent participation in school. For example, Rossen said his kids’ school had virtual school board meetings; he hopes virtual options continue for events like back-to-school information sessions and parenting workshops. “These meetings are often in the evening, and if you’re a single parent or sole caregiver, you may not want to pay a babysitter in order to attend,” he said.

Brittany Greiert, PhD, a school psychologist in Aurora, Colorado, says culturally and linguistically diverse families at her schools benefited from streamlined opportunities to communicate with administrators and teachers. Her district used an app that translates parent communication into 150 languages. Parents can also remotely participate in meetings with school psychologists or teachers, which Greiert says she plans to continue post-pandemic.

Decreased bullying

During stay-at-home orders, kids with neurodevelopmental disorders experienced less bullying than pre-pandemic (McFayden, T. C., et al., Journal of Rural Mental Health , No. 45, Vol. 2, 2021). According to 2019 research, children with emotional, behavioral, and physical health needs experience increased rates of bullying victimization ( Lebrun-Harris, L. A., et al., ), and from the U.S. Department of Education suggests the majority of bullying takes place in person and in unsupervised areas (PDF) .

Scott Graves , PhD, an associate professor of educational studies at The Ohio State University and a member of APA’s Coalition for Psychology in Schools and Education (CPSE), said the supervision by parents and teachers in remote learning likely played a part in reducing bullying. As a result, he’s less worried his Black sons will be victims of microaggressions and racist behavior during online learning.

Some Asian American families also report that remote learning offered protection against racism students may have experienced in person. Shereen Naser, PhD, an associate professor of psychology at Cleveland State University and a member of CPSE, and colleagues found that students are more comfortable saying discriminatory things in school when their teachers are also doing so; Naser suspects this trickle-down effect is less likely to happen when students learn from home ( School Psychology International , 2019).

Reductions in bullying and microaggressions aren’t just beneficial for students’ long-term mental health. Breaux said less bullying at school results in less stress, which can improve students’ self-esteem and mood—both of which impact their ability to learn.

Patricia Perez, PhD, an associate professor of international psychology at The Chicago School of Professional Psychology and a member of CPSE, said it’s important for schools to be proactive in providing spaces for support and cultural expression for students from vulnerable backgrounds, whether in culture-specific clubs, all-school assemblies that address racism and other diversity-related topics, or safe spaces to process feelings with teachers.

According to Rossen, many schools are already considering how to continue supporting students at risk for bullying, including by restructuring the school environment.

One principal, Rossen said, recently switched to single-use bathrooms to avoid congregating in those spaces once in-person learning commences to maintain social distancing requirements. “The principal received feedback from students about how going to the bathroom is much less stressful for these students in part due to less bullying,” he said.

More opportunities for special needs students

In Becker and Breaux’s research, parents of students with attention-deficit/hyperactivity disorder (ADHD), particularly those with a 504 plan and IEP, reported greater difficulties with remote learning. But some students with special learning needs—including those with IEPs and 504 plans—thrived in an at-home learning environment. Recent reporting in The New York Times suggests this is one reason many students want to continue online learning.

According to Cara Laitusis, PhD, a principal research scientist at Educational Testing Service ( ETS ) and a member of CPSE, reduced distractions may improve learning outcomes for some students with disabilities that impact attention in a group setting. “In assessments, small group or individual settings are frequently requested accommodations for some students with ADHD, anxiety, or autism. Being in a quiet place alone without peers for part of the instructional day may also allow for more focus,” she said. However, she also pointed out the benefits of inclusion in the classroom for developing social skills with peers.

Remote learning has improved academic outcomes for students with different learning needs, too. Marks said her seventh-grade daughter, a visual learner, appreciated the increase in video presentations and graphics. Similarly, Hibbard said many of her students who struggle to grasp lessons on the first try have benefited from the ability to watch videos over again until they understand. Post-pandemic, she plans to record bite-size lessons—for example, a 1-minute video of a long division problem—so her students can rewatch and process at their own rate.

Learners with anxiety also appreciate the option not to be in the classroom, because the social pressures of being surrounded by peers can make it hard to focus on academics. “Several of my students have learned more in the last year simply due to the absence of anxiety,” said Rosie Reid, an English teacher at Ygnacio Valley High School in Concord, California, and a 2019 California Teacher of the Year. “It’s just one less thing to negotiate in a learning environment.”

On online learning platforms, it’s easier for kids with social anxiety or shyness to participate. One of Gardner’s students with social anxiety participated far more in virtual settings and chats. Now, Gardner is brainstorming ways to encourage students to chat in person, such as by projecting a chat screen on the blackboard.

Technology has helped school psychologists better engage students, too. For example, Greiert said the virtual setting gave her a new understanding of her students’ personalities and needs. “Typing out their thoughts, they were able to demonstrate humor or complex thoughts they never demonstrated in person,” she said. “I really want to keep incorporating technology into sessions so kids can keep building on their strengths.”

Reid says that along with the high school students she teaches, she’s seen her 6-year-old daughter benefit from learning at her own pace in the familiarity of her home. Before the pandemic, she was behind academically, but by guiding her own learning—writing poems, reading books, playing outside with her siblings—she’s blossomed. “For me, as both a mother and as a teacher, this whole phenomenon has opened the door to what education can be,” Reid said.

Eleanor Di Marino-Linnen, PhD, a psychologist and superintendent of the Rose Tree Media School District in Media, Pennsylvania, says the pandemic afforded her district a chance to rethink old routines and implement new ones. “As challenging as it is, it’s definitely an exciting time to be in education when we have a chance to reenvision what schools have looked like for many years,” she said. “We want to capitalize on what we’ve learned.”

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Why lockdown and distance learning during the COVID-19 pandemic are likely to increase the social class achievement gap

Sébastien Goudeau ORCID: orcid.org/0000-0001-7293-0977 1 ,
Camille Sanrey ORCID: orcid.org/0000-0003-3158-1306 1 ,
Arnaud Stanczak ORCID: orcid.org/0000-0002-2596-1516 2 ,
Antony Manstead ORCID: orcid.org/0000-0001-7540-2096 3 &
Céline Darnon ORCID: orcid.org/0000-0003-2613-689X 2

Nature Human Behaviour volume 5 , pages 1273–1281 ( 2021 ) Cite this article

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The COVID-19 pandemic has forced teachers and parents to quickly adapt to a new educational context: distance learning. Teachers developed online academic material while parents taught the exercises and lessons provided by teachers to their children at home. Considering that the use of digital tools in education has dramatically increased during this crisis, and it is set to continue, there is a pressing need to understand the impact of distance learning. Taking a multidisciplinary view, we argue that by making the learning process rely more than ever on families, rather than on teachers, and by getting students to work predominantly via digital resources, school closures exacerbate social class academic disparities. To address this burning issue, we propose an agenda for future research and outline recommendations to help parents, teachers and policymakers to limit the impact of the lockdown on social-class-based academic inequality.

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The widespread effects of the COVID-19 pandemic that emerged in 2019–2020 have drastically increased health, social and economic inequalities 1 , 2 . For more than 900 million learners around the world, the pandemic led to the closure of schools and universities 3 . This exceptional situation forced teachers, parents and students to quickly adapt to a new educational context: distance learning. Teachers had to develop online academic materials that could be used at home to ensure educational continuity while ensuring the necessary physical distancing. Primary and secondary school students suddenly had to work with various kinds of support, which were usually provided online by their teachers. For college students, lockdown often entailed returning to their hometowns while staying connected with their teachers and classmates via video conferences, email and other digital tools. Despite the best efforts of educational institutions, parents and teachers to keep all children and students engaged in learning activities, ensuring educational continuity during school closure—something that is difficult for everyone—may pose unique material and psychological challenges for working-class families and students.

Not only did the pandemic lead to the closure of schools in many countries, often for several weeks, it also accelerated the digitalization of education and amplified the role of parental involvement in supporting the schoolwork of their children. Thus, beyond the specific circumstances of the COVID-19 lockdown, we believe that studying the effects of the pandemic on academic inequalities provides a way to more broadly examine the consequences of school closure and related effects (for example, digitalization of education) on social class inequalities. Indeed, bearing in mind that (1) the risk of further pandemics is higher than ever (that is, we are in a ‘pandemic era’ 4 , 5 ) and (2) beyond pandemics, the use of digital tools in education (and therefore the influence of parental involvement) has dramatically increased during this crisis, and is set to continue, there is a pressing need for an integrative and comprehensive model that examines the consequences of distance learning. Here, we propose such an integrative model that helps us to understand the extent to which the school closures associated with the pandemic amplify economic, digital and cultural divides that in turn affect the psychological functioning of parents, students and teachers in a way that amplifies academic inequalities. Bringing together research in social sciences, ranging from economics and sociology to social, cultural, cognitive and educational psychology, we argue that by getting students to work predominantly via digital resources rather than direct interactions with their teachers, and by making the learning process rely more than ever on families rather than teachers, school closures exacerbate social class academic disparities.

First, we review research showing that social class is associated with unequal access to digital tools, unequal familiarity with digital skills and unequal uses of such tools for learning purposes 6 , 7 . We then review research documenting how unequal familiarity with school culture, knowledge and skills can also contribute to the accentuation of academic inequalities 8 , 9 . Next, we present the results of surveys conducted during the 2020 lockdown showing that the quality and quantity of pedagogical support received from schools varied according to the social class of families (for examples, see refs. 10 , 11 , 12 ). We then argue that these digital, cultural and structural divides represent barriers to the ability of parents to provide appropriate support for children during distance learning (Fig. 1 ). These divides also alter the levels of self-efficacy of parents and children, thereby affecting their engagement in learning activities 13 , 14 . In the final section, we review preliminary evidence for the hypothesis that distance learning widens the social class achievement gap and we propose an agenda for future research. In addition, we outline recommendations that should help parents, teachers and policymakers to use social science research to limit the impact of school closure and distance learning on the social class achievement gap.

Economic, structural, digital and cultural divides influence the psychological functioning of parents and students in a way that amplify inequalities.

The digital divide

Unequal access to digital resources.

Although the use of digital technologies is almost ubiquitous in developed nations, there is a digital divide such that some people are more likely than others to be numerically excluded 15 (Fig. 1 ). Social class is a strong predictor of digital disparities, including the quality of hardware, software and Internet access 16 , 17 , 18 . For example, in 2019, in France, around 1 in 5 working-class families did not have personal access to the Internet compared with less than 1 in 20 of the most privileged families 19 . Similarly, in 2020, in the United Kingdom, 20% of children who were eligible for free school meals did not have access to a computer at home compared with 7% of other children 20 . In 2021, in the United States, 41% of working-class families do not own a laptop or desktop computer and 43% do not have broadband compared with 8% and 7%, respectively, of upper/middle-class Americans 21 . A similar digital gap is also evident between lower-income and higher-income countries 22 .

Second, simply having access to a computer and an Internet connection does not ensure effective distance learning. For example, many of the educational resources sent by teachers need to be printed, thereby requiring access to printers. Moreover, distance learning is more difficult in households with only one shared computer compared with those where each family member has their own 23 . Furthermore, upper/middle-class families are more likely to be able to guarantee a suitable workspace for each child than their working-class counterparts 24 .

In the context of school closures, such disparities are likely to have important consequences for educational continuity. In line with this idea, a survey of approximately 4,000 parents in the United Kingdom confirmed that during lockdown, more than half of primary school children from the poorest families did not have access to their own study space and were less well equipped for distance learning than higher-income families 10 . Similarly, a survey of around 1,300 parents in the Netherlands found that during lockdown, children from working-class families had fewer computers at home and less room to study than upper/middle-class children 11 .

Data from non-Western countries highlight a more general digital divide, showing that developing countries have poorer access to digital equipment. For example, in India in 2018, only 10.7% of households possessed a digital device 25 , while in Pakistan in 2020, 31% of higher-education teachers did not have Internet access and 68.4% did not have a laptop 26 . In general, developing countries lack access to digital technologies 27 , 28 , and these difficulties of access are even greater in rural areas (for example, see ref. 29 ). Consequently, school closures have huge repercussions for the continuity of learning in these countries. For example, in India in 2018, only 11% of the rural and 40% of the urban population above 14 years old could use a computer and access the Internet 25 . Time spent on education during school closure decreased by 80% in Bangladesh 30 . A similar trend was observed in other countries 31 , with only 22% of children engaging in remote learning in Kenya 32 and 50% in Burkina Faso 33 . In Ghana, 26–32% of children spent no time at all on learning during the pandemic 34 . Beyond the overall digital divide, social class disparities are also evident in developing countries, with lower access to digital resources among households in which parental educational levels were low (versus households in which parental educational levels were high; for example, see ref. 35 for Nigeria and ref. 31 for Ecuador).

Unequal digital skills

In addition to unequal access to digital tools, there are also systematic variations in digital skills 36 , 37 (Fig. 1 ). Upper/middle-class families are more familiar with digital tools and resources and are therefore more likely to have the digital skills needed for distance learning 38 , 39 , 40 . These digital skills are particularly useful during school closures, both for students and for parents, for organizing, retrieving and correctly using the resources provided by the teachers (for example, sending or receiving documents by email, printing documents or using word processors).

Social class disparities in digital skills can be explained in part by the fact that children from upper/middle-class families have the opportunity to develop digital skills earlier than working-class families 41 . In member countries of the OECD (Organisation for Economic Co-operation and Development), only 23% of working-class children had started using a computer at the age of 6 years or earlier compared with 43% of upper/middle-class children 42 . Moreover, because working-class people tend to persist less than upper/middle-class people when confronted with digital difficulties 23 , the use of digital tools and resources for distance learning may interfere with the ability of parents to help children with their schoolwork.

Unequal use of digital tools

A third level of digital divide concerns variations in digital tool use 18 , 43 (Fig. 1 ). Upper/middle-class families are more likely to use digital resources for work and education 6 , 41 , 44 , whereas working-class families are more likely to use these resources for entertainment, such as electronic games or social media 6 , 45 . This divide is also observed among students, whereby working-class students tend to use digital technologies for leisure activities, whereas their upper/middle-class peers are more likely to use them for academic activities 46 and to consider that computers and the Internet provide an opportunity for education and training 23 . Furthermore, working-class families appear to regulate the digital practices of their children less 47 and are more likely to allow screens in the bedrooms of children and teenagers without setting limits on times or practices 48 .

In sum, inequalities in terms of digital resources, skills and use have strong implications for distance learning. This is because they make working-class students and parents particularly vulnerable when learning relies on extensive use of digital devices rather than on face-to-face interaction with teachers.

The cultural divide

Even if all three levels of digital divide were closed, upper/middle-class families would still be better prepared than working-class families to ensure educational continuity for their children. Upper/middle-class families are more familiar with the academic knowledge and skills that are expected and valued in educational settings, as well as with the independent, autonomous way of learning that is valued in the school culture and becomes even more important during school closure (Fig. 1 ).

Unequal familiarity with academic knowledge and skills

According to classical social reproduction theory 8 , 49 , school is not a neutral place in which all forms of language and knowledge are equally valued. Academic contexts expect and value culture-specific and taken-for-granted forms of knowledge, skills and ways of being, thinking and speaking that are more in tune with those developed through upper/middle-class socialization (that is, ‘cultural capital’ 8 , 50 , 51 , 52 , 53 ). For instance, academic contexts value interest in the arts, museums and literature 54 , 55 , a type of interest that is more likely to develop through socialization in upper/middle-class families than in working-class socialization 54 , 56 . Indeed, upper/middle-class parents are more likely than working-class parents to engage in activities that develop this cultural capital. For example, they possess more books and cultural objects at home, read more stories to their children and visit museums and libraries more often (for examples, see refs. 51 , 54 , 55 ). Upper/middle-class children are also more involved in extra-curricular activities (for example, playing a musical instrument) than working-class children 55 , 56 , 57 .

Beyond this implicit familiarization with the school curriculum, upper/middle-class parents more often organize educational activities that are explicitly designed to develop academic skills of their children 57 , 58 , 59 . For example, they are more likely to monitor and re-explain lessons or use games and textbooks to develop and reinforce academic skills (for example, labelling numbers, letters or colours 57 , 60 ). Upper/middle-class parents also provide higher levels of support and spend more time helping children with homework than working-class parents (for examples, see refs. 61 , 62 ). Thus, even if all parents are committed to the academic success of their children, working-class parents have fewer chances to provide the help that children need to complete homework 63 , and homework is more beneficial for children from upper-middle class families than for children from working-class families 64 , 65 .

School closures amplify the impact of cultural inequalities

The trends described above have been observed in ‘normal’ times when schools are open. School closures, by making learning rely more strongly on practices implemented at home (rather than at school), are likely to amplify the impact of these disparities. Consistent with this idea, research has shown that the social class achievement gap usually greatly widens during school breaks—a phenomenon described as ‘summer learning loss’ or ‘summer setback’ 66 , 67 , 68 . During holidays, the learning by children tends to decline, and this is particularly pronounced in children from working-class families. Consequently, the social class achievement gap grows more rapidly during the summer months than it does in the rest of the year. This phenomenon is partly explained by the fact that during the break from school, social class disparities in investment in activities that are beneficial for academic achievement (for example, reading, travelling to a foreign country or museum visits) are more pronounced.

Therefore, when they are out of school, children from upper/middle-class backgrounds may continue to develop academic skills unlike their working-class counterparts, who may stagnate or even regress. Research also indicates that learning loss during school breaks tends to be cumulative 66 . Thus, repeated episodes of school closure are likely to have profound consequences for the social class achievement gap. Consistent with the idea that school closures could lead to similar processes as those identified during summer breaks, a recent survey indicated that during the COVID-19 lockdown in the United Kingdom, children from upper/middle-class families spent more time on educational activities (5.8 h per day) than those from working-class families (4.5 h per day) 7 , 69 .

Unequal dispositions for autonomy and self-regulation

School closures have encouraged autonomous work among students. This ‘independent’ way of studying is compatible with the family socialization of upper/middle-class students, but does not match the interdependent norms more commonly associated with working-class contexts 9 . Upper/middle-class contexts tend to promote cultural norms of independence whereby individuals perceive themselves as autonomous actors, independent of other individuals and of the social context, able to pursue their own goals 70 . For example, upper/middle-class parents tend to invite children to express their interests, preferences and opinions during the various activities of everyday life 54 , 55 . Conversely, in working-class contexts characterized by low economic resources and where life is more uncertain, individuals tend to perceive themselves as interdependent, connected to others and members of social groups 53 , 70 , 71 . This interdependent self-construal fits less well with the independent culture of academic contexts. This cultural mismatch between interdependent self-construal common in working-class students and the independent norms of the educational institution has negative consequences for academic performance 9 .

Once again, the impact of these differences is likely to be amplified during school closures, when being able to work alone and autonomously is especially useful. The requirement to work alone is more likely to match the independent self-construal of upper/middle-class students than the interdependent self-construal of working-class students. In the case of working-class students, this mismatch is likely to increase their difficulties in working alone at home. Supporting our argument, recent research has shown that working-class students tend to underachieve in contexts where students work individually compared with contexts where students work with others 72 . Similarly, during school closures, high self-regulation skills (for example, setting goals, selecting appropriate learning strategies and maintaining motivation 73 ) are required to maintain study activities and are likely to be especially useful for using digital resources efficiently. Research has shown that students from working-class backgrounds typically develop their self-regulation skills to a lesser extent than those from upper/middle-class backgrounds 74 , 75 , 76 .

Interestingly, some authors have suggested that independent (versus interdependent) self-construal may also affect communication with teachers 77 . Indeed, in the context of distance learning, working-class families are less likely to respond to the communication of teachers because their ‘interdependent’ self leads them to respect hierarchies, and thus perceive teachers as an expert who ‘can be trusted to make the right decisions for learning’. Upper/middle class families, relying on ‘independent’ self-construal, are more inclined to seek individualized feedback, and therefore tend to participate to a greater extent in exchanges with teachers. Such cultural differences are important because they can also contribute to the difficulties encountered by working-class families.

The structural divide: unequal support from schools

The issues reviewed thus far all increase the vulnerability of children and students from underprivileged backgrounds when schools are closed. To offset these disadvantages, it might be expected that the school should increase its support by providing additional resources for working-class students. However, recent data suggest that differences in the material and human resources invested in providing educational support for children during periods of school closure were—paradoxically—in favour of upper/middle-class students (Fig. 1 ). In England, for example, upper/middle-class parents reported benefiting from online classes and video-conferencing with teachers more often than working-class parents 10 . Furthermore, active help from school (for example, online teaching, private tutoring or chats with teachers) occurred more frequently in the richest households (64% of the richest households declared having received help from school) than in the poorest households (47%). Another survey found that in the United Kingdom, upper/middle-class children were more likely to take online lessons every day (30%) than working-class students (16%) 12 . This substantial difference might be due, at least in part, to the fact that private schools are better equipped in terms of online platforms (60% of schools have at least one online platform) than state schools (37%, and 23% in the most deprived schools) and were more likely to organize daily online lessons. Similarly, in the United Kingdom, in schools with a high proportion of students eligible for free school meals, teachers were less inclined to broadcast an online lesson for their pupils 78 . Interestingly, 58% of teachers in the wealthiest areas reported having messaged their students or their students’ parents during lockdown compared with 47% in the most deprived schools. In addition, the probability of children receiving technical support from the school (for example, by providing pupils with laptops or other devices) is, surprisingly, higher in the most advantaged schools than in the most deprived 78 .

In addition to social class disparities, there has been less support from schools for African-American and Latinx students. During school closures in the United States, 40% of African-American students and 30% of Latinx students received no online teaching compared with 10% of white students 79 . Another source of inequality is that the probability of school closure was correlated with social class and race. In the United States, for example, school closures from September to December 2020 were more common in schools with a high proportion of racial/ethnic minority students, who experience homelessness and are eligible for free/discounted school meals 80 .

Similarly, access to educational resources and support was lower in poorer (compared with richer) countries 81 . In sub-Saharan Africa, during lockdown, 45% of children had no exposure at all to any type of remote learning. Of those who did, the medium was mostly radio, television or paper rather than digital. In African countries, at most 10% of children received some material through the Internet. In Latin America, 90% of children received some remote learning, but less than half of that was through the internet—the remainder being via radio and television 81 . In Ecuador, high-school students from the lowest wealth quartile had fewer remote-learning opportunities, such as Google class/Zoom, than students from the highest wealth quartile 31 .

Thus, the achievement gap and its accentuation during lockdown are due not only to the cultural and digital disadvantages of working-class families but also to unequal support from schools. This inequality in school support is not due to teachers being indifferent to or even supportive of social stratification. Rather, we believe that these effects are fundamentally structural. In many countries, schools located in upper/middle-class neighbourhoods have more money than those in the poorest neighbourhoods. Moreover, upper/middle-class parents invest more in the schools of their children than working-class parents (for example, see ref. 82 ), and schools have an interest in catering more for upper/middle-class families than for working-class families 83 . Additionally, the expectation of teachers may be lower for working-class children 84 . For example, they tend to estimate that working-class students invest less effort in learning than their upper/middle-class counterparts 85 . These differences in perception may have influenced the behaviour of teachers during school closure, such that teachers in privileged neighbourhoods provided more information to students because they expected more from them in term of effort and achievement. The fact that upper/middle-class parents are better able than working-class parents to comply with the expectations of teachers (for examples, see refs. 55 , 86 ) may have reinforced this phenomenon. These discrepancies echo data showing that working-class students tend to request less help in their schoolwork than upper/middle-class ones 87 , and they may even avoid asking for help because they believe that such requests could lead to reprimands 88 . During school closures, these students (and their families) may in consequence have been less likely to ask for help and resources. Jointly, these phenomena have resulted in upper/middle-class families receiving more support from schools during lockdown than their working-class counterparts.

Psychological effects of digital, cultural and structural divides

Despite being strongly influenced by social class, differences in academic achievement are often interpreted by parents, teachers and students as reflecting differences in ability 89 . As a result, upper/middle-class students are usually perceived—and perceive themselves—as smarter than working-class students, who are perceived—and perceive themselves—as less intelligent 90 , 91 , 92 or less able to succeed 93 . Working-class students also worry more about the fact that they might perform more poorly than upper/middle-class students 94 , 95 . These fears influence academic learning in important ways. In particular, they can consume cognitive resources when children and students work on academic tasks 96 , 97 . Self-efficacy also plays a key role in engaging in learning and perseverance in the face of difficulties 13 , 98 . In addition, working-class students are those for whom the fear of being outperformed by others is the most negatively related to academic performance 99 .

The fact that working-class children and students are less familiar with the tasks set by teachers, and less well equipped and supported, makes them more likely to experience feelings of incompetence (Fig. 1 ). Working-class parents are also more likely than their upper/middle-class counterparts to feel unable to help their children with schoolwork. Consistent with this, research has shown that both working-class students and parents have lower feelings of academic self-efficacy than their upper/middle-class counterparts 100 , 101 . These differences have been documented under ‘normal’ conditions but are likely to be exacerbated during distance learning. Recent surveys conducted during the school closures have confirmed that upper/middle-class families felt better able to support their children in distance learning than did working-class families 10 and that upper/middle-class parents helped their children more and felt more capable to do so 11 , 12 .

Pandemic disparity, future directions and recommendations

The research reviewed thus far suggests that children and their families are highly unequal with respect to digital access, skills and use. It also shows that upper/middle-class students are more likely to be supported in their homework (by their parents and teachers) than working-class students, and that upper/middle-class students and parents will probably feel better able than working-class ones to adapt to the context of distance learning. For all these reasons, we anticipate that as a result of school closures, the COVID-19 pandemic will substantially increase the social class achievement gap. Because school closures are a recent occurrence, it is too early to measure with precision their effects on the widening of the achievement gap. However, some recent data are consistent with this idea.

Evidence for a widening gap during the pandemic

Comparing academic achievement in 2020 with previous years provides an early indication of the effects of school closures during the pandemic. In France, for example, first and second graders take national evaluations at the beginning of the school year. Initial comparisons of the results for 2020 with those from previous years revealed that the gap between schools classified as ‘priority schools’ (those in low-income urban areas) and schools in higher-income neighbourhoods—a gap observed every year—was particularly pronounced in 2020 in both French and mathematics 102 .

Similarly, in the Netherlands, national assessments take place twice a year. In 2020, they took place both before and after school closures. A recent analysis compared progress during this period in 2020 in mathematics/arithmetic, spelling and reading comprehension for 7–11-year-old students within the same period in the three previous years 103 . Results indicated a general learning loss in 2020. More importantly, for the 8% of working-class children, the losses were 40% greater than they were for upper/middle-class children.

Similar results were observed in Belgium among students attending the final year of primary school. Compared with students from previous cohorts, students affected by school closures experienced a substantial decrease in their mathematics and language scores, with children from more disadvantaged backgrounds experiencing greater learning losses 104 . Likewise, oral reading assessments in more than 100 school districts in the United States showed that the development of this skill among children in second and third grade significantly slowed between Spring and Autumn 2020, but this slowdown was more pronounced in schools from lower-achieving districts 105 .

It is likely that school closures have also amplified racial disparities in learning and achievement. For example, in the United States, after the first lockdown, students of colour lost the equivalent of 3–5 months of learning, whereas white students were about 1–3 months behind. Moreover, in the Autumn, when some students started to return to classrooms, African-American and Latinx students were more likely to continue distance learning, despite being less likely to have access to the digital tools, Internet access and live contact with teachers 106 .

In some African countries (for example, Ethiopia, Kenya, Liberia, Tanzania and Uganda), the COVID-19 crisis has resulted in learning loss ranging from 6 months to more 1 year 107 , and this learning loss appears to be greater for working-class children (that is, those attending no-fee schools) than for upper/middle-class children 108 .

These findings show that school closures have exacerbated achievement gaps linked to social class and ethnicity. However, more research is needed to address the question of whether school closures differentially affect the learning of students from working- and upper/middle-class families.

Future directions

First, to assess the specific and unique impact of school closures on student learning, longitudinal research should compare student achievement at different times of the year, before, during and after school closures, as has been done to document the summer learning loss 66 , 109 . In the coming months, alternating periods of school closure and opening may occur, thereby presenting opportunities to do such research. This would also make it possible to examine whether the gap diminishes a few weeks after children return to in-school learning or whether, conversely, it increases with time because the foundations have not been sufficiently acquired to facilitate further learning 110 .

Second, the mechanisms underlying the increase in social class disparities during school closures should be examined. As discussed above, school closures result in situations for which students are unevenly prepared and supported. It would be appropriate to seek to quantify the contribution of each of the factors that might be responsible for accentuating the social class achievement gap. In particular, distinguishing between factors that are relatively ‘controllable’ (for example, resources made available to pupils) and those that are more difficult to control (for example, the self-efficacy of parents in supporting the schoolwork of their children) is essential to inform public policy and teaching practices.

Third, existing studies are based on general comparisons and very few provide insights into the actual practices that took place in families during school closure and how these practices affected the achievement gap. For example, research has documented that parents from working-class backgrounds are likely to find it more difficult to help their children to complete homework and to provide constructive feedback 63 , 111 , something that could in turn have a negative impact on the continuity of learning of their children. In addition, it seems reasonable to assume that during lockdown, parents from upper/middle-class backgrounds encouraged their children to engage in practices that, even if not explicitly requested by teachers, would be beneficial to learning (for example, creative activities or reading). Identifying the practices that best predict the maintenance or decline of educational achievement during school closures would help identify levers for intervention.

Finally, it would be interesting to investigate teaching practices during school closures. The lockdown in the spring of 2020 was sudden and unexpected. Within a few days, teachers had to find a way to compensate for the school closure, which led to highly variable practices. Some teachers posted schoolwork on platforms, others sent it by email, some set work on a weekly basis while others set it day by day. Some teachers also set up live sessions in large or small groups, providing remote meetings for questions and support. There have also been variations in the type of feedback given to students, notably through the monitoring and correcting of work. Future studies should examine in more detail what practices schools and teachers used to compensate for the school closures and their effects on widening, maintaining or even reducing the gap, as has been done for certain specific literacy programmes 112 as well as specific instruction topics (for example, ecology and evolution 113 ).

Practical recommendations

We are aware of the debate about whether social science research on COVID-19 is suitable for making policy decisions 114 , and we draw attention to the fact that some of our recommendations (Table 1 ) are based on evidence from experiments or interventions carried out pre-COVID while others are more speculative. In any case, we emphasize that these suggestions should be viewed with caution and be tested in future research. Some of our recommendations could be implemented in the event of new school closures, others only when schools re-open. We also acknowledge that while these recommendations are intended for parents and teachers, their implementation largely depends on the adoption of structural policies. Importantly, given all the issues discussed above, we emphasize the importance of prioritizing, wherever possible, in-person learning over remote learning 115 and where this is not possible, of implementing strong policies to support distance learning, especially for disadvantaged families.

Where face-to face teaching is not possible and teachers are responsible for implementing distance learning, it will be important to make them aware of the factors that can exacerbate inequalities during lockdown and to provide them with guidance about practices that would reduce these inequalities. Thus, there is an urgent need for interventions aimed at making teachers aware of the impact of the social class of children and families on the following factors: (1) access to, familiarity with and use of digital devices; (2) familiarity with academic knowledge and skills; and (3) preparedness to work autonomously. Increasing awareness of the material, cultural and psychological barriers that working-class children and families face during lockdown should increase the quality and quantity of the support provided by teachers and thereby positively affect the achievements of working-class students.

In addition to increasing the awareness of teachers of these barriers, teachers should be encouraged to adjust the way they communicate with working-class families due to differences in self-construal compared with upper/middle-class families 77 . For example, questions about family (rather than personal) well-being would be congruent with interdependent self-construals. This should contribute to better communication and help keep a better track of the progress of students during distance learning.

It is also necessary to help teachers to engage in practices that have a chance of reducing inequalities 53 , 116 . Particularly important is that teachers and schools ensure that homework can be done by all children, for example, by setting up organizations that would help children whose parents are not in a position to monitor or assist with the homework of their children. Options include homework help groups and tutoring by teachers after class. When schools are open, the growing tendency to set homework through digital media should be resisted as far as possible given the evidence we have reviewed above. Moreover, previous research has underscored the importance of homework feedback provided by teachers, which is positively related to the amount of homework completed and predictive of academic performance 117 . Where homework is web-based, it has also been shown that feedback on web-based homework enhances the learning of students 118 . It therefore seems reasonable to predict that the social class achievement gap will increase more slowly (or even remain constant or be reversed) in schools that establish individualized monitoring of students, by means of regular calls and feedback on homework, compared with schools where the support provided to pupils is more generic.

Given that learning during lockdown has increasingly taken place in family settings, we believe that interventions involving the family are also likely to be effective 119 , 120 , 121 . Simply providing families with suitable material equipment may be insufficient. Families should be given training in the efficient use of digital technology and pedagogical support. This would increase the self-efficacy of parents and students, with positive consequences for achievement. Ideally, such training would be delivered in person to avoid problems arising from the digital divide. Where this is not possible, individualized online tutoring should be provided. For example, studies conducted during the lockdown in Botswana and Italy have shown that individual online tutoring directly targeting either parents or students in middle school has a positive impact on the achievement of students, particularly for working-class students 122 , 123 .

Interventions targeting families should also address the psychological barriers faced by working-class families and children. Some interventions have already been designed and been shown to be effective in reducing the social class achievement gap, particularly in mathematics and language 124 , 125 , 126 . For example, research showed that an intervention designed to train low-income parents in how to support the mathematical development of their pre-kindergarten children (including classes and access to a library of kits to use at home) increased the quality of support provided by the parents, with a corresponding impact on the development of mathematical knowledge of their children. Such interventions should be particularly beneficial in the context of school closure.

Beyond its impact on academic performance and inequalities, the COVID-19 crisis has shaken the economies of countries around the world, casting millions of families around the world into poverty 127 , 128 , 129 . As noted earlier, there has been a marked increase in economic inequalities, bringing with it all the psychological and social problems that such inequalities create 130 , 131 , especially for people who live in scarcity 132 . The increase in educational inequalities is just one facet of the many difficulties that working-class families will encounter in the coming years, but it is one that could seriously limit the chances of their children escaping from poverty by reducing their opportunities for upward mobility. In this context, it should be a priority to concentrate resources on the most deprived students. A large proportion of the poorest households do not own a computer and do not have personal access to the Internet, which has important consequences for distance learning. During school closures, it is therefore imperative to provide such families with adequate equipment and Internet service, as was done in some countries in spring 2020. Even if the provision of such equipment is not in itself sufficient, it is a necessary condition for ensuring pedagogical continuity during lockdown.

Finally, after prolonged periods of school closure, many students may not have acquired the skills needed to pursue their education. A possible consequence would be an increase in the number of students for whom teachers recommend class repetitions. Class repetitions are contentious. On the one hand, class repetition more frequently affects working-class children and is not efficient in terms of learning improvement 133 . On the other hand, accepting lower standards of academic achievement or even suspending the practice of repeating a class could lead to pupils pursuing their education without mastering the key abilities needed at higher grades. This could create difficulties in subsequent years and, in this sense, be counterproductive. We therefore believe that the most appropriate way to limit the damage of the pandemic would be to help children catch up rather than allowing them to continue without mastering the necessary skills. As is being done in some countries, systematic remedial courses (for example, summer learning programmes) should be organized and financially supported following periods of school closure, with priority given to pupils from working-class families. Such interventions have genuine potential in that research has shown that participation in remedial summer programmes is effective in reducing learning loss during the summer break 134 , 135 , 136 . For example, in one study 137 , 438 students from high-poverty schools were offered a multiyear summer school programme that included various pedagogical and enrichment activities (for example, science investigation and music) and were compared with a ‘no-treatment’ control group. Students who participated in the summer programme progressed more than students in the control group. A meta-analysis 138 of 41 summer learning programmes (that is, classroom- and home-based summer interventions) involving children from kindergarten to grade 8 showed that these programmes had significantly larger benefits for children from working-class families. Although such measures are costly, the cost is small compared to the price of failing to fulfil the academic potential of many students simply because they were not born into upper/middle-class families.

The unprecedented nature of the current pandemic means that we lack strong data on what the school closure period is likely to produce in terms of learning deficits and the reproduction of social inequalities. However, the research discussed in this article suggests that there are good reasons to predict that this period of school closures will accelerate the reproduction of social inequalities in educational achievement.

By making school learning less dependent on teachers and more dependent on families and digital tools and resources, school closures are likely to greatly amplify social class inequalities. At a time when many countries are experiencing second, third or fourth waves of the pandemic, resulting in fresh periods of local or general lockdowns, systematic efforts to test these predictions are urgently needed along with steps to reduce the impact of school closures on the social class achievement gap.

Bambra, C., Riordan, R., Ford, J. & Matthews, F. The COVID-19 pandemic and health inequalities. J. Epidemiol. Commun. Health 74 , 964–968 (2020).

Google Scholar

Johnson, P, Joyce, R & Platt, L. The IFS Deaton Review of Inequalities: A New Year’s Message (Institute for Fiscal Studies, 2021).

Education: from disruption to recovery. https://en.unesco.org/covid19/educationresponse (UNESCO, 2020).

Daszak, P. We are entering an era of pandemics—it will end only when we protect the rainforest. The Guardian (28 July 2020); https://www.theguardian.com/commentisfree/2020/jul/28/pandemic-era-rainforest-deforestation-exploitation-wildlife-disease

Dobson, A. P. et al. Ecology and economics for pandemic prevention. Science 369 , 379–381 (2020).

Article CAS PubMed Google Scholar

Harris, C., Straker, L. & Pollock, C. A socioeconomic related ‘digital divide’ exists in how, not if, young people use computers. PLoS ONE 12 , e0175011 (2017).

Article PubMed PubMed Central Google Scholar

Zhang, M. Internet use that reproduces educational inequalities: evidence from big data. Comput. Educ. 86 , 212–223 (2015).

Article Google Scholar

Bourdieu, P. & Passeron, J. C. Reproduction in Education, Society and Culture (Sage, 1990).

Stephens, N. M., Fryberg, S. A., Markus, H. R., Johnson, C. S. & Covarrubias, R. Unseen disadvantage: how American universities’ focus on independence undermines the academic performance of first-generation college students. J. Pers. Soc. Psychol. 102 , 1178–1197 (2012).

Article PubMed Google Scholar

Andrew, A. et al. Inequalities in children’s experiences of home learning during the COVID-19 lockdown in England. Fisc. Stud. 41 , 653–683 (2020).

Bol, T. Inequality in homeschooling during the Corona crisis in the Netherlands. First results from the LISS Panel. Preprint at SocArXiv https://doi.org/10.31235/osf.io/hf32q (2020).

Cullinane, C. & Montacute, R. COVID-19 and Social Mobility. Impact Brief #1: School Shutdown (The Sutton Trust, 2020).

Bandura, A. Self-efficacy: toward a unifying theory of behavioral change. Psychol. Rev. 84 , 191–215 (1977).

Prior, D. D., Mazanov, J., Meacheam, D., Heaslip, G. & Hanson, J. Attitude, digital literacy and self efficacy: low-on effects for online learning behavior. Internet High. Educ. 29 , 91–97 (2016).

Robinson, L. et al. Digital inequalities 2.0: legacy inequalities in the information age. First Monday https://doi.org/10.5210/fm.v25i7.10842 (2020).

Cruz-Jesus, F., Vicente, M. R., Bacao, F. & Oliveira, T. The education-related digital divide: an analysis for the EU-28. Comput. Hum. Behav. 56 , 72–82 (2016).

Rice, R. E. & Haythornthwaite, C. In The Handbook of New Media (eds Lievrouw, L. A. & Livingstone S. M.), 92–113 (Sage, 2006).

Yates, S., Kirby, J. & Lockley, E. Digital media use: differences and inequalities in relation to class and age. Sociol. Res. Online 20 , 71–91 (2015).

Legleye, S. & Rolland, A. Une personne sur six n’utilise pas Internet, plus d’un usager sur trois manques de compétences numériques de base [One in six people do not use the Internet, more than one in three users lack basic digital skills] (INSEE Première, 2019).

Green, F. Schoolwork in lockdown: new evidence on the epidemic of educational poverty (LLAKES Centre, 2020); https://www.llakes.ac.uk/wp-content/uploads/2021/03/RP-67-Francis-Green-Research-Paper-combined-file.pdf

Vogels, E. Digital divide persists even as americans with lower incomes make gains in tech adoption (Pew Research Center, 2021); https://www.pewresearch.org/fact-tank/2021/06/22/digital-divide-persists-even-as-americans-with-lower-incomes-make-gains-in-tech-adoption/

McBurnie, C., Adam, T. & Kaye, T. Is there learning continuity during the COVID-19 pandemic? A synthesis of the emerging evidence. J. Learn. Develop. http://dspace.col.org/handle/11599/3720 (2020).

Baillet, J., Croutte, P. & Prieur, V. Baromètre du numérique 2019 [Digital barometer 2019] (Sourcing Crédoc, 2019).

Giraud, F., Bertrand, J., Court, M. & Nicaise, S. In Enfances de Classes. De l’inégalité Parmi les Enfants (ed. Lahire, B.) 933–952 (Seuil, 2019).

Ahamed, S. & Siddiqui, Z. Disparity in access to quality education and the digital divide (Ideas for India, 2020); https://www.ideasforindia.in/topics/macroeconomics/disparity-in-access-to-quality-education-and-the-digital-divide.html

Soomro, K. A., Kale, U., Curtis, R., Akcaoglu, M. & Bernstein, M. Digital divide among higher education faculty. Int. J. Educ. Tech. High. Ed. 17 , 21 (2020).

Meng, Q. & Li, M. New economy and ICT development in China. Inf. Econ. Policy 14 , 275–295 (2002).

Chinn, M. D. & Fairlie, R. W. The determinants of the global digital divide: a cross-country analysis of computer and internet penetration. Oxf. Econ. Pap. 59 , 16–44 (2006).

Lembani, R., Gunter, A., Breines, M. & Dalu, M. T. B. The same course, different access: the digital divide between urban and rural distance education students in South Africa. J. Geogr. High. Educ. 44 , 70–84 (2020).

Asadullah, N., Bhattacharjee, A., Tasnim, M. & Mumtahena, F. COVID-19, schooling, and learning (BRAC Institute of Governance & Development, 2020); https://bigd.bracu.ac.bd/wp-content/uploads/2020/06/COVID-19-Schooling-and-Learning_June-25-2020.pdf

Asanov, I., Flores, F., McKenzie, D., Mensmann, M. & Schulte, M. Remote-learning, time-use, and mental health of Ecuadorian high-school students during the COVID-19 quarantine. World Dev. 138 , 105225 (2021).

Kihui, N. Kenya: 80% of students missing virtual learning amid school closures—study. AllAfrica (18 May 2020); https://allafrica.com/stories/202005180774.html

Debenedetti, L., Hirji, S., Chabi, M. O. & Swigart, T. Prioritizing evidence-based responses in Burkina Faso to mitigate the economic effects of COVID-19: lessons from RECOVR (Innovations for Poverty Action, 2020); https://www.poverty-action.org/blog/prioritizing-evidence-based-responses-burkina-faso-mitigate-economic-effects-covid-19-lessons

Bosumtwi-Sam, C. & Kabay, S. Using data and evidence to inform school reopening in Ghana (Innovations for Poverty Action, 2020); https://www.poverty-action.org/blog/using-data-and-evidence-inform-school-reopening-ghana

Azubuike, O. B., Adegboye, O. & Quadri, H. Who gets to learn in a pandemic? Exploring the digital divide in remote learning during the COVID-19 pandemic in Nigeria. Int. J. Educ. Res. Open 2 , 100022 (2021).

Attewell, P. Comment: the first and second digital divides. Sociol. Educ. 74 , 252–259 (2001).

DiMaggio, P., Hargittai, E., Neuman, W. R. & Robinson, J. P. Social implications of the Internet. Annu. Rev. Sociol. 27 , 307–336 (2001).

Hargittai, E. Digital na(t)ives? Variation in Internet skills and uses among members of the ‘Net Generation’. Sociol. Inq. 80 , 92–113 (2010).

Iivari, N., Sharma, S. & Ventä-Olkkonen, L. Digital transformation of everyday life—how COVID-19 pandemic transformed the basic education of the young generation and why information management research should care? Int. J. Inform. Manag. 55 , 102183 (2020).

Wei, L. & Hindman, D. B. Does the digital divide matter more? Comparing the effects of new media and old media use on the education-based knowledge gap. Mass Commun. Soc. 14 , 216–235 (2011).

Octobre, S. & Berthomier, N. L’enfance des loisirs [The childhood of leisure]. Cult. Études 6 , 1–12 (2011).

Education at a glance 2015: OECD indicators (OECD, 2015); https://doi.org/10.1787/eag-2015-en

North, S., Snyder, I. & Bulfin, S. Digital tastes: social class and young people’s technology use. Inform. Commun. Soc. 11 , 895–911 (2008).

Robinson, L. & Schulz, J. Net time negotiations within the family. Inform. Commun. Soc. 16 , 542–560 (2013).

Bonfadelli, H. The Internet and knowledge gaps: a theoretical and empirical investigation. Eur. J. Commun. 17 , 65–84 (2002).

Drabowicz, T. Social theory of Internet use: corroboration or rejection among the digital natives? Correspondence analysis of adolescents in two societies. Comput. Educ. 105 , 57–67 (2017).

Nikken, P. & Jansz, J. Developing scales to measure parental mediation of young children’s Internet use. Learn. Media Technol. 39 , 250–266 (2014).

Danic, I., Fontar, B., Grimault-Leprince, A., Le Mentec, M. & David, O. Les espaces de construction des inégalités éducatives [The areas of construction of educational inequalities] (Presses Univ. de Rennes, 2019).

Goudeau, S. Comment l'école reproduit-elle les inégalités? [How does school reproduce inequalities?] (Univ. Grenoble Alpes Editions/Presses Univ. de Grenoble, 2020).

Bernstein, B. Class, Codes, and Control (Routledge, 1975).

Gaddis, S. M. The influence of habitus in the relationship between cultural capital and academic achievement. Soc. Sci. Res. 42 , 1–13 (2013).

Lamont, M. & Lareau, A. Cultural capital: allusions, gaps and glissandos in recent theoretical developments. Sociol. Theory 6 , 153–168 (1988).

Stephens, N. M., Markus, H. R. & Phillips, L. T. Social class culture cycles: how three gateway contexts shape selves and fuel inequality. Annu. Rev. Psychol. 65 , 611–634 (2014).

Lahire, B. Enfances de classe. De l’inégalité parmi les enfants [Social class childhood. Inequality among children] (Le Seuil, 2019).

Lareau, A. Unequal Childhoods: Class, Race, and Family Life (Univ. of California Press, 2003).

Bourdieu, P. La distinction. Critique sociale du jugement [Distinction: a social critique of the judgement of taste] (Éditions de Minuit, 1979).

Bradley, R. H., Corwyn, R. F., McAdoo, H. P. & Garcia Coll, C. The home environments of children in the United States part I: variations by age, ethnicity, and poverty status. Child Dev. 72 , 1844–1867 (2001).

Blevins‐Knabe, B. & Musun‐Miller, L. Number use at home by children and their parents and its relationship to early mathematical performance. Early Dev. Parent. 5 , 35–45 (1996).

LeFevre, J. A. et al. Pathays to mathematics: longitudinal predictors of performance. Child Dev. 81 , 1753–1767 (2010).

Lareau, A. Home Advantage. Social Class and Parental Intervention in Elementary Education (Falmer Press, 1989).

Guryan, J., Hurst, E. & Kearney, M. Parental education and parental time with children. J. Econ. Perspect. 22 , 23–46 (2008).

Hill, C. R. & Stafford, F. P. Allocation of time to preschool children and educational opportunity. J. Hum. Resour. 9 , 323–341 (1974).

Calarco, J. M. A Field Guide to Grad School: Uncovering the Hidden Curriculum (Princeton Univ. Press, 2020).

Daw, J. Parental income and the fruits of labor: variability in homework efficacy in secondary school. Res. Soc. Strat. Mobil. 30 , 246–264 (2012).

Rønning, M. Who benefits from homework assignments? Econ. Educ. Rev. 30 , 55–64 (2011).

Alexander, K. L., Entwisle, D. R. & Olson, L. S. Lasting consequences of the summer learning gap. Am. Sociol. Rev. 72 , 167–180 (2007).

Cooper, H., Nye, B., Charlton, K., Lindsay, J. & Greathouse, S. The effects of summer vacation on achievement test scores: a narrative and meta-analytic review. Rev. Educ. Res. 66 , 227–268 (1996).

Stewart, H., Watson, N. & Campbell, M. The cost of school holidays for children from low income families. Childhood 25 , 516–529 (2018).

Pensiero, N., Kelly, A. & Bokhove, C. Learning inequalities during the Covid-19 pandemic: how families cope with home-schooling (University of Southampton, 2020); https://doi.org/10.5258/SOTON/P0025

Stephens, N. M., Markus, H. R. & Townsend, S. S. Choice as an act of meaning: the case of social class. J. Pers. Soc. Psychol. 93 , 814–830 (2007).

Kraus, M. W., Piff, P. K. & Keltner, D. Social class, sense of control, and social explanation. J. Pers. Soc. Psychol. 97 , 992–1004 (2009).

Dittmann, A. G., Stephens, N. M. & Townsend, S. S. Achievement is not class-neutral: working together benefits pople from working-class contexts. J. Pers. Soc. Psychol. 119 , 517–539 (2020).

Zimmerman, B. J. Investigating self-regulation and motivation: historical background, methodological developments, and future prospects. Am. Educ. Res. J. 45 , 166–183 (2008).

Backer-Grøndahl, A., Nærde, A., Ulleberg, P. & Janson, H. Measuring effortful control using the children’s behavior questionnaire–very short form: modeling matters. J. Pers. Assess. 98 , 100–109 (2016).

Johnson, S. E., Richeson, J. A. & Finkel, E. J. Middle class and marginal? Socioeconomic status, stigma, and self-regulation at an elite university. J. Pers. Soc. Psychol. 100 , 838–852 (2011).

Størksen, I., Ellingsen, I. T., Wanless, S. B. & McClelland, M. M. The influence of parental socioeconomic background and gender on self-regulation among 5-year-old children in Norway. Early Educ. Dev. 26 , 663–684 (2015).

Brady, L. et al. 7 ways for teachers to truly connect with parents. Education Week (31 December 2020); https://www.edweek.org/leadership/opinion-7-ways-for-teachers-to-truly-connect-with-parents/2020/12

Montacute, R. Social mobility and Covid-19: implications of the Covid-19 crisis for educational inequality (Sutton Trust, 2020); https://dera.ioe.ac.uk/35323/2/COVID-19-and-Social-Mobility-1.pdf

Dorn, E., Hancock, B., Sarakatsannis, J. & Viruleg, E. COVID-19 and student learning in the United States: the hurt could last a lifetime (McKinsey & Company, 2020); https://www.mckinsey.com/industries/public-and-social-sector/our-insights/covid-19-and-student-learning-in-the-united-states-the-hurt-could-last-a-lifetime

Parolin, Z. & Lee, E. K. Large socio-economic, geographic and demographic disparities exist in exposure to school closures. Nat. Hum. Behav. 5 , 522–528 (2021).

Saavedra, J. A silent and unequal education crisis. And the seeds for its solution (World Bank, 2021); https://blogs.worldbank.org/education/silent-and-unequal-education-crisis-and-seeds-its-solution

Murray, B., Domina, T., Renzulli, L. & Boylan, R. Civil society goes to school: parent–teacher associations and the equality of educational opportunity. Russell Sage Found. J. Soc. Sci. 5 , 41–63 (2019).

Calarco, J. M. Avoiding us versus them: how schools’ dependence on privileged ‘helicopter’ parents influences enforcement of rules. Am. Sociol. Rev. 85 , 223–246 (2020).

Rist, R. Student social class and teacher expectations: the self-fulfilling prophecy in ghetto education. Harv. Educ. Rev. 40 , 411–451 (1970).

Tobisch, A. & Dresel, M. Negatively or positively biased? Dependencies of teachers’ judgments and expectations based on students’ ethnic and social backgrounds. Soc. Psychol. Educ. 20 , 731–752 (2017).

Brantlinger, E. Dividing Classes: How the Middle-class Negotiates and Rationalizes School Advantage (Routledge, 2003).

Calarco, J. M. ‘I need help!’ Social class and children’s help-seeking in elementary school. Am. Sociol. Rev. 76 , 862–882 (2011).

Calarco, J. M. The inconsistent curriculum: cultural tool kits and student interpretations of ambiguous expectations. Soc. Psychol. Quart. 77 , 185–209 (2014).

Goudeau, S. & Cimpian, A. How do young children explain differences in the classroom? Implications for achievement, motivation, and educational equity. Perspect. Psychol. Sci. 16 , 533–552 (2021).

Croizet, J. C., Goudeau, S., Marot, M. & Millet, M. How do educational contexts contribute to the social class achievement gap: documenting symbolic violence from a social psychological point of view. Curr. Opin. Psychol. 18 , 105–110 (2017).

Goudeau, S. & Croizet, J.-C. Hidden advantages and disadvantages of social class: how classroom settings reproduce social inequality by staging unfair comparison. Psychol. Sci. 28 , 162–170 (2017).

Kudrna, L., Furnham, A. & Swami, V. The influence of social class salience on self-assessed intelligence. Soc. Behav. Personal. 38 , 859–864 (2010).

Wiederkehr, V., Darnon, C., Chazal, S., Guimond, S. & Martinot, D. From social class to self-efficacy: internalization of low social status pupils’ school performance. Soc. Psychol. Educ. 18 , 769–784 (2015).

Jury, M., Smeding, A., Court, M. & Darnon, C. When first-generation students succeed at university: on the link between social class, academic performance, and performance-avoidance goals. Contemp. Educ. Psychol. 41 , 25–36 (2015).

Jury, M., Quiamzade, A., Darnon, C. & Mugny, G. Higher and lower status individuals’ performance goals: the role of hierarchy stability. Motiv. Sci. 5 , 52–65 (2019).

Autin, F. & Croizet, J.-C. Improving working memory efficiency by reframing metacognitive interpretation of task difficulty. J. Exp. Psychol. Gen. 141 , 610–618 (2012).

Schmader, T., Johns, M. & Forbes, C. An integrated process model of stereotype threat effects on performance. Psychol. Rev. 115 , 336–356 (2008).

Usher, E. L. & Pajares, F. Self-efficacy for self-regulated learning: a validation study. Educ. Psychol. Meas. 68 , 443–463 (2008).

Bruno, A., Jury, M., Toczek-Capelle, M.-C. & Darnon, C. Are performance-avoidance goals always deleterious for academic achievement in college? The moderating role of social class. Soc. Psychol. Educ. 22 , 539–555 (2019).

Holloway, S. D. et al. Parenting self-efficacy and parental involvement: mediators or moderators between socioeconomic status and children’s academic competence in Japan and Korea? Res. Hum. Dev. 13 , 258–272 (2016).

Tazouti, Y. & Jarlégan, A. The mediating effects of parental self-efficacy and parental involvement on the link between family socioeconomic status and children’s academic achievement. J. Fam. Stud. 25 , 250–266 (2019).

Andreu, S. et al. Évaluations 2020, repères CP, CE1: premiers résultats [2020 assessments, first and second grades benchmarks: first results] (Ministère de l’Éducation nationale, de la Jeunesse et des Sports, 2020); https://www.education.gouv.fr/evaluations-2020-reperes-cp-ce1-premiers-resultats-307122

Engzell, P., Frey, A. & Verhagen, M. D. Learning loss due to school closures during the COVID-19 pandemic. Proc. Natl Acad. Sci. USA 118 , e2022376118 (2021).

Article CAS PubMed PubMed Central Google Scholar

Maldonado, J. E. & De Witte, K. The effect of school closures on standardized student test outcomes (KU Leuven—Faculty of Economics and Business, 2020); https://limo.libis.be/primo-explore/fulldisplay?docid=LIRIAS3189074&context=L&vid=Lirias&search_scope=Lirias&tab=default_tab&lang=en_US

Domingue, B., Hough, H. J., Lang, D. & Yeatman, J. Changing patterns of growth in oral reading fluency during the COVID-19 pandemic (PACE, 2021); https://edpolicyinca.org/publications/changing-patterns-growth-oral-reading-fluency-during-covid-19-pandemic

Dorn, E., Hancock, B., Sarakatsannis, J. & Viruleg, E. COVID-19 and learning loss—disparities grow and students need help (McKinsey & Company, 2020); https://www.mckinsey.com/industries/public-and-social-sector/our-insights/covid-19-and-learning-loss-disparities-grow-and-students-need-help

Angrist, N. et al. Building back better to avert a learning catastrophe: estimating learning loss from COVID-19 school shutdowns in Africa and facilitating short-term and long-term learning recovery. Int. J. Educ. Dev. 84 , 102397 (2021).

Reddy, V., Soudien, C. & Winnaar, L. Disrupted learning during COVID-19: the impact of school closures on education outcomes in South Africa (The Conversation, 2020); https://theconversation.com/impact-of-school-closures-on-education-outcomes-in-south-africa-136889

Entwisle, D. R. & Alexander, K. L. Summer setback: race, poverty, school composition, and mathematics achievement in the first two years of school. Am. Sociol. Rev. 57 , 72–84 (1992).

Kieffer, M. J. Catching up or falling behind? Initial English proficiency, concentrated poverty, and the reading growth of language minority learners in the United States. J. Educ. Psychol. 100 , 851–868 (2008).

Calarco, J. M., Horn, I. & Chen, G. A. ‘You need to be more responsible’: how math homework operates as a status-reinforcing process in school. Preprint at SocArXiv https://doi.org/10.31235/osf.io/xf96q (2020).

Kaiper-Marquez, A. et al. On the fly: adapting quickly to emergency remote instruction in a family literacy program. Int. Rev. Educ. 66 , 1–23 (2020).

Barton, D. C. Impacts of the COVID‐19 pandemic on field instruction and remote teaching alternatives: results from a survey of instructors. Ecol. Evol. 10 , 12499–12507 (2020).

Article PubMed Central Google Scholar

IJzerman, H. et al. Use caution when applying behavioural science to policy. Nat. Hum. Behav. 4 , 1092–1094 (2020).

Taylor, J. & Mallery, J. In person and online learning go together (Stanford Institute for Economic Policy Research, 2020); https://siepr.stanford.edu/research/publications/person-and-online-learning-go-together

Dietrichson, J., Bøg, M., Filges, T. & Klint Jørgensen, A. M. Academic interventions for elementary and middle school students with low socioeconomic status: a systematic review and meta-analysis. Rev. Educ. Res. 87 , 243–282 (2017).

Núñez, J. C. et al. Teachers’ feedback on homework, homework-related behaviors, and academic achievement. J. Educ. Res. 108 , 204–216 (2015).

Singh, R. et al. In Artificial Intelligence in Education (eds Biswas, G.et al.) 328–336 (Springer Berlin Heidelberg, 2011).

Harackiewicz, J. M., Rozek, C. S., Hulleman, C. S. & Hyde, J. S. Helping parents to motivate adolescents in mathematics and science: an experimental test of a utility-value intervention. Psychol. Sci. 23 , 899–906 (2012).

Jeynes, W. A meta-analysis of the efficacy of different types of parental involvement programs for urban students. Urban Educ. 47 , 706–742 (2012).

Mol, S. E., Bus, A. G., De Jong, M. T. & Smeets, D. J. Added value of dialogic parent–child book readings: a meta-analysis. Early Educ. Dev. 19 , 7–26 (2008).

Angrist, N., Bergman, P. & Matsheng, M. School’s out: experimental evidence on limiting learning loss using “low-tech” in a pandemic (National Bureau of Economic Research, 2021); https://www.nber.org/papers/w28205

Carlana, M. & La Ferrara, E. Apart but connected: online tutoring and student outcomes during the COVID-19 pandemic (Institute of Labor Economics, 2021); http://hdl.handle.net/10419/232846

Pagan, S. & Sénéchal, M. Involving parents in a summer book reading program to promote reading comprehension, fluency, and vocabulary in grade 3 and grade 5 children. Can. J. Educ. 37 , 1–31 (2014).

Sénéchal, M. & LeFevre, J. A. Parental involvement in the development of children’s reading skill: a five‐year longitudinal study. Child Dev. 73 , 445–460 (2002).

Starkey, P. & Klein, A. Fostering parental support for children’s mathematical development: an intervention with Head Start families. Early Educ. Dev. 11 , 659–680 (2000).

Buheji, M. et al. The extent of Covid-19 pandemic socio-economic impact on global poverty: a global integrative multidisciplinary review. Am. J. Econ. 10 , 213–224 (2020).

The world economy on a tightrope (OECD, 2020); http://www.oecd.org/economic-outlook/june-2020/

Martin, A., Markhvida, M., Hallegatte, S. & Walsh, B. Socio-economic impacts of COVID-19 on household consumption and poverty. Econ. Disasters Clim. Change 4 , 453–479 (2020).

Jetten, J., Mols, F. & Selvanathan, H. P. How economic inequality fuels the rise and persistence of the Yellow Vest movement. Int. Rev. Soc. Psychol. 33 , 2 (2020).

Wilkinson, R. G. & Pickett, K. E. Income inequality and social dysfunction. Annu. Rev. Sociol. 35 , 493–511 (2009).

Sommet, N., Morselli, D. & Spini, D. Income inequality affects the psychological health of only the people facing scarcity. Psychol. Sci. 29 , 1911–1921 (2018).

Hattie, J. Visible Learning: A Synthesis of over 800 Meta-analyses Relating to Achievement (Routledge, 2008).

Cooper, H., Charlton, K., Valentine, J. C., Muhlenbruck, L. & Borman, G. D. Making the most of summer school: a meta-analytic and narrative review. Monogr. Soc. Res. Child 65 , 1–127 (2000).

Heyns, B. Schooling and cognitive development: is there a season for learning? Child Dev. 58 , 1151–1160 (1987).

McCombs, J. S., Augustine, C. H. & Schwartz, H. L. Making Summer Count: How Summer Programs can Boost Children’s Learning (Rand Education, 2011).

Borman, G. D. & Dowling, N. M. Longitudinal achievement effects of multiyear summer school: evidence from the teach Baltimore randomized field trial. Educ. Eval. Policy 28 , 25–48 (2006).

Kim, J. S. & Quinn, D. M. The effects of summer reading on low-income children’s literacy achievement from kindergarten to grade 8: a meta-analysis of classroom and home interventions. Rev. Educ. Res. 83 , 386–431 (2013).

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Acknowledgements

We thank G. Reis for editing the figure. The writing of this manuscript was supported by grant ANR-19-CE28-0007–PRESCHOOL from the French National Research Agency (S.G.).

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Goudeau, S., Sanrey, C., Stanczak, A. et al. Why lockdown and distance learning during the COVID-19 pandemic are likely to increase the social class achievement gap. Nat Hum Behav 5 , 1273–1281 (2021). https://doi.org/10.1038/s41562-021-01212-7

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ORIGINAL RESEARCH article

Impact of the covid-19 pandemic on online learning in higher education: a bibliometric analysis.

1 Faculty of Public Administration, University of Ljubljana, Ljubljana, Slovenia
2 Department of Primary Level Education, University of the Aegean, Rhodes, Greece

The outbreak of the COVID-19 pandemic significantly disrupted higher education by forcing the transition to online learning, which became a mandatory teaching process during the lockdowns. Although the epidemiological situation has gradually improved since then, online learning is becoming ever more popular as it provides new learning opportunities. Therefore, the paper aims to present recent research trends concerning online learning in higher education during the COVID-19 pandemic by using selected bibliometric approaches. The bibliometric analysis is based on 8,303 documents from the Scopus database published between January 2020 and March 2022, when repeated lockdowns meant most countries were experiencing constant disruptions to the educational process. The results show that the COVID-19 pandemic increased interest in online learning research, notably in English-speaking and Asian countries, with most research being published in open-access scientific journals. Moreover, the topics most frequently discussed in the online learning research during the COVID-19 pandemic were ICT and pedagogy, technology-enhanced education, mental health and well-being, student experience and curriculum and professional development. Finally, the COVID-19 pandemic encouraged explorations of emergency remote learning approaches like e-learning, distance learning and virtual learning, which are intended to limit physical contact between teachers and students, where the specific requirements of a given field of study often guide which online learning approach is the most suitable. The findings add to the existing body of scientific knowledge and support the evidence-based policymaking needed to ensure sustainable higher education in the future.

1. Introduction

The outbreak of the COVID-19 pandemic significantly disrupted higher education by forcing the transition to online learning, which became a mandatory teaching process during the lockdowns ( Aristovnik et al., 2020a ). Despite the educational process saw disruptions on all levels of education, i.e., primary, secondary and tertiary ( Tang, 2023 ), as well as in adult education ( James and Thériault, 2020 ), worker education ( Dedeilia et al., 2023 ) and lifelong education ( Waller et al., 2020 ), higher education students proved to be one of the worst affected groups because the social distancing measures, on top of their education, challenged their financial and housing situation ( Aristovnik et al., 2020a ). Challenges arising from the density of students in educational facilities (e.g., campuses, faculties, dormitories etc.) meant higher education institutions were forced to offer education relying on various information and communication technologies (ICTs) and tried to ensure education comparable in quality to traditional learning, noting that the quality of online learning delivery holds important implications for student satisfaction and student performance ( Keržič et al., 2021 ). Nevertheless, the lockdown periods were devastating for many students also in terms of their emotional functioning ( Raccanello et al., 2022 ). The COVID-19 pandemic eventually grew more predictable and manageable, allowing higher education institutions to gradually shift back to traditional learning approaches. Although the epidemiological situation has improved over time, online learning is becoming increasingly popular as it provides new learning opportunities, especially when combined with traditional learning.

The rapid, yet from the health protection point of view necessary ( Aristovnik et al., 2020b ), shift from traditional learning to online learning considerably affected teaching and learning. The transition to online learning was made without adequate consideration of whether the study materials and teaching methods were suitable for this mode of higher education delivery. This was an ad hoc shift in a situation of great uncertainty for both teachers and students. The transition to online learning has also brought to the surface gaps in higher education providers’ preparedness and their lack of ICT infrastructure, resulting in unequal access to quality education for all, particularly students from rural areas and regions with lower socio-economic development. It is important to note here that the rapid shift to an online learning environment in emergency circumstances should not be confused with properly planned online education equipped with appropriate infrastructure that enables and supports pedagogical work and study in an online environment ( Hodges et al., 2020 ; Fuchs, 2022 ; Misiejuk et al., 2023 ). Apart from the changes in teaching and learning, the social aspect of students’ lives has been affected as well. The most worrying consequence has been social isolation leading to a lack of crucial social interaction for students ( Elmer et al., 2020 ; Bonsaksen et al., 2021 ; Fried et al., 2021 ; Van der Graaf et al., 2021 ) and in some cases also in coronavirus-related post-traumatic stress syndrome (PTSD) ( Ochnik et al., 2021 ). According to Gavriluţă et al. (2022) , three dimensions affected students during the COVID-19 pandemic: educational, social, and emotional. The transition from traditional to online learning entailed a significant transformation in education, requiring changes in teaching practices and new learning approaches. Further, the social aspect of the COVID-19 pandemic and associated lockdowns is evident in the absence of relational, economic and professional problems (in)directly affecting the transition to adulthood. The new reality changed attitudes to various aspects of life and, in turn, also affected emotional responsiveness. Briefly, substantial changes to everyday student lives were made during the COVID-19 pandemic that may hold far-reaching effects of currently unknown scope in the near and distant future ( Campos et al., 2022 ; Gao et al., 2022 ; Keržič et al., 2022 ; Rasli et al., 2022 ).

Therefore, the educational community requires greater insights into different aspects of the COVID-19 pandemic’s impact on online learning, e.g., students, teachers, pedagogy, ICT technology, online learning approaches and implications for various fields of study. In the context of higher education, some bibliometric studies (e.g., Gurcan et al., 2022 ; Saqr et al., 2023 ) have already sought to address issues involving online learning during the pandemic. Yet, they relied on a limited and narrow bibliographic dataset of peer-reviewed literature or lacked a qualitative synthesis of the results beyond the metrics, thereby neglecting some general comprehensive outlines of the global research into the topic ( Saqr et al., 2023 ). Moreover, despite some bibliometric studies focusing on technical aspects (e.g., Navarro-Espinosa et al., 2021 ; Bozkurt, 2022 ; Tlili et al., 2022 ), the identification of the most effective ICT tools for specific online learning approaches remains unclear. Finally, there are also some bibliometric studies that attempt to determine the effectiveness of online learning in providing higher education ( Brika et al., 2021 ; Baber et al., 2022 ; Bilal et al., 2022 ; Bozkurt, 2022 ; Fauzi, 2022 ; Küçük-Avci et al., 2022 ; Yan et al., 2022 ), however, they often overlook the specific requirements of individual fields of study, thereby neglecting the crucial aspect of tailoring online learning provision to different disciplines.

The bibliometric study presented in the paper accordingly aims to fill the presented gaps in the literature. Specifically, it aims to present a global overview of the recent research trends in online learning in higher education using a comprehensive dataset of literature encompassing different varieties of online learning approaches that can facilitate online learning during the COVID-19 pandemic, provide some relevant qualitative synthesis of the results beyond the metrics and examine the relationships between ICT tools, online learning approaches and fields of study. Thus, the present bibliometric study, focusing on higher education, tries to answer the following three research questions:

• RQ1: What is the current state of the online learning research by conducting a descriptive overview and identifying top-cited documents?

• RQ2: What is the scientific production of online learning research across countries and sources?

• RQ3: Which are the main research hotspots and concepts in online learning research?

The remainder of the paper is structured as follows. The next section provides a literature review of recent bibliometric studies. The following section outlines the materials and methods applied in the study before the results of the present bibliometric analysis are described in the next section. At the end, the final section provides a discussion and conclusion while summarizing the main findings and implications.

2. Literature review

The outbreak of the COVID-19 pandemic led many governments to expand the use of online learning approaches as a solution to the global health challenge. Researchers thus showed rising interest in investigating the field of online learning, its dimensions, and its trends on all levels of education, particularly higher education. Such research relied heavily on bibliometric approaches to analyzing scientific research in the higher education context. Pham et al. (2022) concluded based on the 414 articles that although in the decades prior, there was an increase in the number of articles touching on the components of e-learning, such as the learning management system, this rise was accelerated during the pandemic in both developed and developing countries. This may be attributed to the attention of governmental policies that considered the topic of e-learning to be critical and worthy of priority. Similarly, Fauzi (2022) investigated 1,496 articles and concluded that the research focused on a few specific topics. The first is the delivery factor, which refers to selecting the appropriate learning practices. The second is the health and safety factor that relates to minimizing any risk that e-learning could bring to the mental and physical health of learners or teachers, such as stress, anxiety or even depression. The third topic refers to the field of study and the impact of e-learning. In areas like medical education, where clinical activities and labs have to be attended in person, some online learning approaches might be less appropriate than when used in other areas, such as social studies, where the requirements are less complex or different. Zhang et al. (2022) confirmed this finding after performing bibliometric research on 1,061 articles published between January 2020 and August 2021. They explained that theorists and researchers showed a growing interest in ways to respond to crises, such as the pandemic, and how to develop the best practices to ensure the quality and efficiency of e-learning. Examples of such practices might be inquiry-oriented learning and hands-on activities. This could derive from the already existing tendency of education researchers to respond to unprecedented global challenges or changes. The authors explain that this conclusion addresses interest in e-learning practices holistically.

In the same context, Yan et al. (2022) employed a bibliometric approach and identified that various digital tools are used in e-learning in the field of health studies. After investigating 132 studies, they concluded that selecting appropriate tools depends on many factors, including the field of a given course, the aims, and their effectiveness. They add that these findings can be significant for groups of people such as experts or trainee teachers. Okoro et al. (2022) researched 1,722 articles published between 2012 and 2021 and detected a surge in interest in the mental health of postgraduate students, as revealed by the research trends discussed in these articles. Still, they describe this surge as having been greater between 2020 and 2021, which may be attributed to the COVID-19 restrictions and their implications. Moreover, they believe that this research focus will likely continue soon.

After looking at 2,307 articles published between 2017 and 2021, Baber et al. (2022) detected an increasing trend in researching digital literacy. While this was underway before the pandemic, the latter caused a statistically significant further surge. Digital literacy is approached in the studied articles through parameters like instruction, teachers, learners, ICT and its applications, content knowledge, competencies, skills, perceptions, and higher education. It is also associated with acquiring the qualities required to deal with topics such as misinformation, fake news, technological content knowledge, health literacy, COVID-19, and distance education. The authors state that their study identified dynamics hidden in these research trends, which will likely continue in the next few years.

In higher education specifically, based on 602 articles, Brika et al. (2021) corroborated the growing trend of publishing articles on e-learning during the pandemic and outlined certain sub-topics of it, namely: motivation and students’ attitudes; blended and virtual learning comparison; types of online assessment; stress, anxiety and mental health; strategies to improve learners’ skills; quality; performance of the education delivered; challenges; and the potential of technology to lead to change and reform of higher education syllabi or curricula. The scope of those articles was to paint a bigger picture of how higher education communities and institutions use and treat online learning. This is expected to help with efficient decision-making in the future in order to have better results and functions in higher education and appropriate response to crises.

The bibliometric studies carried out during the pandemic identified a trend among researchers in higher education institutions to investigate more the technology factor and how the progress of the Internet, along with information and communication technologies generally, can further assist new modes of learning, such as online learning and distance learning. This might be attributed to a vision for a better means for new types of learning, as Küçük-Avci et al. (2022) claimed after carrying out a bibliometric analysis of 1,547 articles published between 2020 and 2021. The authors detected certain trends regarding distance learning in higher education. A main finding of their study, along with the increase in studies on distance education and e-learning in higher education, is that before the pandemic, the fact that these approaches were not so mandatory meant there was greater efficiency, probably due to the learners’ motivation. The authors further claim that researchers show a stronger interest in the technological means that can assist these types of learning. In addition, while researching 1,986 articles, Bozkurt (2022) established an increase in the implementation of blended learning by researchers who also aim to investigate the relationship between technological applications and learning institutions. Within these tendencies, researchers consider four thematic fields: a comparison of online and onsite learning with regard to effectiveness and efficiency; the experience, impressions and attitudes of stakeholders and learning community members with respect to blended learning; teacher training and curriculum development that will assure the appropriate and challenge-free implementation of blended learning; and the use of mostly a quantitative approach to research of blended learning.

Bilal et al. (2022) also examined research trends concerned with e-learning in higher education during the COVID-19 period by researching 1,595 studies published between 2020 and 2021. The four main trends they identified were supplementary to those mentioned by other authors: the first is about the challenges regarding online learning or blended learning along with the appropriate strategies in response; the second is student-centered collaborative learning and appropriate curriculum design; the third concerns home-based learning through a type of laboratory and the general conditions surrounding it; and the fourth addresses teachers’ background, training, professional competencies and interdisciplinary learning.

Tlili et al. (2022) focused on mapping COVID-19’s impact on Massive Open Online Courses (MOOCs). The overall finding from the 108 articles they considered is that there has been growing interest in these courses generally, and more specifically in research around their function and quality. This interest encompasses the main features of such courses, which provide easy accessibility and flexibility. However, they noted that this interest followed another trend among researchers in the context. In other words, the countries that published on MOOCs before the pandemic are the same countries that published during the period under study. Moreover, they stated that there is interest in the technical characteristics and requirements of such courses. Finally, the authors concluded that although most MOOCs were ICT courses, research has escalated into courses that refer to business, personal development or the humanities.

Several conclusions can be drawn from the above bibliometric studies. First, the series of bibliometric studies conducted during the pandemic demonstrates the rise of interest in online learning in higher education during the pandemic. Of course, there was a tendency toward e-learning before the pandemic, but between 2020 and 2022, this seems to have accelerated. The phenomenon is more intense in countries such as the USA, Canada, Australia, the UK, India and China. Concerning the area of study, the focus of researchers appears to be greater in fields such as Engineering, Sciences, and Health Sciences, albeit all fields seem to be investigated ( Djeki et al., 2022 ; Pham et al., 2022 ; Vaicondam et al., 2022 ; Zhang et al., 2022 ). Various studies have focused on determining the effectiveness of e-learning classes and courses or pointing out parameters that influence their effectiveness. These could be the appropriate conditions or subtopics like motivation, blended learning, learning tools, teacher training, cooperation between different institutions or efficient practices ( Brika et al., 2021 ; Baber et al., 2022 ; Bilal et al., 2022 ; Bozkurt, 2022 ; Fauzi, 2022 ; Küçük-Avci et al., 2022 ; Yan et al., 2022 ). A specific trend of authors is to examine virtual classes and laboratories ( Kartimi et al., 2022 ; Rojas-Sánchez et al., 2022 ; Zhang et al., 2022 ). Finally, there is a focus on the technology factor. Namely, researchers have concentrated on technical issues and conditions related to e-learning courses and their proper functioning ( Navarro-Espinosa et al., 2021 ; Bozkurt, 2022 ; Tlili et al., 2022 ).

3. Materials and methods

Comprehensive bibliometric data on online learning research during the COVID-19 pandemic were retrieved on 1 March 2022 from Scopus, a world-leading bibliographic database of peer-reviewed literature. The Scopus database was preferred because it has a broader coverage of scientific research than other databases such as Web of Science ( Falagas et al., 2008 ). This was confirmed by an initial search using the same search query in each database, revealing that Scopus provided more relevant documents than Web of Science. Moreover, compared to the Scopus database, the Web of Science has been found to be a database that significantly underrepresents the scientific disciplines of the Social Sciences and the Arts and Humanities ( Mongeon and Paul-Hus, 2016 ). Although English dominates in both Scopus and Web of Science, Scopus generally offers wider coverage of non-English documents, given that the titles, abstracts, and keywords are in English ( Vera-Baceta et al., 2019 ). According to the basic statistical theory, which can also be applied in the context of bibliometric analysis, larger samples lead to analytical outcomes that are likely to be more accurate ( Rogers et al., 2020 ). Therefore, Scopus appears to be a more relevant bibliographic database meeting the specifics of online learning research during the COVID-19 pandemic.

The search strategy was based on title, abstract, and keywords search using the advanced search engine and the search query covered keywords related to different online learning types (using the Boolean operator ‘OR’) and the COVID-19 pandemic (using the Boolean operator ‘AND’). The search was further limited to the period 2020–2022 (using the Boolean operator ‘AND’) to capture documents published between January 2020 and March 2022, when most countries were experiencing constant disruptions in the educational process imposed by repeated lockdowns. As the search query had no language restrictions, the full text of the obtained documents can be in any language, provided that the titles, abstracts, and keywords are in English. Therefore, the language has no impact on the results, as the bibliometric analysis is conducted solely based on the titles, abstracts, and keywords of the documents. According to the presented search query, 9,921 documents were obtained. After further revising the obtained documents, it was identified that some of them are not explicitly related to the context of higher education. By machine screening of documents by title, abstract, and keywords, those related to lower levels of education (i.e., primary and secondary education), as well as adult and worker education (i.e., lifelong education), were excluded from the database. There were 1,618 or 16% of such documents. The remaining 8,303 documents were identified as eligible for further bibliometric examination of online learning research during the COVID-19 pandemic. The bibliometric analysis utilized several bibliometric approaches ( Figure 1 ).

Figure 1 . Bibliometric approaches used in the bibliometric analysis. Own elaboration.

First, a descriptive overview was conducted to examine particular general bibliometric items, including timespan, number of (all, cited, single-authored) documents, authors, sources and author keywords and authors, references, and citations per document as well as to identify the most relevant documents. Scientific production was also examined to determine the most relevant countries and sources. Finally, network analysis was performed to identify the research hotspots according to the keyword co-occurrence network and examine the relationship between the main concepts based on a three-field plot analysis. The presented bibliometric approaches required the use of several different software tools. The descriptive overview was conducted using the Python Data Analysis Library Pandas ( McKinney, 2012 ), scientific production was visualized by the Python Visualization Library Matplotlib ( Hunter, 2007 ), while network analysis was performed using VOSviewer (keyword co-occurrence) ( Van Eck and Waltman, 2010 ) and the Python Visualization Library Plotly (a three-field plot) ( Pandey and Panchal, 2020 ). Specifically, the calculation for the three-field plot analysis included the following steps. Suppose that C 1 , C 2 , … , C m are analysed concepts where each concept C i is defined by a set of keywords and represented by binary indicators W i 1 , W i 2 , … , W i k i , expressed as C i = max j = 1 , … , k i W i j for i = 1 , … , m (matrix column). Using this notation, the relationship between C i and C j can be defined as C 1 T ∗ C j (matrix multiplication) where i and j are from three different sets (ICT tools, online learning approaches, fields of study).

The descriptive overview presented in Table 1 shows the main characteristics of online learning and COVID-19 research in the higher education context. This research area covers a total of 8,303 documents (of which 7,922 (95%) have the full text in English) published in 2,447 sources between January 2020 and March 2022. Slightly less than half (46%) of these documents have at least one citation, while a relatively small number (15%) were written by a single author. The average number of references per document in this research area is 31.39, which is below the general scientific area of Educational Research (44.00) ( Patience et al., 2017 ), suggesting that online learning research during the COVID-19 pandemic is grounded on fewer existing studies than general research. Finally, 3.50 citations per document can be observed for this research area. Due to the potential benefits of online learning, especially when combined with the traditional learning approaches and hence the development of the blended learning environment, this research is expected to further develop and be extended in the ensuing years ( Fauzi, 2022 ). Further, upon analyzing the documents, it is evident that the average year of references is 2014.03, with an h-index of 60 (indicating at least 60 papers with 60 or more citations each) and a g-index of 94 (denoting that the top 94 publications have accumulated citations equal to or greater than the square of 94). Finally, it was found that within the examined dataset, a total of 1,334 documents (16%) have achieved a minimum of 5 citations (C5), while 691 documents (8%) have attained at least 10 citations (C10), 302 documents (4%) have obtained a minimum of 20 citations (C20), 79 documents (1%) have acquired at least 50 citations (C50), and 31 documents (0.4%) have obtained more than 100 citations (C100).

Table 1 . Descriptive overview of online learning and COVID-19 research (2020–2022).

The most relevant (top-10) highly cited documents in online learning and COVID-19 research in the context of higher education are shown in Table 2 . The overview of the most relevant documents reveals several important topics that were intensively discussed. The first most relevant topic concerns ICT. The COVID-19 pandemic has created significant challenges for higher education, especially for medical and surgical education, which requires personal attendance in clinical activities and labs. Accordingly, several innovative ICT tools (i.e., videoconferencing, social media, and telemedicine) and online learning approaches (i.e., flipped classroom or blended learning and virtual learning) were proposed to address this challenge. It is also stressed that by using appropriately established ICT solutions, online learning can lead to more sustainable education ( Adedoyin and Soykan, 2020 ; Chick et al., 2020 ; Dedeilia et al., 2020 ).

Table 2 . Most relevant documents in online learning and COVID-19 research (2020–2022).

The next top-cited topic relates to pedagogy. The disruption of education around the world due to the COVID-19 pandemic required teachers to possess specific pedagogical content knowledge related to designing and organizing better learning experiences with digital technologies. At the same time, challenges for online assessment and post-pandemic pedagogy are also highlighted ( García Peñalvo et al., 2020 ; Iyer et al., 2020 ; Murphy, 2020 ; Rapanta et al., 2020 ). Finally, life and work is another of the most cited topics. Namely, the COVID-19 pandemic has considerably reshaped education and other aspects of life and work, often also through the perspective of mental health or emotional well-being ( Dwivedi et al., 2020 ; Kapasia et al., 2020 ; Aristovnik et al., 2020a ).

Furthermore, it is noteworthy that all of the highly cited documents were published in 2020. However, it is also evident that there are notable and highly relevant publications that emerged in the second year of the COVID-19 pandemic. Accordingly, there are two documents with a minimum of 100 citations published in 2021. In the COVID-19 pandemic context, Watermeyer et al. (2021) , with 148 citations, examined the implications of digital disruption in universities within the United Kingdom, highlighting the challenges and opportunities arising from the emergency shift to online learning. Meanwhile, Pokhrel and Chhetri (2021) conducted a literature review to assess the impact of the COVID-19 pandemic on teaching and learning.

The scientific production across countries and sources is presented in terms of the number of documents and citations, whereby additional information is provided by a circle’s size, revealing the h-index as a measure of the scientific impact ( Harzing and Van Der Wal, 2009 ) and by its color, presenting the time dimension in scientific production. The most relevant (top-10) highly cited countries in online learning and COVID-19 research are shown in Figure 2 . While the United States of America stands out among all countries, the United Kingdom, China and India have a relatively large number of documents and citations. The findings are similar to those of other bibliometric studies on this topic ( Saqr et al., 2023 ).

Figure 2 . Most relevant countries in online learning and COVID-19 research (2020–2022). Own elaboration based on the Scopus database.

The most relevant (top-10) highly cited sources in online learning and COVID-19 research in the context of higher education are presented in Figure 3 . Despite conference proceedings being prominent in terms of the relatively high number of documents, the most prominent journals, considering the number of citations, are Journal of Chemical Education, with the highest number of citations as well as documents, followed by Sustainability, International Journal of Environmental Research and Public Health, and Education Sciences. More specifically, the most relevant journals address different topics. First, Journal of Chemical Education covers the attempts, successes and failures of distance learning during the COVID-19 pandemic in chemistry education. It covers various topics, including the development of at-home practical activities ( Schultz et al., 2020 ), student engagement and learning ( Perets et al., 2020 ), online assessments ( Nguyen et al., 2020 ) and virtual reality labs ( Williams et al., 2021 ). Further, Sustainability is focused on student and teacher perceptions of e-learning and related challenges ( Khan et al., 2020 ; Aristovnik et al., 2020a ) and sustainability in education during the COVID-19 pandemic ( Sobaih et al., 2020 ) to improve online learning and sustain higher education during uncertain times. Further, the International Journal of Environmental Research and Public Health covers various topics like the health and psychological implications of the COVID-19 pandemic ( Sundarasen et al., 2020 ), including well-being and changes in behavior and habits. Finally, Education Sciences publishes some general research on the challenges and opportunities for online learning ( Almazova et al., 2020 ), including student and teacher experiences ( García-Alberti et al., 2021 ; Müller et al., 2021 ).

Figure 3 . Most relevant sources in online learning and COVID-19 research (2020–2022). Own elaboration based on the Scopus database.

The keyword co-occurrence network is presented in Figure 4 . Note that the nodes indicate keywords and the links the relations of co-occurrence between them. The node size is proportional to the number of keyword occurrences, showing the research intensity (node degree), while the link width is proportional to the co-occurrences between keywords (edge weight). In addition, the node color indicates the cluster to which a particular keyword belongs ( Wang et al., 2020 ; Ravšelj et al., 2022 ). The keyword co-occurrence analysis reveals five research hotspots in online learning in higher education research during the COVID-19 pandemic. These are ICT and pedagogy (red cluster), technology-enhanced education (green cluster), mental health and well-being (blue cluster), student experience (yellow cluster) and curriculum and professional development (purple cluster).

Figure 4 . Keyword co-occurrence network in online learning and COVID-19 research (2020–2022). Own elaboration based on the Scopus database.

A detailed synopsis of the research hotspots, including representative (the most frequent) keywords and documents (with several representative keywords), is presented in Table 3 . The first research hotspot highlights the relevance of ICT and pedagogy in higher education during the COVID-19 pandemic. The most representative documents looked at the quality of online learning mechanisms ( Gritsova and Tissen, 2021 ), active learning activities ( Yan et al., 2021 ) and the role of e-learning departments in controlling the quality of academic processes ( Hamdan et al., 2021 ). The second research hotspot refers to technology-enhanced education from different perspectives, such as opportunities to incorporate technological and curricular innovations ( Shapiro and Reza, 2021 ), the adoption of different virtual experiences such as telehealth and virtual learning ( Kahwash et al., 2021 ), and the utilization of social media to reach higher education students ( Leighton et al., 2021 ). The third research hotspot emphasizes the problem of mental health and well-being issues that became a prevalent topic of discussion during the COVID-19 pandemic. Namely, several studies showed an increase in depression, anxiety and stress levels among higher education students in response to the COVID-19 pandemic ( Abu Kwaik et al., 2021 ; Keskin, 2021 ; Yaghi, 2022 ). The fourth cluster is about student experience during the COVID-19 pandemic with specific focus on the between interaction and online learning satisfaction ( Bawa'aneh, 2021 ; Bismala and Manurung, 2021 ; She et al., 2021 ). The fifth research hotspot underscores the relevance of curriculum and professional development. Several studies described the ways in which courses were adapted to online learning during the COVID-19 pandemic as well as the related challenges and strategies ( Chen et al., 2020 ; Gonzalez and Knecht, 2020 ; Rhile, 2020 ).

Table 3 . Research hotspots based on the author keyword co-occurrence network in online learning and COVID-19 research (2020–2022).

Finally, the three-field plot analysis of the relationship between the main concepts (i.e., ICT tools, online learning approaches, fields of study) is presented in a Sankey diagram shown in Figure 5 . The size of a rectangle corresponds to the number of documents for each theme, while the edge width reflects the inclusion index for connected themes ( Wang et al., 2020 ; Ravšelj et al., 2022 ). These three concepts have been proven to be relevant in the context of online learning. Namely, ICT tools are a precondition for delivering course content through different online learning approaches, while the choice of online learning approaches may depend on the field of study ( Ferri et al., 2020 ). During the COVID-19 pandemic, most attention was devoted to exploring e-learning (a combination of asynchronous and synchronous learning), distance learning (pre-recorded online lectures), followed by virtual learning (real-time online lectures). Since all these online learning approaches limit physical contact between teachers and students, they have been referred to as emergency remote learning approaches ( Hodges et al., 2020 ; Fauzi, 2022 ; Fuchs, 2022 ), while other online learning approaches (computer-based learning, blended learning, m-learning) do not necessarily take place in an online learning environment. The emergency remote learning approaches were primarily supported by several ICT tools, particularly by social media (e.g., Facebook), gamification/simulation and virtual reality (integration of game-like elements into online learning platforms, mobile applications, or virtual reality simulations), Zoom and other videoconferencing platforms, as well as telehealth (for educating health professionals). Regarding the fields of study, e-learning, distance learning and virtual learning were mostly addressed in the context of medical/health education, while computer-based learning (i.e., specific engineering software programs etc.) was examined in the context of engineering education. This implies that the specific requirements of a given field of study often guide the selection of the most suitable online learning approaches ( Fauzi, 2022 ).

Figure 5 . Three-field plot showing the network between ICT tools (left), online approaches (middle), and fields of study (right) (2020–2022). Own elaboration based on the Scopus database.

5. Conclusion

The presented bibliometric study provides several important insights arising from research into online learning during the COVID-19 pandemic. In this period, a large volume of scientific knowledge was produced in the context of education that considered a range of aspects ( Saqr et al., 2023 ). Therefore, a combination of selected bibliometric approaches was utilized to extract some general comprehensive outlines of the global research. The bibliometric analysis revealed the following.

As suggested by the descriptive overview of the state of Educational Research ( Patience et al., 2017 ), the research into online learning during the COVID-19 pandemic is characterized by greater cooperation between authors, which coincides with the general observation that (international) scientific collaboration grew significantly during the pandemic ( Duan and Xia, 2021 ). Further, online learning research during the COVID-19 pandemic is grounded on fewer studies than Educational Research ( Patience et al., 2017 ), which may be explained by the absence of COVID-19-related literature at the time these documents were published. Nevertheless, noting the potential benefits of online learning approaches also when the epidemiological conditions are favorable, this line of research is expected to further develop and be extended in the ensuing years ( Fauzi, 2022 ). The potential benefits refer especially to the development of a blended learning environment, which combines online and traditional learning approaches ( Rasheed et al., 2020 ). The overview of the most relevant documents revealed three topics that were intensively discussed in the academic community, i.e., ICT, pedagogy, and life and work. The COVID-19 pandemic highlighted the importance and role of reliable ICT infrastructure for ensuring effective pedagogy in the online environment, as was needed to prevent the spread of the virus and to protect public health. Apart from the devastating health consequences for those directly affected by the virus and the disrupted educational process, the COVID-19 pandemic also dramatically affected students’ social life and work ( Aristovnik et al., 2020a ). The educational community is increasingly interested in finding ways to respond to crises like the COVID-19 pandemic and develop effective pedagogical practices that assure high-quality and efficient education in the online learning environment ( Zhang et al., 2022 ).

The scientific production of online learning during the COVID-19 pandemic was geographically uneven. The greatest scientific production in terms of citations and number of documents can be observed in the United States, followed by the United Kingdom, China and India. Besides developed English-speaking countries, emerging Asian economies also seem to have played a crucial role in online learning research. Similar findings also emerged from other bibliometric studies on this topic ( Saqr et al., 2023 ). Moreover, despite conference proceedings being prominent in terms of the relatively high number of documents, the most prominent journals, considering the number of citations, are Journal of Chemical Education, Sustainability, International Journal of Environmental Research and Public Health and Education Sciences, indicating that online learning research at the time of the COVID-19 pandemic was primarily published in open-access journals, as already observed in other research ( Zhang et al., 2022 ).

The network analysis revealed five research hotspots in online learning research during the COVID-19 pandemic in the context of higher education: (1) ICT and pedagogy, focused on the quality of online learning mechanisms ( Gritsova and Tissen, 2021 ), active learning activities ( Yan et al., 2021 ) and the role of e-learning departments in controlling the quality of academic processes ( Hamdan et al., 2021 ); technology-enhanced education concentrated on opportunities to incorporate technological and curricular innovations ( Shapiro and Reza, 2021 ), the adoption of different virtual experiences such as telehealth and virtual learning ( Kahwash et al., 2021 ), and the utilization of social media to reach higher education students ( Leighton et al., 2021 ); (2) mental health and well-being issues facing higher education students, including depression, anxiety, and stress levels ( Abu Kwaik et al., 2021 ; Keskin, 2021 ; Yaghi, 2022 ); student experience with specific focus on the between interaction and online learning satisfaction ( Bawa'aneh, 2021 ; Bismala and Manurung, 2021 ; She et al., 2021 ) and (3) curriculum and professional development, focused on the ways in which courses were adapted to online learning during the COVID-19 pandemic as well as the related challenges and strategies ( Chen et al., 2020 ; Gonzalez and Knecht, 2020 ; Rhile, 2020 ).

Further, the COVID-19 pandemic led to the exploration of emergency remote learning approaches such as e-learning, distance learning and virtual learning, which are intended to limit physical contact between teachers and students. These approaches were chiefly supported by several ICT tools, including social media, gamification/simulation, virtual reality, videoconferencing platforms, and telehealth. While computer-based learning, blended learning and m-learning do not necessarily occur in an online learning environment, they may still be suitable for certain fields of study, especially in the post-COVID-19 pandemic period. This implies that the determination of which online learning approach is the most suitable is often guided by the specific requirements of a given field of study ( Fauzi, 2022 ).

Before generalizing these conclusions, it is important to note the limitations of the paper. First, the bibliometric analysis relied on documents indexed in the Scopus database, which might not cover the entire collection of research. Namely, documents that are published in journals indexed in other databases such as Web of Science, Education Research Index, Educational Resources Information Centre, etc. are not included in the analysis. However, to achieve the comparability of bibliometric metrics across documents, the bibliometric metrics are obtained from the single and, in general, broader Scopus database. Given the substantial overlap of documents across different databases of peer-reviewed literature, this limitation might not significantly affect the general observations on global research trends. Nevertheless, to check the robustness of the findings, it is still valuable to consider other bibliometric databases for future research. Second, the bibliometric analysis is conducted the bibliometric is based on a short time period (January 2020 – March 2022), which may also impact the metrics of documents published in closed-access (subscription-based) journals, placing them at a disadvantage compared to documents published in open-access journals. While it is not possible to overcome this limitation at present, conducting a bibliometric study with a longer time span would provide further time-dimensional insights. This would also be beneficial in terms of achieving better comparability between documents published in closed-access and open-access journals. Finally, despite the detailed search queries, some other relevant keywords may have been overlooked in the document search. Finally, the bibliometric method, as a method based on big data analysis, may miss certain highlights from the scientific literature that a systematic literature review would otherwise capture. Therefore it would be beneficial for future bibliometric studies also to incorporate a systematic literature review methodology, as the combined approach can provide a more comprehensive and nuanced understanding of the implications of the COVID-19 pandemic on online learning in higher education.

The bibliometric study provides some possible avenues for future research. First, in future bibliometric studies, it would be beneficial to conduct in-depth analyses of the relevant contexts that have emerged as highly significant in online learning during the pandemic. These include ICT and innovation, mental health and well-being, online learning and engagement, and curriculum and professional development. Examining these contexts more comprehensively can provide valuable insights into the specific dynamics and trends within each area, contributing to a deeper understanding of the implications of online learning during the pandemic. Second, it would be beneficial to conduct separate bibliometric analyses and comparisons to examine the differences between developed and developing countries. This approach can shed light on the unique research trends, contributions, and challenges faced by each group of countries in the context of online learning during the pandemic. This can provide a more nuanced understanding of the global landscape and identify potential areas for collaboration and knowledge sharing between developed and developing countries. Finally, it would be valuable to investigate the long-term impact of rapid publishing in open-access journals on the recognition and dissemination of scholarly findings in the field of online learning in higher education during the pandemic.

From the practical perspective, the COVID-19 pandemic has significantly disrupted higher education, but at the same time, it also accelerated the use of online learning tools in the educational process. Although the COVID-19 pandemic has gradually subsided over time, online learning approaches developed during this period continue to hold relevance and value for future education. Therefore, higher education institutions should prioritize leveraging ICT tools and innovative solutions in their educational delivery, which proved effective during the pandemic. Moreover, higher education institutions should also prioritize adapting appropriate online learning approaches and curricula to align with modern realities and the corresponding fields of study. This adaptation is crucial for enhancing student engagement and ensuring that educational programs remain relevant and responsive to the evolving needs of students in various disciplines.

The findings may help not only the scientific community in detecting research gaps in online learning research during the COVID-19 pandemic but also evidence-based policymaking by assisting in identifying appropriate educational practices in emergency circumstances. Specifically, the findings may help higher education policymakers to address the underlying shortcomings of the existing educational framework exposed by the COVID-19 pandemic and to design proactive mechanisms to deal effectively with such disruptions, thereby enabling them to create a more resilient and adaptable education system that can successfully navigate unforeseen challenges and ensure the continuity of quality higher education in the future.

Data availability statement

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding authors.

Author contributions

AA contributed to the design of the study. DR and LU assisted with the data identification, cleaning, and analysis. DR and KK wrote the manuscript in consultation with AA. All authors contributed to the manuscript’s revision and read and approved the submitted version.

This research and the APC were funded by the Slovenian Research Agency under grant numbers P5-0093 and Z5-4569.

Acknowledgments

The authors acknowledge the financial support from the Slovenian Research Agency (research core funding no. P5-0093 and project no. Z5-4569). A preliminary version of the paper was presented at the International Conference on Information, Communication Technologies in Education (ICICTE) in July 2022. The authors are grateful to colleagues who attended the presentation and provided interesting comments and suggestions. Further, they wish to thank the reviewers for their valuable suggestions and comments.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Abu Kwaik, A., Saleh, R., Danadneh, M., and Kateeb, E. (2021). Stress, anxiety and depression among dental students in times of covid-19 lockdown. Int. J. Dentist. Oral Sci. 8, 1560–1564. doi: 10.19070/2377-8075-21000310

CrossRef Full Text | Google Scholar

Adedoyin, O. B., and Soykan, E. (2020). COVID-19 pandemic and online learning: the challenges and opportunities. Interact. Learn. Environ. 31, 863–875. doi: 10.1080/10494820.2020.1813180

Almazova, N., Krylova, E., Rubtsova, A., and Odinokaya, M. (2020). Challenges and opportunities for Russian higher education amid COVID-19: teachers’ perspective. Educ. Sci. 10:368. doi: 10.3390/educsci10120368

Aristovnik, A., Keržič, D., Ravšelj, D., Tomaževič, N., and Umek, L. (2020a). Impacts of the COVID-19 pandemic on life of higher education students: a global perspective. Sustainability 12:8438. doi: 10.3390/su12208438

Aristovnik, A., Ravšelj, D., and Umek, L. (2020b). A bibliometric analysis of COVID-19 across science and social science research landscape. Sustainability 12:9132. doi: 10.3390/su12219132

Baber, H., Fanea-Ivanovici, M., Lee, Y. T., and Tinmaz, H. (2022). A bibliometric analysis of digital literacy research and emerging themes pre-during COVID-19 pandemic. Inform. Learn. Sci. 123, 214–232. doi: 10.1108/ILS-10-2021-0090

Bawa'aneh, M. S. (2021). Distance learning during COVID-19 pandemic in UAE public schools: student satisfaction, attitudes and challenges. Contemp. Educ. Technol. 13:10872. doi: 10.30935/cedtech/10872

Bilal, H., Hysa, E., Akbar, A., Yasmin, F., Rahman, A. u., and Li, S. (2022). Virtual learning during the COVID-19 pandemic: a bibliometric review and future research agenda. Risk Manag. Healthcare Policy 15, 1353–1368. doi: 10.2147/RMHP.S355895

PubMed Abstract | CrossRef Full Text | Google Scholar

Bismala, L., and Manurung, Y. H. (2021). Student satisfaction in E-learning along the COVID-19 pandemic with importance performance analysis. Int. J. Eval. Res. Educ. 10, 753–759. doi: 10.11591/ijere.v10i3.21467

Bonsaksen, T., Leung, J., Schoultz, M., Thygesen, H., Price, D., Ruffolo, M., et al. (2021). Cross-National Study of worrying, loneliness, and mental health during the COVID-19 pandemic: a comparison between individuals with and without infection in the family. Healthcare 9:903. doi: 10.3390/healthcare9070903

Bozkurt, A. (2022). A retro perspective on blended/hybrid learning: systematic review, mapping and visualization of the scholarly landscape. J. Interact. Media Educ. 2022, 1–15. doi: 10.5334/jime.751

Brika, S. K. M., Chergui, K., Algamdi, A., Musa, A. A., and Zouaghi, R. (2021). E-learning research trends in higher education in light of COVID-19: a bibliometric analysis. Front. Psychol. 12:762819. doi: 10.3389/fpsyg.2021.762819

Campos, E., Daruich, S. D. N., Escamilla, J., and Hosseini, S. (2022). Educational model transition: student evaluation of teaching amid the COVID-19 pandemic. Front. Educ. 7:991654. doi: 10.3389/feduc.2022.991654

Chen, K., Chen, Y., Ling, Y., and Lin, J. (2020). The individual experience of online chemistry teacher education in China: coping with COVID-19 pandemic. J. Chem. Educ. 97, 3265–3270. doi: 10.1021/acs.jchemed.0c00581

Chick, R. C., Clifton, G. T., Peace, K. M., Propper, B. W., Hale, D. F., Alseidi, A. A., et al. (2020). Using technology to maintain the education of residents during the COVID-19 pandemic. J. Surg. Educ. 77, 729–732. doi: 10.1016/j.jsurg.2020.03.018

Dedeilia, A., Papapanou, M., Papadopoulos, A. N., Karela, N. R., Androutsou, A., Mitsopoulou, D., et al. (2023). Health worker education during the COVID-19 pandemic: global disruption, responses and lessons for the future—a systematic review and meta-analysis. Hum. Resour. Health 21, 13–35. doi: 10.1186/s12960-023-00799-4

Dedeilia, A., Sotiropoulos, M. G., Hanrahan, J. G., Janga, D., Dedeilias, P., and Sideris, M. (2020). Medical and surgical education challenges and innovations in the COVID-19 era: a systematic review. In Vivo 34, 1603–1611. doi: 10.21873/invivo.11950

Djeki, E., Dégila, J., Bondiombouy, C., and Alhassan, M. H. (2022). E-learning bibliometric analysis from 2015 to 2020. J. Comput. Educ. 9, 727–754. doi: 10.1007/s40692-021-00218-4

Duan, D., and Xia, Q. (2021). Evolution of scientific collaboration on COVID-19: a bibliometric analysis. Learn. Publish. 34, 429–441. doi: 10.1002/leap.1382

Dwivedi, Y. K., Hughes, D. L., Coombs, C., Constantiou, I., Duan, Y., Edwards, J. S., et al. (2020). Impact of COVID-19 pandemic on information management research and practice: transforming education, work and life. Int. J. Inf. Manag. 55:102211. doi: 10.1016/j.ijinfomgt.2020.102211

Elmer, T., Mepham, K., and Stadtfeld, C. (2020). Students under lockdown: comparisons of students' social networks and mental health before and during the COVID-19 crisis in Switzerland. PLoS One 15:e0236337. doi: 10.1371/journal.pone.0236337

Falagas, M. E., Pitsouni, E. I., Malietzis, G. A., and Pappas, G. (2008). Comparison of PubMed, Scopus, web of science, and Google scholar: strengths and weaknesses. FASEB J. 22, 338–342. doi: 10.1096/fj.07-9492LSF

Fauzi, M. A. (2022). E-learning in higher education institutions during COVID-19 pandemic: current and future trends through bibliometric analysis. Heliyon 8:e09433. doi: 10.1016/j.heliyon.2022.e09433

Ferri, F., Grifoni, P., and Guzzo, T. (2020). Online learning and emergency remote teaching: opportunities and challenges in emergency situations. Societies 10:86. doi: 10.3390/soc10040086

Fried, E. I., Papanikolaou, F., and Epskamp, S. (2021). Mental health and social contact during the COVID-19 pandemic: an ecological momentary assessment study. Clin. Psychol. Sci. 10, 340–354. doi: 10.1177/21677026211017839

Fuchs, K. (2022). The difference between emergency remote teaching and e-learning. Front. Educ. 7:921332. doi: 10.3389/feduc.2022.921332

Gao, Y., Wong, S. L., Khambari, M. N., and Noordin, N. (2022). A bibliometric analysis of the scientific production of e-learning in higher education (1998-2020). Int. J. Inform. Educ. Technol. 12, 390–399. doi: 10.18178/ijiet.2022.12.5.1632

García Peñalvo, F. J., Corell Almuzara, A., Abella García, V., and Grande de Prado, M. (2020). La evaluación online en la educación superior en tempos’ de la COVID-19. Educ. Knowl. Soc. 21:23086. doi: 10.14201/eks.23086

García-Alberti, M., Suárez, F., Chiyón, I., and Mosquera Feijoo, J. C. (2021). Challenges and experiences of online evaluation in courses of civil engineering during the lockdown learning due to the COVID-19 pandemic. Educ. Sci. 11:59. doi: 10.3390/educsci11020059

Gavriluţă, C., Dalban, C. M., and Ioan, B. G. (2022). Educational, emotional, and social impact of the emergency state of COVID-19 on Romanian university students. Int. J. Environ. Res. Public Health 19:3990. doi: 10.3390/ijerph19073990

Gonzalez, C., and Knecht, L. D. (2020). Strategies employed in transitioning multi-instructor, multisection introductory general and organic chemistry courses from face-to-face to online learning. J. Chem. Educ. 97, 2871–2877. doi: 10.1021/acs.jchemed.0c00670

Gritsova, O. A., and Tissen, E. V. (2021). Quality assessment of online learning in regional higher education systems. Econ. Regions 17, 929–943. doi: 10.17059/ekon.reg.2021-3-15

Gurcan, F., Dalveren, G. G. M., and Derawi, M. (2022). COVID-19 and E-learning: an exploratory analysis of research topics and interests in E-learning during the pandemic. IEEE Access 10, 123349–123357. doi: 10.1109/ACCESS.2022.3224034

Hamdan, R., Ashour, W., and Daher, W. (2021). The role of the e-learning departments in controlling the quality of electronic assessments in Palestinian universities during the COVID-19 pandemic. Sustainability 13:12021. doi: 10.3390/su132112021

Harzing, A. W., and Van Der Wal, R. (2009). A Google scholar h-index for journals: an alternative metric to measure journal impact in economics and business. J. Am. Soc. Inf. Sci. Technol. 60, 41–46. doi: 10.1002/asi.20953

Hodges, C. B., Moore, S., Lockee, B. B., Trust, T., and Bond, M. A. (2020). The difference between emergency remote teaching and online learning. Virginia Tech Online Available online at: http://hdl.handle.net/10919/104648 (Accessed February 15, 2023).

Google Scholar

Hunter, J. D. (2007). Matplotlib: a 2D graphics environment. Comput. Sci. Eng. 9, 90–95. doi: 10.1109/MCSE.2007.55

Iyer, P., Aziz, K., and Ojcius, D. M. (2020). Impact of COVID-19 on dental education in the United States. J. Dent. Educ. 84, 718–722. doi: 10.1002/jdd.12163

James, N., and Thériault, V. (2020). Adult education in times of the COVID-19 pandemic: inequalities, changes, and resilience. Stud. Educ. Adults 52, 129–133. doi: 10.1080/02660830.2020.1811474

Kahwash, B. M., Deshpande, D. R., Guo, C., Panganiban, C. M., Wangberg, H., and Craig, T. J. (2021). Allergy/immunology trainee experiences during the COVID-19 pandemic: AAAAI work group report of the fellows-in-training committee. J. Allergy Clin. Immunol. Pract. 9, 1–6.e1. doi: 10.1016/j.jaip.2020.09.036

Kapasia, N., Paul, P., Roy, A., Saha, J., Zaveri, A., Mallick, R., et al. (2020). Impact of lockdown on learning status of undergraduate and postgraduate students during COVID-19 pandemic in West Bengal, India. Child Youth Serv. Rev. 116:105194. doi: 10.1016/j.childyouth.2020.105194

Kartimi, K., Yunita, Y., Addiin, I., and Shidiq, A. S. (2022). A bibliometric analysis on chemistry virtual laboratory. Educación Química 33:194. doi: 10.22201/fq.18708404e.2022.2.80579

Keržič, D., Alex, J. K., Pamela Balbontín Alvarado, R., da Silva Bezerra, D., Cheraghi, M., Dobrowolska, B., et al. (2021). Academic student satisfaction and perceived performance in the e-learning environment during the COVID-19 pandemic: evidence across ten countries. PLoS One 16:e0258807. doi: 10.1371/journal.pone.0258807

Keržič, D., Umek, L., Tomaževič, N., and Aristovnik, A. (2022). E-learning acceptance of public administration students in Slovenia in comparison with Europe: differences between the first and second waves of the COVID-19 pandemic. EGPA 2022 Conference, 6–9 September 2022, Lisbon, Portugal.

Keskin, G. (2021). Self-report measurement of depression, anxiety, and stress caused by covid-19 pandemic in senior undergraduate dental students. Pesquisa Brasileira em Odontopediatria e Clínica Integrada 21:102. doi: 10.1590/pboci.2021.102

Khan, M. A., Nabi, M. K., Khojah, M., and Tahir, M. (2020). Students’ perception towards e-learning during COVID-19 pandemic in India: an empirical study. Sustainability 13:57. doi: 10.3390/su13010057

Küçük-Avci, Ş., Topal, M., and İstanbullu, A. (2022). The effects of the COVID-19 pandemic on distance education in higher education: a bibliometric analysis study. Croatian J. Educ. 24, 457–488. doi: 10.15516/cje.v24i2.4534

Leighton, K., Kardong-Edgren, S., Schneidereith, T., and Foisy-Doll, C. (2021). Using social media and snowball sampling as an alternative recruitment strategy for research. Clin. Simul. Nurs. 55, 37–42. doi: 10.1016/j.ecns.2021.03.006

McKinney, W. (2012). Python for data analysis: Data wrangling with pandas, NumPy, and IPython . Sebastopol, CA: O'Reilly Media, Inc.

Misiejuk, K., Ness, I. J., Gray, R., and Wasson, B. (2023). Changes in online course designs: before, during, and after the pandemic. Front. Educ. 7:996006. doi: 10.3389/feduc.2022.996006

Mongeon, P., and Paul-Hus, A. (2016). The journal coverage of web of science and Scopus: a comparative analysis. Scientometrics 106, 213–228. doi: 10.1007/s11192-015-1765-5

Müller, A. M., Goh, C., Lim, L. Z., and Gao, X. (2021). COVID-19 emergency elearning and beyond: experiences and perspectives of university educators. Educ. Sci. 11:19. doi: 10.3390/educsci11010019

Murphy, M. P. (2020). COVID-19 and emergency eLearning: consequences of the securitization of higher education for post-pandemic pedagogy. Contemp. Security Policy 41, 492–505. doi: 10.1080/13523260.2020.1761749

Navarro-Espinosa, J. A., Vaquero-Abellán, M., Perea-Moreno, A.-J., Pedrós-Pérez, G., Aparicio-Martínez, P., and Martínez-Jiménez, M. P. (2021). The influence of technology on mental well-being of STEM teachers at university level: COVID-19 as a stressor. Int. J. Environ. Res. Public Health 18:9605. doi: 10.3390/ijerph18189605

Nguyen, J. G., Keuseman, K. J., and Humston, J. J. (2020). Minimize online cheating for online assessments during COVID-19 pandemic. J. Chem. Educ. 97, 3429–3435. doi: 10.1021/acs.jchemed.0c00790

Ochnik, D., Rogowska, A. M., Kuśnierz, C., Jakubiak, M., Wierzbik-Strońska, M., Schütz, A., et al. (2021). Exposure to COVID-19 during the first and the second wave of the pandemic and coronavirus-related PTSD risk among university students from six countries-a repeated cross-sectional study. J. Clin. Med. 10:5564. doi: 10.3390/jcm10235564

Okoro, C., Owojori, O. M., and Umeokafor, N. (2022). The developmental trajectory of a decade of research on mental health and well-being amongst graduate students: a bibliometric analysis. Int. J. Environ. Res. Public Health 19:4929. doi: 10.3390/ijerph19094929

Pandey, K., and Panchal, R. (2020). A study of real world data visualization of COVID-19 dataset using Python. Int. J. Manag. Human. 4, 104–107. doi: 10.35940/ijmh.H0834.044820

Patience, G. S., Patience, C. A., Blais, B., and Bertrand, F. (2017). Citation analysis of scientific categories. Heliyon 3:e00300. doi: 10.1016/j.heliyon.2017.e00300

Perets, E. A., Chabeda, D., Gong, A. Z., Huang, X., Fung, T. S., Ng, K. Y., et al. (2020). Impact of the emergency transition to remote teaching on student engagement in a non-STEM undergraduate chemistry course in the time of COVID-19. J. Chem. Educ. 97, 2439–2447. doi: 10.1021/acs.jchemed.0c00879

Pham, P. T., Lien, D. T. H., Kien, H. C., Chi, N. H., Tinh, P. T., Do, T., et al. (2022). Learning management system in developing countries: a bibliometric analysis between 2005 and 2020. Europ. J. Educ. Res. 11, 1363–1377. doi: 10.12973/eu-jer.11.3.1363

Pokhrel, S., and Chhetri, R. (2021). A literature review on impact of COVID-19 pandemic on teaching and learning. High. Educ. Future 8, 133–141. doi: 10.1177/234763112098348

Raccanello, D., Balbontín-Alvarado, R., da Silva Bezerra, D., Burro, R., Cheraghi, M., Dobrowolska, B., et al. (2022). Higher education students’ achievement emotions and their antecedents in e-learning amid COVID-19 pandemic: a multi-country survey. Learn. Instr. 80:101629. doi: 10.1016/j.learninstruc.2022.101629

Rapanta, C., Botturi, L., Goodyear, P., Guàrdia, L., and Koole, M. (2020). Online university teaching during and after the COVID-19 crisis: refocusing teacher presence and learning activity. Postdigital Sci. Educ. 2, 923–945. doi: 10.1007/s42438-020-00155-y

Rasheed, R. A., Kamsin, A., and Abdullah, N. A. (2020). Challenges in the online component of blended learning: a systematic review. Comput. Educ. 144:103701. doi: 10.1016/j.compedu.2019.103701

Rasli, A., Tee, M., Ling, L. Y., Cheak, T. Z., and Hui, S. E. (2022). Post COVID-19 strategies for higher education institutions in dealing with unknown and uncertainties. Front. Educ. 7:992063. doi: 10.3389/feduc.2022.992063

Ravšelj, D., Umek, L., Todorovski, L., and Aristovnik, A. (2022). A review of digital era governance research in the first two decades: a bibliometric study. Future Internet 14:126. doi: 10.3390/fi14050126

Rhile, I. J. (2020). Course redesign for college general chemistry during the COVID-19 pandemic. J. Chem. Educ. 97, 2857–2862. doi: 10.1021/acs.jchemed.0c00618

Rogers, G., Szomszor, M., and Adams, J. (2020). Sample size in bibliometric analysis. Scientometrics 125, 777–794. doi: 10.1007/s11192-020-03647-7

Rojas-Sánchez, M. A., Palos-Sánchez, P. R., and Folgado-Fernández, J. A. (2022). Systematic literature review and bibliometric analysis on virtual reality and education. Educ. Inf. Technol. 28, 155–192. doi: 10.1007/s10639-022-11167-5

Saqr, M., Raspopovic Milic, M., Pancheva, K., Jovic, J., Peltekova, E. V., and Conde, M. Á. (2023). A multimethod synthesis of COVID-19 education research: the tightrope between covidization and meaningfulness. Univ. Access Inf. Soc. 1-14, 1–14. doi: 10.1007/s10209-023-00989-w

Schultz, M., Callahan, D. L., and Miltiadous, A. (2020). Development and use of kitchen chemistry home practical activities during unanticipated campus closures. J. Chem. Educ. 97, 2678–2684. doi: 10.1021/acs.jchemed.0c00620

Shapiro, H., and Reza, N. (2021). Cardiovascular medical education during the coronavirus disease 2019 pandemic: challenges, adaptations, and considerations for the future. US Cardiol. 15:25. doi: 10.15420/usc.2020.25

She, L., Ma, L., Jan, A., Sharif Nia, H., and Rahmatpour, P. (2021). Online learning satisfaction during COVID-19 pandemic among Chinese university students: the serial mediation model. Front. Psychol. 12:743936. doi: 10.3389/fpsyg.2021.743936

Sobaih, A. E. E., Hasanein, A. M., and Abu Elnasr, A. E. (2020). Responses to COVID-19 in higher education: social media usage for sustaining formal academic communication in developing countries. Sustainability 12:6520. doi: 10.3390/su12166520x

Sundarasen, S., Chinna, K., Kamaludin, K., Nurunnabi, M., Baloch, G. M., Khoshaim, H. B., et al. (2020). Psychological impact of COVID-19 and lockdown among university students in Malaysia: implications and policy recommendations. Int. J. Environ. Res. Public Health 17:6206. doi: 10.3390/ijerph17176206

Tang, K. H. D. (2023). Impacts of COVID-19 on primary, secondary and tertiary education: a comprehensive review and recommendations for educational practices. Educ. Res. Policy Prac. 22, 23–61. doi: 10.1007/s10671-022-09319-y

Tlili, A., Altınay, F., Altınay, Z., Aydın, C. H., Huang, R., and Sharma, R. (2022). Reflections on massive open online courses (Moocs) during the COVID-19 pandemic: a bibliometric mapping analysis. Turk. Online J. Dist. Educ. 23, 1–17. doi: 10.17718/tojde.1137107

Vaicondam, Y., Sikandar, H., Irum, S., Khan, N., and Qureshi, M. I. (2022). Research landscape of digital learning over the past 20 years: a bibliometric and visualisation analysis. Int. J. Online Biomed. Eng. 18, 4–22. doi: 10.3991/ijoe.v18i08.31963

Van der Graaf, L., Dunajeva, J., Siarova, H., and Bankauskaite, R. (2021). Research for CULT committee – Education and youth in post-COVID-19 Europe – Crisis effects and policy recommendations. European Parliament, Policy Department for Structural and Cohesion Policies, Brussels. Available at: http://www.europarl.europa.eu/thinktank/en/document.html?reference=IPOL_STU(2021)690872 (Accessed February 15, 2023).

Van Eck, N., and Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics 84, 523–538. doi: 10.1007/s11192-009-0146-3

Vera-Baceta, M. A., Thelwall, M., and Kousha, K. (2019). Web of science and Scopus language coverage. Scientometrics 121, 1803–1813. doi: 10.1007/s11192-019-03264-z

Waller, R., Hodge, S., Holford, J., Milana, M., and Webb, S. (2020). Lifelong education, social inequality and the COVID-19 health pandemic. Int. J. Lifelong Educ. 39, 243–246. doi: 10.1080/02601370.2020.1790267

Wang, C., Lim, M. K., Zhao, L., Tseng, M. L., Chien, C. F., and Lev, B. (2020). The evolution of omega-the international journal of management science over the past 40 years: a bibliometric overview. Omega 93:102098. doi: 10.1016/j.omega.2019.08.005

Watermeyer, R., Crick, T., Knight, C., and Goodall, J. (2021). COVID-19 and digital disruption in UK universities: afflictions and affordances of emergency online migration. High. Educ. 81, 623–641. doi: 10.1007/s10734-020-00561-y

Williams, N. D., Gallardo-Williams, M. T., Griffith, E. H., and Bretz, S. L. (2021). Investigating meaningful learning in virtual reality organic chemistry laboratories. J. Chem. Educ. 99, 1100–1105. doi: 10.1021/acs.jchemed.1c00476

Yaghi, A. (2022). Impact of online education on anxiety and stress among undergraduate public affairs students: a longitudinal study during the COVID-19 pandemic. J. Public Affairs Educ. 28, 91–108. doi: 10.1080/15236803.2021.1954469

Yan, Y., Cheng, X., Zhou, C., Yang, X., and Li, Y. Q. (2021). The perceptions of anatomy teachers for different majors during the COVID-19 pandemic: a national Chinese survey. Med. Educ. Online 26:1897267. doi: 10.1080/10872981.2021.1897267

Yan, H., Rahgozar, A., Sethuram, C., Karunananthan, S., Archibald, D., Bradley, L., et al. (2022). Natural language processing to identify digital learning tools in postgraduate family medicine: protocol for a scoping review. JMIR Res. Protocols 11:e34575. doi: 10.2196/34575

Zhang, L., Carter, R. A. Jr., Qian, X., Yang, S., Rujimora, J., and Wen, S. (2022). Academia’s responses to crisis: a bibliometric analysis of literature on online learning in higher education during COVID-19. Br. J. Educ. Technol. 53, 620–646. doi: 10.1111/bjet.13191

Keywords: online learning, e-learning, higher education, bibliometrics, mapping, visualization, VOSviewer, COVID-19

Citation: Aristovnik A, Karampelas K, Umek L and Ravšelj D (2023) Impact of the COVID-19 pandemic on online learning in higher education: a bibliometric analysis. Front. Educ . 8:1225834. doi: 10.3389/feduc.2023.1225834

Received: 19 May 2023; Accepted: 14 July 2023; Published: 03 August 2023.

Reviewed by:

Copyright © 2023 Aristovnik, Karampelas, Umek and Ravšelj. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Aleksander Aristovnik, [email protected] ; Dejan Ravšelj, [email protected]

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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Open Access

Peer-reviewed

Research Article

COVID-19’s impacts on the scope, effectiveness, and interaction characteristics of online learning: A social network analysis

Roles Data curation, Formal analysis, Methodology, Writing – review & editing

¶ ‡ JZ and YD are contributed equally to this work as first authors.

Affiliation School of Educational Information Technology, South China Normal University, Guangzhou, Guangdong, China

Roles Data curation, Formal analysis, Methodology, Writing – original draft

Affiliations School of Educational Information Technology, South China Normal University, Guangzhou, Guangdong, China, Hangzhou Zhongce Vocational School Qiantang, Hangzhou, Zhejiang, China

Roles Data curation, Writing – original draft

Roles Data curation

Roles Writing – original draft

Affiliation Faculty of Education, Shenzhen University, Shenzhen, Guangdong, China

Roles Conceptualization, Supervision, Writing – review & editing

* E-mail: [email protected] (JH); [email protected] (YZ)

Junyi Zhang,
Yigang Ding,
Xinru Yang,
Jinping Zhong,
XinXin Qiu,
Zhishan Zou,
Yujie Xu,
Xiunan Jin,
Xiaomin Wu,

Published: August 23, 2022
https://doi.org/10.1371/journal.pone.0273016
Reader Comments

The COVID-19 outbreak brought online learning to the forefront of education. Scholars have conducted many studies on online learning during the pandemic, but only a few have performed quantitative comparative analyses of students’ online learning behavior before and after the outbreak. We collected review data from China’s massive open online course platform called icourse.163 and performed social network analysis on 15 courses to explore courses’ interaction characteristics before, during, and after the COVID-19 pan-demic. Specifically, we focused on the following aspects: (1) variations in the scale of online learning amid COVID-19; (2a) the characteristics of online learning interaction during the pandemic; (2b) the characteristics of online learning interaction after the pandemic; and (3) differences in the interaction characteristics of social science courses and natural science courses. Results revealed that only a small number of courses witnessed an uptick in online interaction, suggesting that the pandemic’s role in promoting the scale of courses was not significant. During the pandemic, online learning interaction became more frequent among course network members whose interaction scale increased. After the pandemic, although the scale of interaction declined, online learning interaction became more effective. The scale and level of interaction in Electrodynamics (a natural science course) and Economics (a social science course) both rose during the pan-demic. However, long after the pandemic, the Economics course sustained online interaction whereas interaction in the Electrodynamics course steadily declined. This discrepancy could be due to the unique characteristics of natural science courses and social science courses.

Citation: Zhang J, Ding Y, Yang X, Zhong J, Qiu X, Zou Z, et al. (2022) COVID-19’s impacts on the scope, effectiveness, and interaction characteristics of online learning: A social network analysis. PLoS ONE 17(8): e0273016. https://doi.org/10.1371/journal.pone.0273016

Editor: Heng Luo, Central China Normal University, CHINA

Received: April 20, 2022; Accepted: July 29, 2022; Published: August 23, 2022

Copyright: © 2022 Zhang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: The data underlying the results presented in the study were downloaded from https://www.icourse163.org/ and are now shared fully on Github ( https://github.com/zjyzhangjunyi/dataset-from-icourse163-for-SNA ). These data have no private information and can be used for academic research free of charge.

Funding: The author(s) received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist.

1. Introduction

The development of the mobile internet has spurred rapid advances in online learning, offering novel prospects for teaching and learning and a learning experience completely different from traditional instruction. Online learning harnesses the advantages of network technology and multimedia technology to transcend the boundaries of conventional education [ 1 ]. Online courses have become a popular learning mode owing to their flexibility and openness. During online learning, teachers and students are in different physical locations but interact in multiple ways (e.g., via online forum discussions and asynchronous group discussions). An analysis of online learning therefore calls for attention to students’ participation. Alqurashi [ 2 ] defined interaction in online learning as the process of constructing meaningful information and thought exchanges between more than two people; such interaction typically occurs between teachers and learners, learners and learners, and the course content and learners.

Massive open online courses (MOOCs), a 21st-century teaching mode, have greatly influenced global education. Data released by China’s Ministry of Education in 2020 show that the country ranks first globally in the number and scale of higher education MOOCs. The COVID-19 outbreak has further propelled this learning mode, with universities being urged to leverage MOOCs and other online resource platforms to respond to government’s “School’s Out, But Class’s On” policy [ 3 ]. Besides MOOCs, to reduce in-person gatherings and curb the spread of COVID-19, various online learning methods have since become ubiquitous [ 4 ]. Though Lederman asserted that the COVID-19 outbreak has positioned online learning technologies as the best way for teachers and students to obtain satisfactory learning experiences [ 5 ], it remains unclear whether the COVID-19 pandemic has encouraged interaction in online learning, as interactions between students and others play key roles in academic performance and largely determine the quality of learning experiences [ 6 ]. Similarly, it is also unclear what impact the COVID-19 pandemic has had on the scale of online learning.

Social constructivism paints learning as a social phenomenon. As such, analyzing the social structures or patterns that emerge during the learning process can shed light on learning-based interaction [ 7 ]. Social network analysis helps to explain how a social network, rooted in interactions between learners and their peers, guides individuals’ behavior, emotions, and outcomes. This analytical approach is especially useful for evaluating interactive relationships between network members [ 8 ]. Mohammed cited social network analysis (SNA) as a method that can provide timely information about students, learning communities and interactive networks. SNA has been applied in numerous fields, including education, to identify the number and characteristics of interelement relationships. For example, Lee et al. also used SNA to explore the effects of blogs on peer relationships [ 7 ]. Therefore, adopting SNA to examine interactions in online learning communities during the COVID-19 pandemic can uncover potential issues with this online learning model.

Taking China’s icourse.163 MOOC platform as an example, we chose 15 courses with a large number of participants for SNA, focusing on learners’ interaction characteristics before, during, and after the COVID-19 outbreak. We visually assessed changes in the scale of network interaction before, during, and after the outbreak along with the characteristics of interaction in Gephi. Examining students’ interactions in different courses revealed distinct interactive network characteristics, the pandemic’s impact on online courses, and relevant suggestions. Findings are expected to promote effective interaction and deep learning among students in addition to serving as a reference for the development of other online learning communities.

2. Literature review and research questions

Interaction is deemed as central to the educational experience and is a major focus of research on online learning. Moore began to study the problem of interaction in distance education as early as 1989. He defined three core types of interaction: student–teacher, student–content, and student–student [ 9 ]. Lear et al. [ 10 ] described an interactivity/ community-process model of distance education: they specifically discussed the relationships between interactivity, community awareness, and engaging learners and found interactivity and community awareness to be correlated with learner engagement. Zulfikar et al. [ 11 ] suggested that discussions initiated by the students encourage more students’ engagement than discussions initiated by the instructors. It is most important to afford learners opportunities to interact purposefully with teachers, and improving the quality of learner interaction is crucial to fostering profound learning [ 12 ]. Interaction is an important way for learners to communicate and share information, and a key factor in the quality of online learning [ 13 ].

Timely feedback is the main component of online learning interaction. Woo and Reeves discovered that students often become frustrated when they fail to receive prompt feedback [ 14 ]. Shelley et al. conducted a three-year study of graduate and undergraduate students’ satisfaction with online learning at universities and found that interaction with educators and students is the main factor affecting satisfaction [ 15 ]. Teachers therefore need to provide students with scoring justification, support, and constructive criticism during online learning. Some researchers examined online learning during the COVID-19 pandemic. They found that most students preferred face-to-face learning rather than online learning due to obstacles faced online, such as a lack of motivation, limited teacher-student interaction, and a sense of isolation when learning in different times and spaces [ 16 , 17 ]. However, it can be reduced by enhancing the online interaction between teachers and students [ 18 ].

Research showed that interactions contributed to maintaining students’ motivation to continue learning [ 19 ]. Baber argued that interaction played a key role in students’ academic performance and influenced the quality of the online learning experience [ 20 ]. Hodges et al. maintained that well-designed online instruction can lead to unique teaching experiences [ 21 ]. Banna et al. mentioned that using discussion boards, chat sessions, blogs, wikis, and other tools could promote student interaction and improve participation in online courses [ 22 ]. During the COVID-19 pandemic, Mahmood proposed a series of teaching strategies suitable for distance learning to improve its effectiveness [ 23 ]. Lapitan et al. devised an online strategy to ease the transition from traditional face-to-face instruction to online learning [ 24 ]. The preceding discussion suggests that online learning goes beyond simply providing learning resources; teachers should ideally design real-life activities to give learners more opportunities to participate.

As mentioned, COVID-19 has driven many scholars to explore the online learning environment. However, most have ignored the uniqueness of online learning during this time and have rarely compared pre- and post-pandemic online learning interaction. Taking China’s icourse.163 MOOC platform as an example, we chose 15 courses with a large number of participants for SNA, centering on student interaction before and after the pandemic. Gephi was used to visually analyze changes in the scale and characteristics of network interaction. The following questions were of particular interest:

(1) Can the COVID-19 pandemic promote the expansion of online learning?
(2a) What are the characteristics of online learning interaction during the pandemic?
(2b) What are the characteristics of online learning interaction after the pandemic?
(3) How do interaction characteristics differ between social science courses and natural science courses?

3. Methodology

3.1 research context.

We selected several courses with a large number of participants and extensive online interaction among hundreds of courses on the icourse.163 MOOC platform. These courses had been offered on the platform for at least three semesters, covering three periods (i.e., before, during, and after the COVID-19 outbreak). To eliminate the effects of shifts in irrelevant variables (e.g., course teaching activities), we chose several courses with similar teaching activities and compared them on multiple dimensions. All course content was taught online. The teachers of each course posted discussion threads related to learning topics; students were expected to reply via comments. Learners could exchange ideas freely in their responses in addition to asking questions and sharing their learning experiences. Teachers could answer students’ questions as well. Conversations in the comment area could partly compensate for a relative absence of online classroom interaction. Teacher–student interaction is conducive to the formation of a social network structure and enabled us to examine teachers’ and students’ learning behavior through SNA. The comment areas in these courses were intended for learners to construct knowledge via reciprocal communication. Meanwhile, by answering students’ questions, teachers could encourage them to reflect on their learning progress. These courses’ successive terms also spanned several phases of COVID-19, allowing us to ascertain the pandemic’s impact on online learning.

3.2 Data collection and preprocessing

To avoid interference from invalid or unclear data, the following criteria were applied to select representative courses: (1) generality (i.e., public courses and professional courses were chosen from different schools across China); (2) time validity (i.e., courses were held before during, and after the pandemic); and (3) notability (i.e., each course had at least 2,000 participants). We ultimately chose 15 courses across the social sciences and natural sciences (see Table 1 ). The coding is used to represent the course name.

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https://doi.org/10.1371/journal.pone.0273016.t001

To discern courses’ evolution during the pandemic, we gathered data on three terms before, during, and after the COVID-19 outbreak in addition to obtaining data from two terms completed well before the pandemic and long after. Our final dataset comprised five sets of interactive data. Finally, we collected about 120,000 comments for SNA. Because each course had a different start time—in line with fluctuations in the number of confirmed COVID-19 cases in China and the opening dates of most colleges and universities—we divided our sample into five phases: well before the pandemic (Phase I); before the pandemic (Phase Ⅱ); during the pandemic (Phase Ⅲ); after the pandemic (Phase Ⅳ); and long after the pandemic (Phase Ⅴ). We sought to preserve consistent time spans to balance the amount of data in each period ( Fig 1 ).

https://doi.org/10.1371/journal.pone.0273016.g001

3.3 Instrumentation

Participants’ comments and “thumbs-up” behavior data were converted into a network structure and compared using social network analysis (SNA). Network analysis, according to M’Chirgui, is an effective tool for clarifying network relationships by employing sophisticated techniques [ 25 ]. Specifically, SNA can help explain the underlying relationships among team members and provide a better understanding of their internal processes. Yang and Tang used SNA to discuss the relationship between team structure and team performance [ 26 ]. Golbeck argued that SNA could improve the understanding of students’ learning processes and reveal learners’ and teachers’ role dynamics [ 27 ].

To analyze Question (1), the number of nodes and diameter in the generated network were deemed as indicators of changes in network size. Social networks are typically represented as graphs with nodes and degrees, and node count indicates the sample size [ 15 ]. Wellman et al. proposed that the larger the network scale, the greater the number of network members providing emotional support, goods, services, and companionship [ 28 ]. Jan’s study measured the network size by counting the nodes which represented students, lecturers, and tutors [ 29 ]. Similarly, network nodes in the present study indicated how many learners and teachers participated in the course, with more nodes indicating more participants. Furthermore, we investigated the network diameter, a structural feature of social networks, which is a common metric for measuring network size in SNA [ 30 ]. The network diameter refers to the longest path between any two nodes in the network. There has been evidence that a larger network diameter leads to greater spread of behavior [ 31 ]. Likewise, Gašević et al. found that larger networks were more likely to spread innovative ideas about educational technology when analyzing MOOC-related research citations [ 32 ]. Therefore, we employed node count and network diameter to measure the network’s spatial size and further explore the expansion characteristic of online courses. Brief introduction of these indicators can be summarized in Table 2 .

https://doi.org/10.1371/journal.pone.0273016.t002

To address Question (2), a list of interactive analysis metrics in SNA were introduced to scrutinize learners’ interaction characteristics in online learning during and after the pandemic, as shown below:

(1) The average degree reflects the density of the network by calculating the average number of connections for each node. As Rong and Xu suggested, the average degree of a network indicates how active its participants are [ 33 ]. According to Hu, a higher average degree implies that more students are interacting directly with each other in a learning context [ 34 ]. The present study inherited the concept of the average degree from these previous studies: the higher the average degree, the more frequent the interaction between individuals in the network.
(2) Essentially, a weighted average degree in a network is calculated by multiplying each degree by its respective weight, and then taking the average. Bydžovská took the strength of the relationship into account when determining the weighted average degree [ 35 ]. By calculating friendship’s weighted value, Maroulis assessed peer achievement within a small-school reform [ 36 ]. Accordingly, we considered the number of interactions as the weight of the degree, with a higher average degree indicating more active interaction among learners.
(3) Network density is the ratio between actual connections and potential connections in a network. The more connections group members have with each other, the higher the network density. In SNA, network density is similar to group cohesion, i.e., a network of more strong relationships is more cohesive [ 37 ]. Network density also reflects how much all members are connected together [ 38 ]. Therefore, we adopted network density to indicate the closeness among network members. Higher network density indicates more frequent interaction and closer communication among students.
(4) Clustering coefficient describes local network attributes and indicates that two nodes in the network could be connected through adjacent nodes. The clustering coefficient measures users’ tendency to gather (cluster) with others in the network: the higher the clustering coefficient, the more frequently users communicate with other group members. We regarded this indicator as a reflection of the cohesiveness of the group [ 39 ].
(5) In a network, the average path length is the average number of steps along the shortest paths between any two nodes. Oliveres has observed that when an average path length is small, the route from one node to another is shorter when graphed [ 40 ]. This is especially true in educational settings where students tend to become closer friends. So we consider that the smaller the average path length, the greater the possibility of interaction between individuals in the network.
(6) A network with a large number of nodes, but whose average path length is surprisingly small, is known as the small-world effect [ 41 ]. A higher clustering coefficient and shorter average path length are important indicators of a small-world network: a shorter average path length enables the network to spread information faster and more accurately; a higher clustering coefficient can promote frequent knowledge exchange within the group while boosting the timeliness and accuracy of knowledge dissemination [ 42 ]. Brief introduction of these indicators can be summarized in Table 3 .

https://doi.org/10.1371/journal.pone.0273016.t003

To analyze Question 3, we used the concept of closeness centrality, which determines how close a vertex is to others in the network. As Opsahl et al. explained, closeness centrality reveals how closely actors are coupled with their entire social network [ 43 ]. In order to analyze social network-based engineering education, Putnik et al. examined closeness centrality and found that it was significantly correlated with grades [ 38 ]. We used closeness centrality to measure the position of an individual in the network. Brief introduction of these indicators can be summarized in Table 4 .

https://doi.org/10.1371/journal.pone.0273016.t004

3.4 Ethics statement

This study was approved by the Academic Committee Office (ACO) of South China Normal University ( http://fzghb.scnu.edu.cn/ ), Guangzhou, China. Research data were collected from the open platform and analyzed anonymously. There are thus no privacy issues involved in this study.

4.1 COVID-19’s role in promoting the scale of online courses was not as important as expected

As shown in Fig 2 , the number of course participants and nodes are closely correlated with the pandemic’s trajectory. Because the number of participants in each course varied widely, we normalized the number of participants and nodes to more conveniently visualize course trends. Fig 2 depicts changes in the chosen courses’ number of participants and nodes before the pandemic (Phase II), during the pandemic (Phase III), and after the pandemic (Phase IV). The number of participants in most courses during the pandemic exceeded those before and after the pandemic. But the number of people who participate in interaction in some courses did not increase.

https://doi.org/10.1371/journal.pone.0273016.g002

In order to better analyze the trend of interaction scale in online courses before, during, and after the pandemic, the selected courses were categorized according to their scale change. When the number of participants increased (decreased) beyond 20% (statistical experience) and the diameter also increased (decreased), the course scale was determined to have increased (decreased); otherwise, no significant change was identified in the course’s interaction scale. Courses were subsequently divided into three categories: increased interaction scale, decreased interaction scale, and no significant change. Results appear in Table 5 .

https://doi.org/10.1371/journal.pone.0273016.t005

From before the pandemic until it broke out, the interaction scale of five courses increased, accounting for 33.3% of the full sample; one course’s interaction scale declined, accounting for 6.7%. The interaction scale of nine courses decreased, accounting for 60%. The pandemic’s role in promoting online courses thus was not as important as anticipated, and most courses’ interaction scale did not change significantly throughout.

No courses displayed growing interaction scale after the pandemic: the interaction scale of nine courses fell, accounting for 60%; and the interaction scale of six courses did not shift significantly, accounting for 40%. Courses with an increased scale of interaction during the pandemic did not maintain an upward trend. On the contrary, the improvement in the pandemic caused learners’ enthusiasm for online learning to wane. We next analyzed several interaction metrics to further explore course interaction during different pandemic periods.

4.2 Characteristics of online learning interaction amid COVID-19

4.2.1 during the covid-19 pandemic, online learning interaction in some courses became more active..

Changes in course indicators with the growing interaction scale during the pandemic are presented in Fig 3 , including SS5, SS6, NS1, NS3, and NS8. The horizontal ordinate indicates the number of courses, with red color representing the rise of the indicator value on the vertical ordinate and blue representing the decline.

https://doi.org/10.1371/journal.pone.0273016.g003

Specifically: (1) The average degree and weighted average degree of the five course networks demonstrated an upward trend. The emergence of the pandemic promoted students’ enthusiasm; learners were more active in the interactive network. (2) Fig 3 shows that 3 courses had increased network density and 2 courses had decreased. The higher the network density, the more communication within the team. Even though the pandemic accelerated the interaction scale and frequency, the tightness between learners in some courses did not improve. (3) The clustering coefficient of social science courses rose whereas the clustering coefficient and small-world property of natural science courses fell. The higher the clustering coefficient and the small-world property, the better the relationship between adjacent nodes and the higher the cohesion [ 39 ]. (4) Most courses’ average path length increased as the interaction scale increased. However, when the average path length grew, adverse effects could manifest: communication between learners might be limited to a small group without multi-directional interaction.

When the pandemic emerged, the only declining network scale belonged to a natural science course (NS2). The change in each course index is pictured in Fig 4 . The abscissa indicates the size of the value, with larger values to the right. The red dot indicates the index value before the pandemic; the blue dot indicates its value during the pandemic. If the blue dot is to the right of the red dot, then the value of the index increased; otherwise, the index value declined. Only the weighted average degree of the course network increased. The average degree, network density decreased, indicating that network members were not active and that learners’ interaction degree and communication frequency lessened. Despite reduced learner interaction, the average path length was small and the connectivity between learners was adequate.

https://doi.org/10.1371/journal.pone.0273016.g004

4.2.2 After the COVID-19 pandemic, the scale decreased rapidly, but most course interaction was more effective.

Fig 5 shows the changes in various courses’ interaction indicators after the pandemic, including SS1, SS2, SS3, SS6, SS7, NS2, NS3, NS7, and NS8.

https://doi.org/10.1371/journal.pone.0273016.g005

Specifically: (1) The average degree and weighted average degree of most course networks decreased. The scope and intensity of interaction among network members declined rapidly, as did learners’ enthusiasm for communication. (2) The network density of seven courses also fell, indicating weaker connections between learners in most courses. (3) In addition, the clustering coefficient and small-world property of most course networks decreased, suggesting little possibility of small groups in the network. The scope of interaction between learners was not limited to a specific space, and the interaction objects had no significant tendencies. (4) Although the scale of course interaction became smaller in this phase, the average path length of members’ social networks shortened in nine courses. Its shorter average path length would expedite the spread of information within the network as well as communication and sharing among network members.

Fig 6 displays the evolution of course interaction indicators without significant changes in interaction scale after the pandemic, including SS4, SS5, NS1, NS4, NS5, and NS6.

https://doi.org/10.1371/journal.pone.0273016.g006

Specifically: (1) Some course members’ social networks exhibited an increase in the average and weighted average. In these cases, even though the course network’s scale did not continue to increase, communication among network members rose and interaction became more frequent and deeper than before. (2) Network density and average path length are indicators of social network density. The greater the network density, the denser the social network; the shorter the average path length, the more concentrated the communication among network members. However, at this phase, the average path length and network density in most courses had increased. Yet the network density remained small despite having risen ( Table 6 ). Even with more frequent learner interaction, connections remained distant and the social network was comparatively sparse.

https://doi.org/10.1371/journal.pone.0273016.t006

In summary, the scale of interaction did not change significantly overall. Nonetheless, some course members’ frequency and extent of interaction increased, and the relationships between network members became closer as well. In the study, we found it interesting that the interaction scale of Economics (a social science course) course and Electrodynamics (a natural science course) course expanded rapidly during the pandemic and retained their interaction scale thereafter. We next assessed these two courses to determine whether their level of interaction persisted after the pandemic.

4.3 Analyses of natural science courses and social science courses

4.3.1 analyses of the interaction characteristics of economics and electrodynamics..

Economics and Electrodynamics are social science courses and natural science courses, respectively. Members’ interaction within these courses was similar: the interaction scale increased significantly when COVID-19 broke out (Phase Ⅲ), and no significant changes emerged after the pandemic (Phase Ⅴ). We hence focused on course interaction long after the outbreak (Phase V) and compared changes across multiple indicators, as listed in Table 7 .

https://doi.org/10.1371/journal.pone.0273016.t007

As the pandemic continued to improve, the number of participants and the diameter long after the outbreak (Phase V) each declined for Economics compared with after the pandemic (Phase IV). The interaction scale decreased, but the interaction between learners was much deeper. Specifically: (1) The weighted average degree, network density, clustering coefficient, and small-world property each reflected upward trends. The pandemic therefore exerted a strong impact on this course. Interaction was well maintained even after the pandemic. The smaller network scale promoted members’ interaction and communication. (2) Compared with after the pandemic (Phase IV), members’ network density increased significantly, showing that relationships between learners were closer and that cohesion was improving. (3) At the same time, as the clustering coefficient and small-world property grew, network members demonstrated strong small-group characteristics: the communication between them was deepening and their enthusiasm for interaction was higher. (4) Long after the COVID-19 outbreak (Phase V), the average path length was reduced compared with previous terms, knowledge flowed more quickly among network members, and the degree of interaction gradually deepened.

The average degree, weighted average degree, network density, clustering coefficient, and small-world property of Electrodynamics all decreased long after the COVID-19 outbreak (Phase V) and were lower than during the outbreak (Phase Ⅲ). The level of learner interaction therefore gradually declined long after the outbreak (Phase V), and connections between learners were no longer active. Although the pandemic increased course members’ extent of interaction, this rise was merely temporary: students’ enthusiasm for learning waned rapidly and their interaction decreased after the pandemic (Phase IV). To further analyze the interaction characteristics of course members in Economics and Electrodynamics, we evaluated the closeness centrality of their social networks, as shown in section 4.3.2.

4.3.2 Analysis of the closeness centrality of Economics and Electrodynamics.

The change in the closeness centrality of social networks in Economics was small, and no sharp upward trend appeared during the pandemic outbreak, as shown in Fig 7 . The emergence of COVID-19 apparently fostered learners’ interaction in Economics albeit without a significant impact. The closeness centrality changed in Electrodynamics varied from that of Economics: upon the COVID-19 outbreak, closeness centrality was significantly different from other semesters. Communication between learners was closer and interaction was more effective. Electrodynamics course members’ social network proximity decreased rapidly after the pandemic. Learners’ communication lessened. In general, Economics course showed better interaction before the outbreak and was less affected by the pandemic; Electrodynamics course was more affected by the pandemic and showed different interaction characteristics at different periods of the pandemic.

(Note: "****" indicates the significant distinction in closeness centrality between the two periods, otherwise no significant distinction).

https://doi.org/10.1371/journal.pone.0273016.g007

5. Discussion

We referred to discussion forums from several courses on the icourse.163 MOOC platform to compare online learning before, during, and after the COVID-19 pandemic via SNA and to delineate the pandemic’s effects on online courses. Only 33.3% of courses in our sample increased in terms of interaction during the pandemic; the scale of interaction did not rise in any courses thereafter. When the courses scale rose, the scope and frequency of interaction showed upward trends during the pandemic; and the clustering coefficient of natural science courses and social science courses differed: the coefficient for social science courses tended to rise whereas that for natural science courses generally declined. When the pandemic broke out, the interaction scale of a single natural science course decreased along with its interaction scope and frequency. The amount of interaction in most courses shrank rapidly during the pandemic and network members were not as active as they had been before. However, after the pandemic, some courses saw declining interaction but greater communication between members; interaction also became more frequent and deeper than before.

5.1 During the COVID-19 pandemic, the scale of interaction increased in only a few courses

The pandemic outbreak led to a rapid increase in the number of participants in most courses; however, the change in network scale was not significant. The scale of online interaction expanded swiftly in only a few courses; in others, the scale either did not change significantly or displayed a downward trend. After the pandemic, the interaction scale in most courses decreased quickly; the same pattern applied to communication between network members. Learners’ enthusiasm for online interaction reduced as the circumstances of the pandemic improved—potentially because, during the pandemic, China’s Ministry of Education declared “School’s Out, But Class’s On” policy. Major colleges and universities were encouraged to use the Internet and informational resources to provide learning support, hence the sudden increase in the number of participants and interaction in online courses [ 46 ]. After the pandemic, students’ enthusiasm for online learning gradually weakened, presumably due to easing of the pandemic [ 47 ]. More activities also transitioned from online to offline, which tempered learners’ online discussion. Research has shown that long-term online learning can even bore students [ 48 ].

Most courses’ interaction scale decreased significantly after the pandemic. First, teachers and students occupied separate spaces during the outbreak, had few opportunities for mutual cooperation and friendship, and lacked a sense of belonging [ 49 ]. Students’ enthusiasm for learning dissipated over time [ 50 ]. Second, some teachers were especially concerned about adapting in-person instructional materials for digital platforms; their pedagogical methods were ineffective, and they did not provide learning activities germane to student interaction [ 51 ]. Third, although teachers and students in remote areas were actively engaged in online learning, some students could not continue to participate in distance learning due to inadequate technology later in the outbreak [ 52 ].

5.2 Characteristics of online learning interaction during and after the COVID-19 pandemic

5.2.1 during the covid-19 pandemic, online interaction in most courses did not change significantly..

The interaction scale of only a few courses increased during the pandemic. The interaction scope and frequency of these courses climbed as well. Yet even as the degree of network interaction rose, course network density did not expand in all cases. The pandemic sparked a surge in the number of online learners and a rapid increase in network scale, but students found it difficult to interact with all learners. Yau pointed out that a greater network scale did not enrich the range of interaction between individuals; rather, the number of individuals who could interact directly was limited [ 53 ]. The internet facilitates interpersonal communication. However, not everyone has the time or ability to establish close ties with others [ 54 ].

In addition, social science courses and natural science courses in our sample revealed disparate trends in this regard: the clustering coefficient of social science courses increased and that of natural science courses decreased. Social science courses usually employ learning approaches distinct from those in natural science courses [ 55 ]. Social science courses emphasize critical and innovative thinking along with personal expression [ 56 ]. Natural science courses focus on practical skills, methods, and principles [ 57 ]. Therefore, the content of social science courses can spur large-scale discussion among learners. Some course evaluations indicated that the course content design was suboptimal as well: teachers paid close attention to knowledge transmission and much less to piquing students’ interest in learning. In addition, the thread topics that teachers posted were scarcely diversified and teachers’ questions lacked openness. These attributes could not spark active discussion among learners.

5.2.2 Online learning interaction declined after the COVID-19 pandemic.

Most courses’ interaction scale and intensity decreased rapidly after the pandemic, but some did not change. Courses with a larger network scale did not continue to expand after the outbreak, and students’ enthusiasm for learning paled. The pandemic’s reduced severity also influenced the number of participants in online courses. Meanwhile, restored school order moved many learning activities from virtual to in-person spaces. Face-to-face learning has gradually replaced online learning, resulting in lower enrollment and less interaction in online courses. Prolonged online courses could have also led students to feel lonely and to lack a sense of belonging [ 58 ].

The scale of interaction in some courses did not change substantially after the pandemic yet learners’ connections became tighter. We hence recommend that teachers seize pandemic-related opportunities to design suitable activities. Additionally, instructors should promote student-teacher and student-student interaction, encourage students to actively participate online, and generally intensify the impact of online learning.

5.3 What are the characteristics of interaction in social science courses and natural science courses?

The level of interaction in Economics (a social science course) was significantly higher than that in Electrodynamics (a natural science course), and the small-world property in Economics increased as well. To boost online courses’ learning-related impacts, teachers can divide groups of learners based on the clustering coefficient and the average path length. Small groups of students may benefit teachers in several ways: to participate actively in activities intended to expand students’ knowledge, and to serve as key actors in these small groups. Cultivating students’ keenness to participate in class activities and self-management can also help teachers guide learner interaction and foster deep knowledge construction.

As evidenced by comments posted in the Electrodynamics course, we observed less interaction between students. Teachers also rarely urged students to contribute to conversations. These trends may have arisen because teachers and students were in different spaces. Teachers might have struggled to discern students’ interaction status. Teachers could also have failed to intervene in time, to design online learning activities that piqued learners’ interest, and to employ sound interactive theme planning and guidance. Teachers are often active in traditional classroom settings. Their roles are comparatively weakened online, such that they possess less control over instruction [ 59 ]. Online instruction also requires a stronger hand in learning: teachers should play a leading role in regulating network members’ interactive communication [ 60 ]. Teachers can guide learners to participate, help learners establish social networks, and heighten students’ interest in learning [ 61 ]. Teachers should attend to core members in online learning while also considering edge members; by doing so, all network members can be driven to share their knowledge and become more engaged. Finally, teachers and assistant teachers should help learners develop knowledge, exchange topic-related ideas, pose relevant questions during course discussions, and craft activities that enable learners to interact online [ 62 ]. These tactics can improve the effectiveness of online learning.

As described, network members displayed distinct interaction behavior in Economics and Electrodynamics courses. First, these courses varied in their difficulty: the social science course seemed easier to understand and focused on divergent thinking. Learners were often willing to express their views in comments and to ponder others’ perspectives [ 63 ]. The natural science course seemed more demanding and was oriented around logical thinking and skills [ 64 ]. Second, courses’ content differed. In general, social science courses favor the acquisition of declarative knowledge and creative knowledge compared with natural science courses. Social science courses also entertain open questions [ 65 ]. Natural science courses revolve around principle knowledge, strategic knowledge, and transfer knowledge [ 66 ]. Problems in these courses are normally more complicated than those in social science courses. Third, the indicators affecting students’ attitudes toward learning were unique. Guo et al. discovered that “teacher feedback” most strongly influenced students’ attitudes towards learning social science courses but had less impact on students in natural science courses [ 67 ]. Therefore, learners in social science courses likely expect more feedback from teachers and greater interaction with others.

6. Conclusion and future work

Our findings show that the network interaction scale of some online courses expanded during the COVID-19 pandemic. The network scale of most courses did not change significantly, demonstrating that the pandemic did not notably alter the scale of course interaction. Online learning interaction among course network members whose interaction scale increased also became more frequent during the pandemic. Once the outbreak was under control, although the scale of interaction declined, the level and scope of some courses’ interactive networks continued to rise; interaction was thus particularly effective in these cases. Overall, the pandemic appeared to have a relatively positive impact on online learning interaction. We considered a pair of courses in detail and found that Economics (a social science course) fared much better than Electrodynamics (a natural science course) in classroom interaction; learners were more willing to partake in-class activities, perhaps due to these courses’ unique characteristics. Brint et al. also came to similar conclusions [ 57 ].

This study was intended to be rigorous. Even so, several constraints can be addressed in future work. The first limitation involves our sample: we focused on a select set of courses hosted on China’s icourse.163 MOOC platform. Future studies should involve an expansive collection of courses to provide a more holistic understanding of how the pandemic has influenced online interaction. Second, we only explored the interactive relationship between learners and did not analyze interactive content. More in-depth content analysis should be carried out in subsequent research. All in all, the emergence of COVID-19 has provided a new path for online learning and has reshaped the distance learning landscape. To cope with associated challenges, educational practitioners will need to continue innovating in online instructional design, strengthen related pedagogy, optimize online learning conditions, and bolster teachers’ and students’ competence in online learning.

View Article
Google Scholar
PubMed/NCBI
30. Serrat O. Social network analysis. Knowledge solutions: Springer; 2017. p. 39–43. https://doi.org/10.1007/978-981-10-0983-9_9
33. Rong Y, Xu E, editors. Strategies for the Management of the Government Affairs Microblogs in China Based on the SNA of Fifty Government Affairs Microblogs in Beijing. 14th International Conference on Service Systems and Service Management 2017.
34. Hu X, Chu S, editors. A comparison on using social media in a professional experience course. International Conference on Social Media and Society; 2013.
35. Bydžovská H. A Comparative Analysis of Techniques for Predicting Student Performance. Proceedings of the 9th International Conference on Educational Data Mining; Raleigh, NC, USA: International Educational Data Mining Society2016. p. 306–311.
40. Olivares D, Adesope O, Hundhausen C, et al., editors. Using social network analysis to measure the effect of learning analytics in computing education. 19th IEEE International Conference on Advanced Learning Technologies 2019.
41. Travers J, Milgram S. An experimental study of the small world problem. Social Networks: Elsevier; 1977. p. 179–197. https://doi.org/10.1016/B978-0-12-442450-0.50018–3
43. Okamoto K, Chen W, Li X-Y, editors. Ranking of closeness centrality for large-scale social networks. International workshop on frontiers in algorithmics; 2008; Springer, Berlin, Heidelberg: Springer.
47. Ding Y, Yang X, Zheng Y, editors. COVID-19’s Effects on the Scope, Effectiveness, and Roles of Teachers in Online Learning Based on Social Network Analysis: A Case Study. International Conference on Blended Learning; 2021: Springer.
64. Boys C, Brennan J., Henkel M., Kirkland J., Kogan M., Youl P. Higher Education and Preparation for Work. Jessica Kingsley Publishers. 1988. https://doi.org/10.1080/03075079612331381467

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Academic performance under COVID-19: The role of online learning readiness and emotional competence

1 University of Alabama, Tuscaloosa, AL 35487 USA

Wenjing Guo

2 Beijing Normal University, Beijing, China

3 Dalian Neusoft University of Information, Dalian, China

Associated Data

The authors do not have permission to share the data used in this study.

The COVID-19 pandemic caused school closures and social isolation, which created both learning and emotional challenges for adolescents. Schools worked hard to move classes online, but less attention was paid to whether students were cognitively and emotionally ready to learn effectively in a virtual environment. This study focused on online learning readiness and emotional competence as key constructs to investigate their implications for students’ academic performance during the COVID-19 period. Two groups of students participated in this study, with 1,316 high school students ( Mean age = 16.32, SD = 0.63) representing adolescents and 668 college students ( Mean age = 20.20, SD = 1.43) representing young adults. Structural equation modeling was conducted to explore the associations among online learning readiness, emotional competence, and online academic performance during COVID-19 after controlling for pre–COVID-19 academic performance. The results showed that, for high school students, both online learning readiness and emotional competence were positively associated with online academic performance during COVID-19. However, for college students, only online learning readiness showed a significant positive relationship with online academic performance during COVID-19. These results demonstrated that being ready to study online and having high emotional competence could make adolescents more resilient toward COVID-19–related challenges and help them learn more effectively online. This study also highlighted different patterns of associations among cognitive factors, emotional factors, and online academic performance during COVID-19 in adolescence and young adulthood. Developmental implications were also discussed.

COVID-19, as a public health crisis, stimulated a subsequent education crisis in which the existing achievement gap, learning loss, and dropout rate were exacerbated due to school closures (Sahu, 2020 ; United Nations, 2020 ). To prevent COVID-19 transmission, educational institutions worldwide made massive efforts to shift from in-person to online teaching (Basilaia & Kvavadze, 2020 ; Chen et al., 2020 ; Daniels et al., 2021 ; Subedi et al., 2020 ). However, little is known about whether students were cognitively and emotionally ready to learn effectively online at the time of transition.

COVID-19 created learning challenges caused by changes in educational platforms, especially for adolescents. Adolescence is a time when peer influences expand (Knoll et al., 2015 ; Knoll et al., 2016 ). With the dramatic changes in adolescents’ “social brain,” these students have a stronger desire for social interaction and are more sensitive to social isolation (Blakemore, 2008 ; Steinberg, 2005 ; Yurgelun-Todd, 2007 ). Social interactions with teachers, peers, and others are crucial elements in adolescents’ learning experiences (Perret-Clermont et al., 2004 ). Therefore, students struggle to be cognitively engaged in class without the motivation of in-person interactions with teachers and peers during online learning (Kim & Frick, 2011 ; Zembylas et al., 2008 ). Moreover, the new platform delivers information in an entirely different way within a totally different environment (i.e., school vs. home), which requires students to use technology and communicate effectively virtually while resisting distractions in the new environment (Aguilera-Hermida, 2020 ; Chen & Jang, 2010 ; Ferrer et al., 2020 ). In short, learning effectively online was extremely challenging during the pandemic.

In addition, COVID-19–related mental health difficulties, such as loss of relatives, social isolation, and heightened stress and anxiety (Hamza et al., 2020 ; Son et al., 2020 ; Wang et al., 2020 ), made students’ academic lives even more challenging (Grubic et al., 2020 ; Liang et al., 2020 ; Thakur, 2020 ; Zhai & Du, 2020 ; Zhao, 2021 ). As mentioned above, adolescence is a developmental stage characterized by a particularly sensitive “social brain” (Blakemore, 2008 ), and it is a critical period for emotional competence development (Booker & Dunsmore, 2017 ; Trentacosta & Fine, 2010 ). As such, any interpersonal and social-emotional suffering is magnified for adolescents when compared to individuals in other developmental stages. Students during this developmental stage need to have higher emotional competence to cope with emotional distress effectively, allowing them to be more resilient to the challenges of the COVID-19 pandemic and perform better academically (Baba, 2020 ; Bao, 2020 ). Therefore, this study focused on online learning readiness and emotional competence as key constructs to investigate their implications for students’ academic performance during the COVID-19 period.

COVID-19 and online learning readiness

Online learning readiness refers to students’ preparation to learn effectively in an online environment (Demir Kaymak & Horzum, 2013 ; Wei & Chou, 2020 ). Although whether students are ready for the “novice” online learning environment of the COVID-19 pandemic is an ongoing question, some preliminary findings provide insight into this question. Within higher education, according to Chung et al. ( 2020 ), students were generally ready for online learning in Malaysia. However, other researchers claimed that students’ learning readiness was lacking (Widodo et al., 2020 ). In high school settings, students were found to have inadequate digital skills for online learning in Delhi (Bhaumik & Priyadarshini, 2020 ). Conversely, Dwiyanti et al. ( 2020 ) reported that most junior high school students in Indonesia were ready and only needed a few improvements. Considering that each institution, country, and researcher may have different standards of “being ready” for online learning, a more meaningful question is this: How did online learning readiness influence students’ academic performance during the COVID-19 pandemic?

Online learning readiness and academic performance

Facilitating academic success is especially important for adolescents and young adults because academic performance has significant implications for future career development (Negru-Subtirica & Pop, 2016 ; Van der Aar et al., 2019 ). The current pandemic is lowering adolescents’ academic motivation (Aboagye et al., 2020 ), inducing learning loss (Kuhfield & Tarasawa, 2020 ; Turner et al., 2020 ), and ultimately causing lower academic performance (Kuhfeld et al., 2020 ). This phenomenon is partly due to a lack of readiness for online learning. According to the OECD’s Programme in International Student Assessment (PISA), most adolescents from diverse countries (i.e., 15-year-olds in the 79 education systems in the PISA database) were not ready to learn online (Reimers & Schleicher, 2020 ).

Online learning is not purely about having a place or a computer with which to study. More importantly, it requires specific skills and online learning self-efficacy (Smith, 2005 ). Many studies have recognized the importance of students’ motivation in the online learning environment (e.g., Chen & Jang, 2010 ; Khalilzadeh & Khodi, 2021 ). One challenge of online learning readiness research is that researchers have used different constructs, some of which overlap with self-directed learning and motivation (e.g., Cigdem & Ozturk, 2016 ; Pintrich, 2000 ; Zimmerman, 2008 ). Based on previous studies and in an effort to distinguish online learning readiness from self-directed learning and motivation, the current study focused on the three most-used factors in the online learning readiness literature: computer and Internet self-efficacy, learners’ self-control in online contexts, and online communication self-efficacy (Hung et al., 2010 ; Yu, 2018 ).

Studies have indicated that these three online learning readiness factors are associated with students’ online academic performance. Computer and Internet self-efficacy concerns students’ confidence with computer and Internet use (Hatlevik et al., 2018 ; Torkzadeh et al., 2006 ). Having confidence in using Microsoft Office software or conducting Internet research enables online problem-solving, lessens the stress caused by technology, and improves academic performance (Compeau & Higgins, 1995 ; Eastin & LaRose, 2000 ; Tsai & Lin, 2004 ). Learners’ self-control in online contexts refers to students’ ability to avoid distractions from social media (e.g., Facebook or Instagram) and video games and to focus on online courses and assignments (Teng et al., 2014 ; Wang & Beasley, 2002 ). Finally, online communication self-efficacy reflects students’ willingness and confidence in online interactions with instructors and peers to deepen understanding, which benefits their learning outcomes and learning satisfaction (Roper, 2007 ; Yilmaz, 2017 ). Having computer and Internet self-efficacy, self-control in online contexts, and online communication self-efficacy assists students with the transition to the online learning environment (Miao et al., 2020 ). Ultimately, these three factors all contribute to students’ online learning performance.

Overall, online learning readiness has been shown to positively correlate with college students’ academic performance in the online learning environment (Davies & Graff, 2005 ; Lee & Choi, 2013 ; Yu, 2018 ). Moreover, research results have been consistent across studies in diverse college samples (Bernard et al., 2004 ; Joosten & Cusatis, 2020 ). However, before the current pandemic, the majority of online learning readiness studies focused on higher education. More studies are needed to address the role of online learning readiness in high school students’ online academic performance and to determine how to support high school students in preparing for online learning, especially during the COVID-19 pandemic.

COVID-19 and emotional competence

Beyond online learning preparedness (e.g., computer skills or self-control in an online learning environment) for virtual learning during the COVID-19 pandemic, students also need emotional competence to prepare them for the hectic world. Emotional competence is defined as an individual’s ability to express, regulate, and understand emotions (Denham et al., 2015 ; Saarni, 1999 , 2000 ). Special attention needs to be paid to adolescents’ emotional competence during the COVID-19 pandemic for two major reasons. First, emotional competence, as a crucial factor in academic performance (Brackett et al., 2012 ; Oberle et al., 2014 ; Rhoades et al., 2011 ) and effective functioning in adulthood (Kotsou et al., 2011 ; Takšić, 2002 ), are developed through socialization during adolescence (Valiente et al., 2020 ). With the unavoidable social isolation caused by COVID-19, adolescents have been shown to be less aware and less accepting of their own emotions (Hurrell et al., 2017 ; Valiente et al., 2020 ) and to have a harder time regulating their emotions (Casey et al., 2019 ; Cole, 2014 ). Indeed, several early works on COVID-19’s immediate impacts reported an increase in low emotional competence-related mental health issues in adolescents and young adults (e.g., Janssen et al., 2020 ; Orgilés et al., 2020 ; Smirni et al., 2020 ).

Second, there is an urgent need for adolescents to be emotionally competent to deal with the extra emotional distress caused by COVID-19, including the experience of illness, loss of relatives, and financial difficulties during the pandemic (Li et al., 2021 ; Pan, 2020 ; Wathelet et al., 2020 ) as well as feelings of anxiety, depression, and sadness (Imran et al., 2020 ). Having high emotional competence would help students control and regulate their grief, sadness, and stress to cope with the new online learning environment more effectively (Baba, 2020 ; Moroń & Biolik-Moroń, 2020 ).

Emotional competence and academic performance

High emotional competence could not only lessen mental health issues but could also contribute to academic performance in both adolescent and young adult populations (Brackett et al., 2012 ; Harley et al., 2019 ; Parker et al., 2004 ). Low emotional competence is related to increased mental health problems (e.g., Janssen et al., 2020 ; Orgilés et al., 2020 ; Smirni et al., 2020 ), which in turn interfere with academic performance (Dekker et al., 2020 ; Tembo et al., 2017 ). COVID-19 escalated this linkage because adolescents had a harder time regulating emotions due to social relationship changes (Akgül & Atalan Ergin, 2021 ; Mathews et al., 2016 ) and experienced higher levels of emotional distress caused by COVID-19-related issues (Magson et al., 2021 ).

According to recent research, students with a better ability to perceive and regulate emotions had higher online learning readiness levels and were more resistant to online distractions (Engin, 2017 ), so they were more likely to have better academic performance in an online learning setting (Artino Jr & Jones II, 2012 ; Kim & Pekrun, 2014 ). However, most emotional competence studies have been conducted in traditional face-to-face learning settings and focused on specific emotions, so it is necessary to test the role of emotional competence in online settings, especially during the current pandemic. Moreover, emotional competence plays different roles in adolescents’ and young adults’ lives (Hallam et al., 2014 ; Kotsou et al., 2011 ), but few studies have differentiated the roles that emotional competence play in academic performance between adolescence (high school students) and young adulthood (college students). Therefore, more research is needed to address the role that emotional competence plays during the COVID-19 pandemic from a developmental perspective.

The current study

Above all, online learning readiness and emotional competence are critical for understanding adolescents’ academic performance during COVID-19. Given the lack of research on high school students’ online learning readiness and students’ emotional competence in online settings, little is known about whether online learning readiness and emotional competence may influence students’ academic performance differently for high school students (adolescents) and college students (young adults). Therefore, this study aimed to (a) investigate how online learning readiness and emotional competence contribute to students’ academic performance in both high school and college students during COVID-19 and (b) explore whether the pattern of associations would be different in high school students and college students. As mentioned above, college students with better online learning readiness have been shown to have higher online academic performance (e.g., Tsai & Lin, 2004 ; Yilmaz, 2017 ), and in a traditional face-to-face setting, students with higher emotional competence have tended to have better academic performance (e.g., Brackett et al., 2012 ; Harley et al., 2019 ). In aim (a), this study proposed two hypotheses: Hypothesis 1 —Both high school and college students with a higher level of online learning readiness will have better online academic performance during the COVID-19 pandemic; Hypothesis 2 —Both high school and college students with better emotional competence will have higher online academic performance during the COVID-19 pandemic. Without enough evidence in the extant literature for us to make a specific prediction, aim (b) will be examined in an exploratory manner.

Participants and procedure

High school sample.

This study recruited 1,689 first-year students from a high school in northeast China with medium education quality. As recommended by Kline ( 2015 ), the minimum sample-size-to-parameters ratio would be 10:1. In the high school sample, the number of model parameters that required statistical estimates was 99. The sample-size-to-parameters ratio in our study was 17:1, meeting the requirement of above 10:1. A survey was set up on Wen Juan Xing (a Chinese survey engine similar to Qualtrics). The head teacher first sent out the consent form to students’ parents through WeChat. Parents signed the form electronically and returned it to the head teacher. After obtaining consent from parents or guardians, the head teacher sent the survey link to students through WeChat during students’ free time. The survey data were collected over a 2-week period in July 2020. After removing “careless cases” (i.e., the responses from participants who failed the attention check), the final sample consisted of 1,316 first-year high school students (39.1% male, 53.8% female, and 7.1% preferred not to say). We incorporated two attention checking items to avoid careless responses. For example, for this question, please select disagree. Participants who answered both attention checking questions correctly were included in this study. Participants’ ages ranged from 15 to 18 years old ( Mean = 16.32, SD = 0.63); 94.2% identified their race as Han (i.e., the majority in China), and 5.8% identified as minorities.

College student sample

A sample of 1,049 college students was recruited from a 4-year university in northeast China with medium education quality. In the college sample, the number of model parameters that required statistical estimates was 75. The sample-size-to-parameters ratio was 14:1, above the recommended 10:1 (Kline, 2015 ). The same survey on Wen Juan Xing was used to collect data. A university lecturer first sent out the consent form to students or students’ parents or guardians through WeChat (with forms sent to parents/guardians only for those students who were under 18). After receiving the signed consent forms, the university lecturer sent the survey link to students through WeChat during students’ free time. The survey data were collected over a 2-week period in July 2020. After removing careless cases (i.e., the responses from participants who failed the attention check), the final sample consisted of 668 college students (43.3% male, 51.8% female, and 4.9% preferred not to say). Participants’ ages ranged from 17 to 25 years old ( Mean = 20.20, SD = 1.43). Among them, 149 were freshmen, 207 were sophomores, 76 were juniors, and 236 were seniors; 89.2% identified their race as Han (i.e., the majority in China), and 10.8% identified as minorities.

Measurement

Translation.

All questionnaires originally in English (i.e., questionnaires on emotional competence and online learning readiness) were translated into Chinese through translation and back-translation procedures (Beaton et al., 2000 ). Specifically, one Chinese postdoctoral student fluent in English translated the scales to Chinese, and another Chinese university lecturer back-translated all scales to ensure translation accuracy. A bilingual US university faculty member checked both the translated and back-translated scales to further validate the translation. The whole survey included demographic information (e.g., gender, age, race) and questionnaires on emotional competence and online learning readiness.

Emotional competence

Emotional competence was measured by the Short Profile of Emotional Competence (S-PEC), which demonstrated high internal reliability in the original study ( D-G Rho = 0.85; Mikolajczak et al., 2014 ). The S-PEC included five parallel subfactors in both the intrapersonal (10 items) and interpersonal (10 items) dimensions. Each of the five subfactors was assessed by two items. These subfactors were identification (e.g., “When I am touched by something, I immediately know what I feel”), comprehension (e.g., “I do not always understand why I respond in the way I do”), expression (e.g., “I find it difficult to explain my feelings to others even if I want to”), regulation (e.g., “When I am angry, I find it easy to calm myself down”), and utilization (“If I wanted, I could easily make someone feel uneasy”). All items were rated on a scale from 1 = never to 5 = very often . In our study, two items in each subfactor were averaged to create a composite score; a higher value indicated better emotional competence in that specific subfactor. In our samples, both the reliability (Cronbach’s α = 0.71 in the high school sample and 0.76 in the college sample) and the constructive validity (high school sample: χ 2 (25) = 48.12, p = 0.004, CFI = 0.99, TLI = 0.98, RMSEA (90% CI) = 0.03 (0.02–0.04), SRMR = 0.02; college sample: χ 2 (29) = 59.62, p = 0.001, CFI = 0.98, TLI = 0.97, RMSEA (90% CI) = 0.04 (0.03–0.05), SRMR = 0.03) of this translated measure were acceptable.

Apart from the confirmatory factor analysis, to further validate the psychometric properties of this translated instrument, we conducted item response theory analyses, like Alavi et al. ( 2021 ) and Khodi et al. ( 2021 ). Specifically, we applied the polytomous Rasch Rating Scale model (Andrich, 1978 ) to both the high school and college samples. Rasch measurement theory provides a clear and theoretically based framework that allows researchers to evaluate the degree to which the instrument adheres to invariant measurement (Martha et al., 2021 ; Wind et al., 2021 ; Wind & Guo, 2019 ). We used Winsteps software (Linacre, 2016 ) to obtain model-data fit statistics (i.e., infit and outfit MSE ) and the reliability of separation statistics ( Rel ) for students and items. On average, the values of model-data fit statistics were around 1 for both high school students ( M infit MSE = 1.01, SD = 0.73; M outfit MSE = 1.02, SD = 0.72) and college students ( M infit MSE = 1.02, SD = 0.88; M outfit MSE = 1.00, SD = 0.84), and for items, the infit and outfit MSE were also close to 1 (high school sample: M infit MSE = 1.03, SD = 0.28; M outfit MSE = 1.02, SD = 0.26; college sample: M infit MSE = 1.00, SD = 0.27; M outfit MSE = 1.00, SD = 0.26), indicating acceptable fit to the Rasch model. The reliability of the separation statistic for students (high school sample: Rel = 0.86; college sample: Rel = 0.88) suggests that the instrument effectively differentiated students with different levels of emotional competence. Similarly, the reliability of the separation statistic for items (high school sample: Rel = 1.00; college sample: Rel = 1.00) indicates that there were differences in difficulty to endorse each item. We also conducted differential item functioning (DIF) analysis to determine whether the item response differed between high school students and college students while controlling for an estimate of emotional competence. Several researchers (Draba, 1977 ; Wind & Guo, 2019 ; Wright et al., 1976 ) have recommended that absolute logit differences that exceed 0.5 suggest that DIF occurs between two groups. Our results show that the range of differences in Rasch calibrations were from -0.38 logits to 0.43 logits, which indicates that there were no substantively meaningful differences between high school students and college students. In summary, the emotional competence instrument demonstrated acceptable psychometric properties for measuring emotional competence among both high school and college students.

Online learning readiness

Items that directly targeted the online learning environment on the Online Learning Readiness Scale (OLRS; Hung et al., 2010 ) were employed to measure online learning readiness. Specifically, there were three items in each of the following three subscales: computer/Internet self-efficacy (e.g., “I feel confident in my knowledge and skills of how to manage software for online learning,” Cronbach’s α = 0.74), learner control in online contexts (e.g., “I can direct my own learning progress in online courses,” Cronbach’s α = 0.73), and online communication self-efficacy (e.g., “I feel confident in expressing myself [emotions and humor] through text,” Cronbach’s α = 0.87). All items were rated from 1 = strongly disagree to 5 = strongly agree . Three items on each of the subscales were averaged to create a composite score so that a higher value indicated higher levels of online learning readiness on that subscale. In our samples, both reliability (Cronbach’s α ranged from 0.72 to 0.73 in the high school sample and 0.75 to 0.82 in the college sample) and constructive validity (high school sample: χ 2 (19) = 100.04, p < 0.001, CFI = 0.98, TLI = 0.96, RMSEA (90% CI) = 0.06 (0.05–0.07), SRMR = 0.02; college sample: χ 2 (22) = 45.70, p = 0.002, CFI = 0.99, TLI = 0.99, RMSEA (90% CI) = 0.04 (0.02–0.06), SRMR = 0.02) of this translated measure were acceptable.

Apart from the confirmatory factor analysis, to further evaluate the psychometric properties of the translated OLRS, we also conducted Rasch analysis as we did for S-PEC. The results indicate that OLRS exhibited acceptable psychometric properties for measuring both high school and college students’ online learning readiness. Specifically, the average values of model-data fit statistics were around 1 for both groups (high school sample: M infit MSE = 1.00, SD = 0.98, M outfit MSE = 1.00, SD = 0.97; college sample: M infit MSE = 0.96, SD = 1.15; M outfit MSE = 0.97, SD = 1.17) and items (high school sample: M infit MSE = 1.00, SD = 0.19, M outfit MSE = 1.00, SD = 0.20; college sample: M infit MSE = 0.99, SD = 0.19, M outfit MSE = 0.97, SD = 0.20). The reliability of separation statistics for students (high school sample: Rel = 0.86; college sample: Rel = 0.87) and for items (high school sample: Rel = 0.99; college sample: Rel = 0.98) suggest that OLRS can effectively differentiate among individuals with different levels of online learning readiness. DIF analysis demonstrated that there were no substantively meaningful differences between high school students and college students (-0.41 ≤ logit difference ≤ 0.33).

Academic performance

After getting approval from their institutions, consent from students and their parents/guardians (for minor-aged students), we obtained students’ academic performance (indicated by final exam scores) from their teachers in both the high school and the college samples. In the high school sample, we collected students’ final exam scores on Chinese, math, and English—three major disciplines in the Chinese high school education system (the maximum possible score for each discipline was 150). In the college sample, we gathered students’ average final exam scores across all courses they had taken (the maximum possible score was 100). We collected participants’ scores at two time points (T1 and T2) for both samples. T1 was before the COVID-19 pandemic when traditional face-to-face teaching was used, and T2 was during the COVID-19 pandemic when online synchronous teaching was used. Students in both samples had similar online learning experiences. Specifically, the online synchronous teaching adopted Dingding (a Chinese meeting software application like Zoom), and Microsoft Office programs were used for assignments. WeChat (a Chinese messaging app) was utilized for teacher–teacher, teacher–student, student–student, and teacher–parent communication. For the high school sample, data were collected in December 2019 (T1) and July 2020 (T2); for the college sample, data were collected in January 2020 (T1) and June 2020 (T2). Students were assigned a four-digit research ID to confidentially link their final exam scores and the survey results.

Plan of analysis

Data analysis was conducted in Mplus version 8.4 (Muthén & Muthén, 2017 ). In both the high school and college samples, measurement models via confirmatory factor analysis (CFA) were first estimated on the latent constructs of emotional competence, online learning readiness, and academic performance (high school sample only), individually. Specifically, the latent variable of emotional competence was indicated by 10 composite scores—identification, comprehension, expression, regulation, and utilization in both intrapersonal and interpersonal domains. The latent variable of online learning readiness was indicated by three composite scores of computer/Internet self-efficacy, learner control in online contexts, and online communication self-efficacy.

In the high school sample, the latent variable of pre-COVID academic performance was indicated by students’ final exam scores on Chinese, English, and math at T1, and the latent variable of during-COVID academic performance was indicated by these three scores at T2. An overall measurement model including both the T1 and T2 latent constructs of academic performance was conducted after a CFA for each time point. In the college sample, because there was only a single score for each time point, that single score was used as a manifest variable for academic performance at T1 and T2. In each measurement model, correlations between residual variances were added one at a time according to modification indices (Sorbom, 1989 ).

Next, we used structural regression models to examine the association between emotional competence, online learning readiness, and students’ during-COVID academic performance while controlling for their pre-COVID academic performance and the demographic characteristics of age and gender. That is, the T2 academic performance variable was regressed on age, gender, T1 academic performance, emotional competence, and online learning readiness. All predictors were allowed to correlate with each other. This analysis was conducted separately in the high school and college samples.

Both measurement models and structural regression models were estimated using full information maximum likelihood estimation to minimize the bias caused by missingness (Widaman, 2006 ). Overall model fit acceptability was evaluated using the following criteria: the comparative fit index (CFI) value was greater than 0.95, the Tucker-Lewis index (TLI) was greater than 0.90, the root mean square error of approximation (RMSEA) was less than 0.06, and the standardized root mean square residual (SRMR) value was less than 0.08 (Hu & Bentler, 1999 ). Standardized path coefficients were reported in each model.

Last, group invariance tests were conducted across gender groups in both the high school and college samples to indicate whether the overall structural regression model was significantly different by gender. This was done by comparing two multiple group models that did not include gender as a control variable: in the first model, all regression paths were freely estimated across groups; in the second model, all regression paths were constrained to be the same across groups. Changes in the CFI (ΔCFI) were used as a preferred approach for model fit comparison, with ΔCFI equal to or greater than 0.01 indicating a significant change in model fit caused by path constraints (Cheung & Rensvold, 2002 ). This approach was more suitable than the Chi-square difference test for a large sample.

Table Table1 1 includes descriptive statistics and correlation information for the variables used in the structural regression models. The lower panel shows correlations in the high school sample, and the upper panel depicts correlations in the college sample. Overall, variables were correlated in the expected directions in both samples. Moreover, the CFA models for emotional competence, online learning readiness, and academic performance across T1 and T2 all showed a good fit with the data (see Table Table2 2 ).

Correlations, Means, and Standard Deviations

Correlations		1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	16	17
1. ECintra_id		–													.02	.08	-.02	.02
2. ECintra_co			–												-.01	.02	.01	.05
3. ECintra_ex				–							.06				.01	.03	.05	-.01
4. ECintra_re					–						.06				-.01	.05	.01	-.05
5. ECintra_ut				-.05		–				.00	.01	.05	.07		-.05	-.05	-.05	-.02
6. ECinter_id							–				-.01				.01	.03	-.02
7. ECinter_co								–			-.05				.01	.08	-.03	.04
8. ECinter_ex						.04			–		-.01				.01	.03	-.02	.07
9. ECinter_re						.05				–	.07				-.05	.02	.04	.02
10. ECinter_ut			.00		-.02	.03		.04	.04		–					-.09	-.05
11. OL_eff						.05						–			.07		-.02	.00
12. OL_con						-.04							–		.06		.04	.01
13. OL_com														–	-.01	.08	.00	.-.04
14. T1 score			.02	-.03	-.01		-.01	.03	-	-	.00			.02	–		-.01
15. T2 score				.05	.02		.04	.05	.01	-.04	.05					–	.04
16. Age		-.01	.03	.03	-.02		-.01	-.01	-.01	.02	-.05	-.05	.02				–
17. Gender			.04	.02						-.04	-.03	-.05			.04			–
Mean	H.	3.75	3.20	2.75	3.11	3.30	3.34	3.32	3.20	2.99	2.76	3.25	2.94	3.16	95.52	103.92	16.32	1.58
	C.	3.81	3.47	3.15	3.42	3.23	3.55	3.43	3.35	3.19	2.97	3.78	3.59	3.64	76.03	76.00	20.20	1.54
S.D.	H.	0.94	0.81	0.92	1.02	0.71	0.85	0.83	0.88	0.87	0.96	0.94	0.90	0.90	16.64	13.61	0.63	0.49
	C.	0.85	0.78	0.87	0.86	0.64	0.78	0.75	0.77	0.80	0.93	0.83	0.83	0.87	6.75	8.59	1.43	0.50

Note . Statistically significant correlations are bold and underlined ( p < .05). For gender: 1=male, 2=female.

The lower panel presents correlations in the high school sample and the upper panel presents correlations in the college sample.

EC=Emotional Competence, intra=intrapersonal dimension, inter=interpersonal dimension, id=identification, co=comprehension, re=regulation, ut=utilization; OL=Online Learning, eff=computer/internet self-efficacy, con=learner control in online contexts, com=online communication self-efficacy; T1 score=pre-COVID final exam score, T2 score=during-COVID final exam score; H.=high school sample, C.=college sample.

Due to space limit, high school T1 score and T2 score were composite scores (i.e. average score of Chinese, English, and Math at T1 and T2) in this correlation table (but they were latent variables in the formal analyses).

Model fit information for measurement models and structural regression models

	χ (df)	CFI	TLI	RMSEA (90%CI)	SRMR
High School Sample
M1: EC	48.12 (25) **	0.99	0.98	0.03 (0.02-0.04)	0.02
M2: OL	–	–	–	–	–
M3: T1 score	–	–	–	–	–
M4: T2 score	–	–	–	–	–
M5: overall (exclude gender, age)	367.90 (123) 669.25 (153)	0.97	0.96	0.04 (0.03-0.04)	0.04
S: overall structural regression model	367.90 (123) 669.25 (153)	0.94	0.92	0.05 (0.05-0.06)	0.05
College sample
M1: EC	59.62 (29) **	0.98	0.97	0.04 (0.03-0.05)	0.03
M2: OL	–	–	–	–	–
M3: overall (exclude gender, age)	164.08 (73) 192.80 (95)	0.97	0.96	0.04 (0.03-0.05)	0.04
S: overall structural regression model	164.08 (73) 192.80 (95)	0.97	0.96	0.04 (0.03-0.05)	0.04

Note . * p < .05, ** p < .01.

M1-M5=measurement model 1-5, S=structural regression model, EC=Emotional Competence, OL=Online Learning Readiness, T1 score=Pre-COVID Academic Performance, T2 score=During-COVID Academic Performance.

Model fit information of M2-M4 in high school sample and M2 in college sample were not available, since there were only 3 manifest variables loading on 1 latent variable in each model and these models were just identified

In the high school sample, the structural regression model had acceptable model fit, where χ 2 (153) = 669.25, p < 0.01; CFI = 0.94; TLI = 0.92; RMSEA = 0.05 (90%: 0.05–0.06); SRMR = 0.05. All regression paths are listed in Fig. Fig.1 1 (a). Both emotional competence ( β = 0.06, p = .030) and online learning readiness ( β = 0.07, p = .006) were significantly associated with high school students’ during-COVID academic performance, even after accounting for the stability of their academic performance from the pre-COVID to during-COVID periods ( β = 0.78, p < .001) and controlling for the influence of age ( β = - 0.02, p = .489) and gender ( β = 0.07, p = .009).

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The Associations of Emotional Competence, Online Learning Readiness, and Academic Performance. All predictors were correlated with each other. Residuals were allowed to correlated according to modification indices

In the college sample, the structural regression model had good model fit, where χ 2 (95) = 192.80, p < 0.01; CFI = 0.97; TLI = 0.96; RMSEA = 0.04 (90%: 0.03–0.05); SRMR = 0.04. All regression paths are listed in Fig. Fig.1 1 (b). Only online learning readiness ( β = 0.15, p = .003) was significantly associated with college students’ during-COVID academic performance after accounting for the stability of their academic performance from the pre-COVID to during-COVID period ( β = 0.61, p < .001) and controlling for the influence of age ( β = 0.07, p = .061) and gender ( β = 0.08, p = .024). However, unlike the high school group, the association between emotional competence and during-COVID academic performance was not significant for college students ( β = - 0.02, p = .756).

Overall, the pattern of associations among variables was consistent across gender groups in both the high school and college samples, which was indicated by the insignificant change in the overall model fit (high school sample: ΔCFI = .000; college sample: ΔCFI = .002) between the model with constrained regression paths (i.e., constrained model) and the model with freely estimated regression paths (i.e., freely estimated model) across gender groups. This suggests that the association among emotional competence, online learning readiness, and during-COVID academic performance was representative of the whole sample (in the high school sample and college sample) regardless of a participant’s gender.

The present study was designed to evaluate how online learning readiness and emotional competence are related to students’ online academic performance during the COVID-19 pandemic. The results of structural regression models in both the high school and college samples generally supported our hypotheses. Consistent with Hypothesis 1, online learning readiness was associated with academic performance significantly for both high school students and college students (after controlling for their pre-COVID academic performance). However, there were some nuanced differences in the association between emotional competence and academic performance in the two samples. Partially consistent with Hypothesis 2, emotional competence was significantly associated with high school students’ academic performance, but such an association was not significant for college students. This finding also shed light on our second exploratory research question about the potentially different patterns of association among these constructs during adolescence (high school sample) and young adulthood (college sample). The association between online learning readiness and online academic performance was consistent across the two samples, but the association between emotional competence and online academic performance during COVID-19 was different.

The findings for online learning readiness were consistent with previous research (e.g., Cigdem & Ozturk, 2016 ; Horzum et al., 2015 ) and highlighted the vital role of online learning readiness in the high school population. Both high school students and college students who are more ready to learn online had better online learning academic performance. Specifically, high school and college students who have confidence in using Microsoft Office programs, managing software, and using the search engines (e.g., Google and Yahoo) were more likely to have higher academic performance (Tsai & Lin, 2004 ). Moreover, as in previous studies (Roper, 2007 ; Yilmaz, 2017 ), students who could direct their own learning online, avoid online distractions (e.g., instant messages or surfing the Internet), and communicate effectively with peers or instructors online demonstrated stronger academic performance during COVID-19.

All these findings are in line with classical developmental psychology theories, especially Bandura’s ( 1969 , 1977 ) interactive triangle of personal factors, personal behaviors, and environmental factors and Vygotsky’s ( 1978 ) social learning theory. A change in social and learning environment could influence students’ learning significantly, and how well students’ responses fit the environment are key factors of the learning outcome. Online learning and the pandemic are foreign for both high school and college students; the more ready students are, or the more quickly they can adjust to the new environment, the better their learning outcomes will be (Tu, 2002 ).

Developmental differences were identified in the associations between emotional competence and academic performance. The association between emotional competence and during-COVID-19 academic performance in the high school sample confirmed the findings from previous research that high emotional competence could contribute to academic performance (Brackett et al., 2012 ; Garner, 2010 ). Adolescents who could identify, comprehend, regulate, and utilize their own or others’ emotions performed better academically (Brackett et al., 2012 ; Durlak et al., 2011 ; Zins et al., 2007 ). Such findings are consistent with Pekrun’s ( 2000 , 2006 ) control-value theory of achievement emotion, which highlights the emotional arousal in academic settings elicited by academic achievement. Achievement emotion can influence cognitive, motivational, and regulatory processes associated with learning and achievement. Conversely, negative emotions consume energy that is essential for cognition and impair academic performance (Meinhardt & Pekrun, 2003 ). Therefore, adolescents who could better identify and regulate emotion achieved higher grades in the current study.

However, in the college sample, no association was identified between emotional competence and academic performance. This discrepancy in the association pattern between emotional competence and during-COVID-19 online academic performance is likely due to two factors: developmental differences and different measurements of academic performance. Developmentally, adolescents may have a harder time regulating emotions due to brain, body, and social relationship changes (Casey et al., 2019 ; Miller-Slough & Dunsmore, 2016 ), so emotional competence appears to be more critical for adolescents than young adults. The discrepancy might also be caused partially by the different measures of GPA (i.e., high school—Chinese, math, and English total grade; college—a single average score).

The current study has both theoretical and practical implications. The relatively large pooled sample sizes (15–25 years of age) enabled us to make more generalizable statistical inferences about both high school students (adolescents) and college students (young adults), at least in the Chinese student population. Theoretically, this study added to the limited literature on adolescents’ online learning readiness (Tsai & Lin, 2004 ) and replicated prior work in the college population to emphasize the important role online learning readiness plays in online academic performance during young adulthood (e.g., Hung et al., 2010 ; Rafique et al., 2021 ). Moreover, our findings extended previous research on the impact of emotional competence on psychological development outcomes (e.g., Kotsou et al., 2011 ; Valiente et al., 2020 ) to highlight its crucial role in online academic performance, especially for high school students.

Practically, this study informed both high schools and higher education institutions that preparing students to learn online is as essential as preparing the institution to operate online (Habibu et al., 2012 ; Littlejohn & Pegler, 2007 ). Being ready to transition to an online learning environment and having high emotional competence could make adolescents more resilient to COVID-19-related challenges, such as social isolation and learning loss (Shanahan et al., 2020 ). Educational institutions not only need to provide instructions on how to use Microsoft Office software and online searching techniques but should also provide learning strategies like how to avoid online distractions (e.g., social media and video games) and how to communicate effectively with teachers and peers online. Such guidance would be especially beneficial for students who think they are not ready for online learning. Moreover, students’ mental health issues need to be addressed by emotional competence-related interventions, especially for adolescents (Lau & Wu, 2012 ). Schools and universities should consider having interventions and training on emotional competence to promote students’ mental health and help them navigate the volatile, uncertain, complex, and ambiguous world (Hadar et al., 2020 ). Effective strategies of identifying, comprehending, regulating, and utilizing emotions should be offered via online instructions and activities, especially for high school students. Moreover, online counseling should be more accessible for adolescents (O’Connor, 2020 ; Wen et al., 2020 ).

Limitations

This study has some limitations that should be considered when interpreting its results. First, although pre-COVID academic performance has been controlled for from a longitudinal perspective, the directionality of the association between online learning readiness, emotional competence, and online academic performance during the COVID-19 pandemic could not be deduced due to the cross-sectional nature of the current data. The different measures of grade point average across the sample may have contributed to different findings for the groups Second, self-reported data on emotional competence and online learning readiness unavoidably introduced bias into the measurements. Thirdly, this study did not account for demographic control variables such as socioeconomic status, which can be a key factor contributing to students’ access to computers and the Internet or other resources. Moreover, the data collection intervals were different for high school and college students, being 2 months less for the latter group.

Future directions

Future studies that include students in small towns and rural areas will enrich the generalizability of our findings because our samples were predominantly students from cities. Rural or suburban students would likely have less access to online resources or learning resources in general (Lai & Widmar, 2021 ). Moreover, longitudinal research is needed to infer the associational patterns of emotional competence and online learning readiness with academic performance, considering the enduring and emerging nature of emotional competence during adolescence (including young adulthood) and their potential nuanced implications for academic performance trajectory. Regardless, this is one of the first studies, to our knowledge, that simultaneously considered cognitive and emotional factors associated with online academic performance across different developmental stages in adolescence during the COVID-19 pandemic.

Availability of data and material

Declarations.

On behalf of all authors, the corresponding author states that there is no conflict of interest.

Not applicable.

This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Ethics Committee of the Beijing Normal University and Dalian Neusoft University of Information. We are in compliance with the 1964 Declaration of Helsinki and its later addenda.

Informed consent was obtained from all individual participants included in the study. For participants under 18 years old, parent and guardian consent were obtained.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Aboagye E, Yawson JA, Appiah KN. COVID-19 and E-Learning: the Challenges of Students in Tertiary Institutions. Social Education Research. 2020; 2 (1):1–8. doi: 10.37256/ser.212021422. [ CrossRef ] [ Google Scholar ]
Aguilera-Hermida AP. College students’ use and acceptance of emergency online learning due to COVID-19. International Journal of Educational Research Open. 2020; 1 :100011. doi: 10.1016/j.ijedro.2020.100011. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Akgül G, Atalan Ergin D. Adolescents’ and parents’ anxiety during COVID-19: Is there a role of cyberchondriasis and emotion regulation through the internet? Current Psychology. 2021; 40 :4750–4759. doi: 10.1007/s12144-020-01229-7. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Alavi, S. M., Karami, H., & Khodi, A. (2021). Examination of factorial structure of Iranian English language proficiency test: An IRT analysis of Konkur examination. Current Psychology. 10.1007/s12144-021-01922-1
Andrich D. A rating formulation for ordered response categories. Psychometrika. 1978; 43 :561–573. doi: 10.1007/BF02293814. [ CrossRef ] [ Google Scholar ]
Artino AR, Jr, Jones KD., II Exploring the complex relations between achievement emotions and self-regulated learning behaviors in online learning. The Internet and Higher Education. 2012; 15 (3):170–175. doi: 10.1016/j.iheduc.2012.01.006. [ CrossRef ] [ Google Scholar ]
Baba MM. Navigating COVID-19 with emotional intelligence. International Journal of Social Psychiatry. 2020; 66 (8):810–820. doi: 10.1177/0020764020934519. [ PubMed ] [ CrossRef ] [ Google Scholar ]
Bandura A. Social-learning theory of identificatory processes. Handbook of socialization theory and research. 1969; 213 :262. [ Google Scholar ]
Bandura A. Self-efficacy: toward a unifying theory of behavioral change. Psychological review. 1977; 84 (2):191. doi: 10.1037/0033-295X.84.2.191. [ PubMed ] [ CrossRef ] [ Google Scholar ]
Bao W. COVID-19 and online teaching in higher education: A case study of Peking University. Human Behavior and Emerging Technologies. 2020; 2 (2):113–115. doi: 10.1002/hbe2.191. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Basilaia, G., & Kvavadze, D. (2020). Transition to online education in schools during a SARS-CoV-2 coronavirus (COVID-19) pandemic in Georgia. Pedagogical Research, 5 (4).
Beaton DE, Bombardier C, Guillemin F, Ferraz MB. Guidelines for the process of cross-cultural adaptation of self-report measures. Spine. 2000; 25 (24):3186–3191. doi: 10.1097/00007632-200012150-00014. [ PubMed ] [ CrossRef ] [ Google Scholar ]
Bernard RM, Brauer A, Abrami PC, Surkes M. The development of a questionnaire for predicting online learning achievement. Distance education. 2004; 25 (1):31–47. doi: 10.1080/0158791042000212440. [ CrossRef ] [ Google Scholar ]
Bhaumik R, Priyadarshini A. E-readiness of senior secondary school learners to online learning transition amid COVID-19 lockdown. Asian Journal of Distance Education. 2020; 15 (1):244–256. [ Google Scholar ]
Blakemore SJ. The social brain in adolescence. Nature Reviews Neuroscience. 2008; 9 (4):267–277. doi: 10.1038/nrn2353. [ PubMed ] [ CrossRef ] [ Google Scholar ]
Booker JA, Dunsmore JC. Affective social competence in adolescence: Current findings and future directions. Social Development. 2017; 26 (1):3–20. doi: 10.1111/sode.12193. [ CrossRef ] [ Google Scholar ]
Brackett MA, Rivers SE, Reyes MR, Salovey P. Enhancing academic performance and social and emotional competence with the RULER feeling words curriculum. Learning and Individual Differences. 2012; 22 (2):218–224. doi: 10.1016/j.lindif.2010.10.002. [ CrossRef ] [ Google Scholar ]
Casey BJ, Heller AS, Gee DG, Cohen AO. Development of the emotional brain. Neuroscience letters. 2019; 693 :29–34. doi: 10.1016/j.neulet.2017.11.055. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Chen KC, Jang SJ. Motivation in online learning: Testing a model of self-determination theory. Computers in Human Behavior. 2010; 26 (4):741–752. doi: 10.1016/j.chb.2010.01.011. [ CrossRef ] [ Google Scholar ]
Chen T, Peng L, Jing B, Wu C, Yang J, Cong G. The impact of the COVID-19 pandemic on user experience with online education platforms in China. Sustainability. 2020; 12 (18):7329. doi: 10.3390/su12187329. [ CrossRef ] [ Google Scholar ]
Cheung GW, Rensvold RB. Evaluating goodness-of-fit indexes for testing measurement invariance. Structural equation modeling. 2002; 9 (2):233–255. doi: 10.1207/S15328007SEM0902_5. [ CrossRef ] [ Google Scholar ]
Chung E, Subramaniam G, Dass LC. Online Learning Readiness among University Students in Malaysia amidst COVID-19. Asian Journal of University Education. 2020; 16 (2):46–58. doi: 10.24191/ajue.v16i2.10294. [ CrossRef ] [ Google Scholar ]
Cigdem, H., & Ozturk, M. (2016). Critical components of online learning readiness and their relationships with learner achievement. Turkish Online Journal of Distance Education, 17 (2).
Cole PM. Moving ahead in the study of the development of emotion regulation. International Journal of Behavioral Development. 2014; 38 (2):203–207. doi: 10.1177/0165025414522170. [ CrossRef ] [ Google Scholar ]
Compeau DR, Higgins CA. Computer self-efficacy: Development of a measure and initial test. MIS quarterly. 1995; 19 (2):189–211. doi: 10.2307/249688. [ CrossRef ] [ Google Scholar ]
Daniels LM, Goegan LD, Parker PC. The impact of COVID-19 triggered changes to instruction and assessment on university students’ self-reported motivation, engagement and perceptions. Social Psychology of Education. 2021; 24 (2):1–20. [ PMC free article ] [ PubMed ] [ Google Scholar ]
Davies J, Graff M. Performance in e-learning: online participation and student grades. British Journal of Educational Technology. 2005; 36 (4):657–663. doi: 10.1111/j.1467-8535.2005.00542.x. [ CrossRef ] [ Google Scholar ]
Dekker I, De Jong EM, Schippers MC, Bruijn-Smolders D, Alexiou A, Giesbers B. Optimizing Students’ mental health and academic performance: ai-enhanced life crafting. Frontiers in Psychology. 2020; 11 :1063. doi: 10.3389/fpsyg.2020.01063. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Demir Kaymak Z, Horzum MB. Relationship between online learning readiness and structure and interaction of online learning students. Educational Sciences: Theory and Practice. 2013; 13 (3):1792–1797. [ Google Scholar ]
Denham SA, Bassett HH, Wyatt T. The socialization of emotional competence . In: Grusec JE, Hastings PD, editors. Handbook of socialization: Theory and research . The Guilford Press; 2015. pp. 590–613. [ Google Scholar ]
Draba, R. E. (1977). The identification and interpretation of item bias (Research Memorandum No. 25). Chicago, IL: Statistical Laboratory, Department of Education, University of Chicago.
Durlak JA, Weissberg RP, Dymnicki AB, Taylor RD, Schellinger KB. The impact of enhancing students’ social and emotional learning: A meta-analysis of school-based universal interventions. Child development. 2011; 82 (1):405–432. doi: 10.1111/j.1467-8624.2010.01564.x. [ PubMed ] [ CrossRef ] [ Google Scholar ]
Dwiyanti KE, Pratama IPY, Manik NPIMC. Online Learning Readiness of Junior High School Students in Denpasar. IJEE (Indonesian Journal of English Education) 2020; 7 (2):172–188. doi: 10.15408/ijee.v7i2.17773. [ CrossRef ] [ Google Scholar ]
Eastin MS, LaRose R. Internet self-efficacy and the psychology of the digital divide. Journal of computer-mediated communication. 2000; 6 (1):JCMC611. [ Google Scholar ]
Engin M. Analysis of Students' Online Learning Readiness Based on Their Emotional Intelligence Level. Universal Journal of Educational Research. 2017; 5 (n12A):32–40. doi: 10.13189/ujer.2017.051306. [ CrossRef ] [ Google Scholar ]
Ferrer, J., Ringer, A., Saville, K., Parris, M. A., & Kashi, K. (2020). Students’ motivation and engagement in higher education: The importance of attitude to online learning. Higher Education, 1-22 . 10.1007/s10734-020-00657-5
Garner PW. Emotional competence and its influences on teaching and learning. Educational Psychology Review. 2010; 22 (3):297–321. doi: 10.1007/s10648-010-9129-4. [ CrossRef ] [ Google Scholar ]
Grubic N, Badovinac S, Johri AM. Student mental health in the midst of the COVID-19 pandemic: A call for further research and immediate solutions. International Journal of Social Psychiatry. 2020; 66 (5):517–518. doi: 10.1177/0020764020925108. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Habibu T, Abdullah-Al-Mamun MD, Clement C. Difficulties faced by teachers in using ICT in teaching-learning at technical and higher educational institutions of Uganda. International Journal of Engineering. 2012; 1 (7):1–10. [ Google Scholar ]
Hadar LL, Ergas O, Alpert B, Ariav T. Rethinking teacher education in a VUCA world: student teachers’ social-emotional competencies during the Covid-19 crisis. European Journal of Teacher Education. 2020; 43 (4):573–586. doi: 10.1080/02619768.2020.1807513. [ CrossRef ] [ Google Scholar ]
Hallam WT, Olsson CA, O’Connor M, Hawkins M, Toumbourou JW, Bowes G, Sanson A. Association between adolescent eudaimonic behaviours and emotional competence in young adulthood. Journal of Happiness Studies. 2014; 15 (5):1165–1177. doi: 10.1007/s10902-013-9469-0. [ CrossRef ] [ Google Scholar ]
Hamza, C. A., Ewing, L., Heath, N. L., & Goldstein, A. L. (2020). When social isolation is nothing new: A longitudinal study psychological distress during COVID-19 among university students with and without preexisting mental health concerns. Canadian Psychology/Psychologie canadienne. Advance online publication. 10.1037/cap0000255
Harley JM, Pekrun R, Taxer JL, Gross JJ. Emotion regulation in achievement situations: An integrated model. Educational Psychologist. 2019; 54 (2):106–126. doi: 10.1080/00461520.2019.1587297. [ CrossRef ] [ Google Scholar ]
Hatlevik OE, Throndsen I, Loi M, Gudmundsdottir GB. Students’ ICT self-efficacy and computer and information literacy: Determinants and relationships. Computers & Education. 2018; 118 :107–119. doi: 10.1016/j.compedu.2017.11.011. [ CrossRef ] [ Google Scholar ]
Horzum MB, Kaymak ZD, Gungoren OC. Structural equation modeling towards online learning readiness, academic motivations, and perceived learning. Educational Sciences: Theory and Practice. 2015; 15 (3):759–770. [ Google Scholar ]
Hu LT, Bentler PM. Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural equation modeling: a multidisciplinary journal. 1999; 6 (1):1–55. doi: 10.1080/10705519909540118. [ CrossRef ] [ Google Scholar ]
Hung ML, Chou C, Chen CH, Own ZY. Learner readiness for online learning: Scale development and student perceptions. Computers & Education. 2010; 55 (3):1080–1090. doi: 10.1016/j.compedu.2010.05.004. [ CrossRef ] [ Google Scholar ]
Hurrell KE, Houwing FL, Hudson JL. Parental meta-emotion philosophy and emotion coaching in families of children and adolescents with an anxiety disorder. Journal of abnormal child psychology. 2017; 45 (3):569–582. doi: 10.1007/s10802-016-0180-6. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Hurrell KE, Hudson JL, Schniering CA. Parental reactions to children's negative emotions: Relationships with emotion regulation in children with an anxiety disorder. Journal of anxiety disorders. 2015; 29 :72–82. doi: 10.1016/j.janxdis.2014.10.008. [ PubMed ] [ CrossRef ] [ Google Scholar ]
Imran, N., Zeshan, M., & Pervaiz, Z. (2020). Mental health considerations for children & adolescents in COVID-19 Pandemic. Pakistan journal of medical sciences , 36 (COVID19-S4), S67. [ PMC free article ] [ PubMed ]
Janssen LH, Kullberg MLJ, Verkuil B, van Zwieten N, Wever MC, van Houtum LA, Elzinga BM. Does the COVID-19 pandemic impact parents’ and adolescents’ well-being? An EMA-study on daily affect and parenting. PloS one. 2020; 15 (10):e0240962. doi: 10.1371/journal.pone.0240962. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Joosten T, Cusatis R. Online learning readiness. American Journal of Distance Education. 2020; 34 (3):180–193. doi: 10.1080/08923647.2020.1726167. [ CrossRef ] [ Google Scholar ]
Khalilzadeh S, Khodi A. Teachers’ personality traits and students’ motivation: A structural equation modeling analysis. Current Psychology. 2021; 40 (4):1635–1650. doi: 10.1007/s12144-018-0064-8. [ CrossRef ] [ Google Scholar ]
Khodi A, Alavi SM, Karami H. Test review of Iranian university entrance exam: English Konkur examination. Language Testing in Asia. 2021; 11 (14):1–10. doi: 10.1186/s40468-021-00125-6. [ CrossRef ] [ Google Scholar ]
Kim KJ, Frick TW. Changes in student motivation during online learning. Journal of Educational Computing Research. 2011; 44 (1):1–23. doi: 10.2190/EC.44.1.a. [ CrossRef ] [ Google Scholar ]
Kim C, Pekrun R. Emotions and Motivation in Learning and Performance. In: Spector JM, Merrill MD, Elen J, Bishop MJ, editors. Handbook of Research on Educational Communications and Technology . Springer; 2014. pp. 65–75. [ Google Scholar ]
Kline, R. B. (2015). Principles and practice of structural equation modeling . Guilford publications.
Knoll LJ, Fuhrmann D, Sakhardande AL, Stamp F, Speekenbrink M, Blakemore SJ. A window of opportunity for cognitive training in adolescence. Psychological Science. 2016; 27 (12):1620–1631. doi: 10.1177/0956797616671327. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Knoll LJ, Magis-Weinberg L, Speekenbrink M, Blakemore SJ. Social influence on risk perception during adolescence. Psychological science. 2015; 26 (5):583–592. doi: 10.1177/0956797615569578. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Kotsou I, Nelis D, Grégoire J, Mikolajczak M. Emotional plasticity: conditions and effects of improving emotional competence in adulthood. Journal of applied psychology. 2011; 96 (4):827. doi: 10.1037/a0023047. [ PubMed ] [ CrossRef ] [ Google Scholar ]
Kuhfeld M, Soland J, Tarasawa B, Johnson A, Ruzek E, Liu J. Projecting the potential impact of COVID-19 school closures on academic achievement. Educational Researcher. 2020; 49 (8):549–565. doi: 10.3102/0013189X20965918. [ CrossRef ] [ Google Scholar ]
Kuhfield, M., & Tarasawa, B. (2020). The COVID-19 Slide: What Summer Learning Loss Can Tell Us about the Potential Impact of School Closures on Student Academic Achievement. Brief. NWEA . https://files.eric.ed.gov/fulltext/ED609141.pdf
Lai J, Widmar NO. Revisiting the Digital Divide in the COVID-19 Era. Applied economic perspectives and policy. 2021; 43 (1):458–464. doi: 10.1002/aepp.13104. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Lau, P. S., & Wu, F. K. (2012). Emotional competence as a positive youth development construct: A conceptual review. The Scientific World Journal, 2012 . 10.1100/2012/975189 [ PMC free article ] [ PubMed ]
Lee Y, Choi J. A structural equation model of predictors of online learning retention. The Internet and Higher Education. 2013; 16 :36–42. doi: 10.1016/j.iheduc.2012.01.005. [ CrossRef ] [ Google Scholar ]
Li X, Fu P, Fan C, Zhu M, Li M. COVID-19 Stress and Mental Health of Students in Locked-Down Colleges. International journal of environmental research and public health. 2021; 18 (2):771. doi: 10.3390/ijerph18020771. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Liang L, Ren H, Cao R, Hu Y, Qin Z, Li C, Mei S. The effect of COVID-19 on youth mental health. Psychiatric quarterly. 2020; 91 (3):841–852. doi: 10.1007/s11126-020-09744-3. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Linacre, J. M. (2016). Winsteps Rasch measurement (Version 3.92.1). Beaverton, OR: Winsteps.com
Littlejohn A, Pegler C. Preparing for blended e-learning . Routledge; 2007. [ Google Scholar ]
Magson NR, Freeman JY, Rapee RM, Richardson CE, Oar EL, Fardouly J. Risk and protective factors for prospective changes in adolescent mental health during the COVID-19 pandemic. Journal of youth and adolescence. 2021; 50 (1):44–57. doi: 10.1007/s10964-020-01332-9. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Martha, A. S. D., Junus, K., Santoso, H. B., & Suhartanto, H. (2021). Assessing undergraduate students’ e-learning competencies: A case study of higher education context in Indonesia. Education Sciences, 11 (4). 10.3390/educsci11040189
Mathews BL, Koehn AJ, Abtahi MM, Kerns KA. Emotional competence and anxiety in childhood and adolescence: A meta-analytic review. Clinical Child and Family Psychology Review. 2016; 19 :162–184. doi: 10.1007/s10567-016-0204-3. [ PubMed ] [ CrossRef ] [ Google Scholar ]
Meinhardt J, Pekrun R. Attentional resource allocation to emotional events: An ERP study. Cognition and Emotion. 2003; 17 :477–500. doi: 10.1080/02699930244000039. [ PubMed ] [ CrossRef ] [ Google Scholar ]
Miao, T.-C., Gu, C.-H., Liu, S., & Zhou, Z. K. (2020). Internet literacy and academic achievement among Chinese adolescent: A moderated mediation model. Behaviour & Information Technology, 1–13 . 10.1080/0144929X.2020.1831074
Mikolajczak M, Brasseur S, Fantini-Hauwel C. Measuring intrapersonal and interpersonal EQ: The short profile of emotional competence (S-PEC) Personality and individual differences. 2014; 65 :42–46. doi: 10.1016/j.paid.2014.01.023. [ CrossRef ] [ Google Scholar ]
Miller-Slough RL, Dunsmore JC. Parent and friend emotion socialization in adolescence: Associations with psychological adjustment. Adolescent Research Review. 2016; 1 (4):287–305. doi: 10.1007/s40894-016-0026-z. [ PubMed ] [ CrossRef ] [ Google Scholar ]
Moroń, M., & Biolik-Moroń, M. (2020). Trait emotional intelligence and emotional experiences during the COVID-19 pandemic outbreak in Poland: A daily diary study. Personality and Individual Differences , 168 , 110348. [ PMC free article ] [ PubMed ]
Muthén, L. K., & Muthén, B. O. (2017). Mplus user’s guide (1998–2017). Los Angeles, CA: Muthén & Muthén .
Negru-Subtirica O, Pop EI. Longitudinal links between career adaptability and academic achievement in adolescence. Journal of Vocational Behavior. 2016; 93 :163–170. doi: 10.1016/j.jvb.2016.02.006. [ CrossRef ] [ Google Scholar ]
Oberle E, Schonert-Reichl KA, Hertzman C, Zumbo BD. Social–emotional competencies make the grade: Predicting academic success in early adolescence. Journal of Applied Developmental Psychology. 2014; 35 (3):138–147. doi: 10.1016/j.appdev.2014.02.004. [ CrossRef ] [ Google Scholar ]
O’Connor, M. (2020). School counselling during COVID-19: An initial examination of school counselling use during a 5-week remote learning period. Pastoral Care in Education, 1–11 . 10.1080/02643944.2020.1855674
Orgilés M, Morales A, Delvecchio E, Mazzeschi C, Espada JP. Immediate psychological effects of the COVID-19 quarantine in youth from Italy and Spain. Frontiers in psychology. 2020; 11 :2986. doi: 10.3389/fpsyg.2020.579038. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Pan H. A glimpse of university students’ family life amidst the COVID-19 virus. Journal of Loss and Trauma. 2020; 25 (6-7):594–597. doi: 10.1080/15325024.2020.1750194. [ CrossRef ] [ Google Scholar ]
Parker JD, Summerfeldt LJ, Hogan MJ, Majeski SA. Emotional intelligence and academic success: Examining the transition from high school to university. Personality and individual differences. 2004; 36 (1):163–172. doi: 10.1016/S0191-8869(03)00076-X. [ CrossRef ] [ Google Scholar ]
Pekrun, R. (2000). A social-cognitive, control-value theory of achievement emotions.
Pekrun R. The control-value theory of achievement emotions: Assumptions, corollaries, and implications for educational research and practice. Educational psychology review. 2006; 18 (4):315–341. doi: 10.1007/s10648-006-9029-9. [ CrossRef ] [ Google Scholar ]
Perret-Clermont AN, Pontecorvo C, Resnick LB, Zittoun T, Burge B. Joining society: Social interaction and learning in adolescence and youth . Cambridge University Press; 2004. [ Google Scholar ]
Pintrich PR. The role of goal orientation in self-regulated learning. In: Boekaerts M, Pintrich P, Zeidner, editors. Handbook of self-regulation . Academic Press; 2000. pp. 451–502. [ Google Scholar ]
Rafique GM, Mahmood K, Warraich NF, Rehman SU. Readiness for Online Learning during COVID-19 pandemic: A survey of Pakistani LIS students. The Journal of Academic Librarianship. 2021; 47 (3):102346. doi: 10.1016/j.acalib.2021.102346. [ CrossRef ] [ Google Scholar ]
Rhoades BL, Warren HK, Domitrovich CE, Greenberg MT. Examining the link between preschool social–emotional competence and first grade academic achievement: The role of attention skills. Early Childhood Research Quarterly. 2011; 26 (2):182–191. doi: 10.1016/j.ecresq.2010.07.003. [ CrossRef ] [ Google Scholar ]
Reimers, F. M., & Schleicher, A. (2020). A framework to guide an education response to the COVID-19 Pandemic of 2020. OECD. Retrieved from https://oecd.dam-broadcast.com/pm_7379_126_126988-t63lxosohs.pdf
Roper AR. How students develop online learning skills. Educause Quarterly. 2007; 30 (1):62. [ Google Scholar ]
Saarni C. The development of emotional competence . Guilford press; 1999. [ Google Scholar ]
Saarni C. Emotional competence: A developmental perspective. In: Bar-On R, Parker JDA, editors. The handbook of emotional intelligence: Theory, development, assessment, and application at home, school, and in the workplace . Jossey-Bass; 2000. pp. 68–91. [ Google Scholar ]
Sahu P. Closure of Universities Due to Coronavirus Disease 2019 (COVID-19): Impact on Education and Mental Health of Students and Academic Staff. Cureus. 2020; 12 (4):e7541. doi: 10.7759/cureus.7541. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Shanahan, L., Steinhoff, A., Bechtiger, L., Murray, A., Nivette, A., Hepp, U., . . . Eisner, M. (2020). Emotional distress in young adults during the COVID-19 pandemic: Evidence of risk and resilience from a longitudinal cohort study. Psychological Medicine, 1-10. 10.1017/S003329172000241X [ PMC free article ] [ PubMed ]
Smirni P, Lavanco G, Smirni D. Anxiety in Older Adolescents at the Time of COVID-19. Journal of Clinical Medicine. 2020; 9 (10):3064. doi: 10.3390/jcm9103064. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Smith PJ. Learning preferences and readiness for online learning. Educational psychology. 2005; 25 (1):3–12. doi: 10.1080/0144341042000294868. [ CrossRef ] [ Google Scholar ]
Son C, Hegde S, Smith A, Wang X, Sasangohar F. Effects of COVID-19 on college students’ mental health in the United States: Interview survey study. Journal of medical internet research. 2020; 22 (9):e21279. doi: 10.2196/21279. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Sorbom D. Model modification. Psychometrika. 1989; 54 :371–384. doi: 10.1007/BF02294623. [ CrossRef ] [ Google Scholar ]
Steinberg L. Cognitive and affective development in adolescence. Trends in cognitive sciences. 2005; 9 (2):69–74. doi: 10.1016/j.tics.2004.12.005. [ PubMed ] [ CrossRef ] [ Google Scholar ]
Subedi S, Nayaju S, Subedi S, Shah SK, Shah JM. Impact of E-learning during COVID-19 pandemic among nursing students and teachers of Nepal. International Journal of Science & Healthcare Research. 2020; 5 (3):68–76. [ Google Scholar ]
Takšić, V. (2002, October 4-6). The importance of emotional intelligence (competence) in positive psychology [Conference presentation]. 2002 1st International Positive Psychology Summit, Washington DC, United States.
Teng Z, Li Y, Liu Y. Online gaming, internet addiction, and aggression in Chinese male students: The mediating role of low self-control. International Journal of Psychological Studies. 2014; 6 (2):89. doi: 10.5539/ijps.v6n2p89. [ CrossRef ] [ Google Scholar ]
Tembo C, Burns S, Kalembo F. The association between levels of alcohol consumption and mental health problems and academic performance among young university students. PLoS One. 2017; 12 (6):e0178142. doi: 10.1371/journal.pone.0178142. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Thakur A. Mental Health in High School Students at the Time of COVID-19: A Student's Perspective. Journal of the American Academy of Child and Adolescent Psychiatry. 2020; 59 (12):1309–1310. doi: 10.1016/j.jaac.2020.08.005. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Torkzadeh G, Chang JCJ, Demirhan D. A contingency model of computer and Internet self-efficacy. Information & Management. 2006; 43 (4):541–550. doi: 10.1016/j.im.2006.02.001. [ CrossRef ] [ Google Scholar ]
Trentacosta CJ, Fine SE. Emotion knowledge, social competence, and behavior problems in childhood and adolescence: A meta-analytic review. Social Development. 2010; 19 (1):1–29. doi: 10.1111/j.1467-9507.2009.00543.x. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Tsai CC, Lin CC. Taiwanese adolescents' perceptions and attitudes regarding the Internet: Exploring gender differences. Adolescence. 2004; 39 (156):725–734. [ PubMed ] [ Google Scholar ]
Tsai MJ, Tsai CC. Student computer achievement, attitude, and anxiety: The role of learning strategies. Journal of Educational computing research. 2003; 28 (1):47–61. doi: 10.2190/PL27-TC1Q-08B2-RMCL. [ CrossRef ] [ Google Scholar ]
Tu CH. The measurement of social presence in an online learning environment. International Journal on E-learning. 2002; 1 (2):34–45. [ Google Scholar ]
Turner KL, Hughes M, Presland K. Learning loss, a potential challenge for transition to undergraduate study following COVID19 school disruption. Journal of Chemical Education. 2020; 97 (9):3346–3352. doi: 10.1021/acs.jchemed.0c00705. [ CrossRef ] [ Google Scholar ]
United Nations. (2020, August). Policy Brief: Education during COVID-19 and beyond. United Nations. Retrieved from https://www.un.org/development/desa/dspd/wp-content/uploads/sites/22/2020/08/sg_policy_brief_covid-19_and_education_august_2020.pdf
Valiente C, Swanson J, DeLay D, Fraser AM, Parker JH. Emotion-related socialization in the classroom: Considering the roles of teachers, peers, and the classroom context. Developmental psychology. 2020; 56 (3):578. doi: 10.1037/dev0000863. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Van der Aar LPE, Peters S, Van der Cruijsen R, Crone EA. The neural correlates of academic self-concept in adolescence and the relation to making future-oriented academic choices. Trends in neuroscience and education. 2019; 15 :10–17. doi: 10.1016/j.tine.2019.02.003. [ PubMed ] [ CrossRef ] [ Google Scholar ]
Vygotsky LS. Socio-cultural theory. Mind in society. 1978; 6 :52–58. [ Google Scholar ]
Wang LCC, Beasley W. Effects of learner control and hypermedia preference on cyber-students performance in a Web-based learning environment. Journal of Educational Multimedia and Hypermedia. 2002; 11 (1):71–91. [ Google Scholar ]
Wang X, Hegde S, Son C, Keller B, Smith A, Sasangohar F. Investigating mental health of US college students during the COVID-19 pandemic: cross-sectional survey study. Journal of medical Internet research. 2020; 22 (9):e22817. doi: 10.2196/22817. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Wathelet M, Duhem S, Vaiva G, Baubet T, Habran E, Veerapa E, D’Hondt F. Factors associated with mental health disorders among university students in France confined during the COVID-19 pandemic. JAMA network open. 2020; 3 (10):e2025591–e2025591. doi: 10.1001/jamanetworkopen.2020.25591. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Wei HC, Chou C. Online learning performance and satisfaction: do perceptions and readiness matter? Distance Education. 2020; 41 (1):48–69. doi: 10.1080/01587919.2020.1724768. [ CrossRef ] [ Google Scholar ]
Widaman KF. Best practices in quantitative methods for developmentalists: III. Missing data: What to do with or without them. Monographs of the Society for Research in Child Development. 2006; 71 (3):42–64. doi: 10.1111/j.1540-5834.2006.00404.x. [ PubMed ] [ CrossRef ] [ Google Scholar ]
Widodo A, Nursaptini N, Novitasari S, Sutisna D, Umar U. From face-to-face learning to web base learning: How are student readiness? Premiere Educandum: Jurnal Pendidikan Dasar Dan Pembelajaran. 2020; 10 (2):149–160. [ Google Scholar ]
Wind SA, Guo W. Exploring the Combined Effects of Rater Misfit and Differential Rater Functioning in Performance Assessments. Educational and Psychological Measurement. 2019; 79 (5):962–987. doi: 10.1177/0013164419834613. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Wind SA, Jami PY, Mansouri B. Exploring the psychometric properties of the empathy quotient for farsi speakers. Current Psychology. 2021; 40 (1):306–320. doi: 10.1007/s12144-018-9938-z. [ CrossRef ] [ Google Scholar ]
Wind SA, Mansouri B, Jami PY. Student Perceptions of Grammar Instruction in Iranian Secondary Education: Evaluation of an Instrument using Rasch Measurement Theory. Journal of Applied Measurement. 2019; 20 (1):46–65. [ PubMed ] [ Google Scholar ]
Wen, Y., Chen, H., Li, K., & Gu, X. (2020). The Challenges of Life Design Counseling in the Times of the Coronavirus Pandemic (COVID-19). Frontiers in Psychology, 11 . 10.3389/fpsyg.2020.01235 [ PMC free article ] [ PubMed ]
Wright, B. D., Mead, R., & Draba, R. E. (1976). Detecting and correcting test item bias with a logistic response model (Research Memorandum No. 22). Chicago, IL: Statistical Laboratory, Department of Education, University of Chicago.
Yilmaz R. Exploring the role of e-learning readiness on student satisfaction and motivation in flipped classroom. Computers in Human Behavior. 2017; 70 :251–260. doi: 10.1016/j.chb.2016.12.085. [ CrossRef ] [ Google Scholar ]
Yu T. Examining Construct Validity of the Student Online Learning Readiness (SOLR) Instrument Using Confirmatory Factor Analysis. Online Learning. 2018; 22 (4):277–288. doi: 10.24059/olj.v22i4.1297. [ CrossRef ] [ Google Scholar ]
Yurgelun-Todd D. Emotional and cognitive changes during adolescence. Current opinion in neurobiology. 2007; 17 (2):251–257. doi: 10.1016/j.conb.2007.03.009. [ PubMed ] [ CrossRef ] [ Google Scholar ]
Zembylas M, Theodorou M, Pavlakis A. The role of emotions in the experience of online learning: Challenges and opportunities. Educational Media International. 2008; 45 (2):107–117. doi: 10.1080/09523980802107237. [ CrossRef ] [ Google Scholar ]
Zhai Y, Du X. Addressing collegiate mental health amid COVID-19 pandemic. Psychiatry research. 2020; 288 :113003. doi: 10.1016/j.psychres.2020.113003. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Zhao, Y. (2021). Build back better: Avoid the learning loss trap. Prospects, 1-5 . 10.1007/s11125-021-09544-y [ PMC free article ] [ PubMed ]
Zimmerman BJ. Investigating self-regulation and motivation: Historical background, methodological developments, and future prospects. American educational research journal. 2008; 45 (1):166–183. doi: 10.3102/0002831207312909. [ CrossRef ] [ Google Scholar ]
Zins JE, Payton JW, Weissberg RP, O'Brien MU. Social and emotional learning for successful school performance. In: Matthews G, Zeidner M, Roberts RD, editors. The science of emotional intelligence: Knowns and unknowns . Oxford University Press; 2007. pp. 376–395. [ Google Scholar ]

Review article
Open access
Published: 09 November 2022

Shifting online during COVID-19: A systematic review of teaching and learning strategies and their outcomes

Joyce Hwee Ling Koh ORCID: orcid.org/0000-0001-5626-4927 1 &
Ben Kei Daniel 1

International Journal of Educational Technology in Higher Education volume 19 , Article number: 56 ( 2022 ) Cite this article

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This systematic literature review of 36 peer-reviewed empirical articles outlines eight strategies used by higher education lecturers and students to maintain educational continuity during the COVID-19 pandemic since January 2020. The findings show that students’ online access and positive coping strategies could not eradicate their infrastructure and home environment challenges. Lecturers’ learning access equity strategies made learning resources available asynchronously, but having access did not imply that students could effectively self-direct learning. Lecturers designed classroom replication, online practical skills training, online assessment integrity, and student engagement strategies to boost online learning quality, but students who used ineffective online participation strategies had poor engagement. These findings indicate that lecturers and students need to develop more dexterity for adapting and manoeuvring their online strategies across different online teaching and learning modalities. How these online competencies could be developed in higher education are discussed.

Introduction

Higher education institutions have launched new programmes online for three decades, but their integration of online teaching and learning into on-campus programmes remained less cohesive (Kirkwood & Price, 2014 ). Since early 2020, educational institutions have been shifting online in response to the COVID-19 pandemic. Some consider this kind of emergency remote teaching a temporary online shift during a crisis, whereas online learning involves purposive design for online delivery (Hodges et al., 2020 ). Two years into the pandemic, fully online, blended or hybridised modalities are still being used in response to evolving COVID-19 health advisories (Jaschik, 2021 ). Even though standards for the pedagogical, social, administrative, and technical requirements of online learning have already been published before the pandemic (e.g. Bigatel et al., 2012 ; Goodyear et al., 2001 ), the online competencies of lecturers and students remain critical challenges for higher education institutions during the pandemic (Turnbull et al., 2021 ). Emerging systematic literature reviews about higher education online teaching and learning during the pandemic focus on the clinical aspects of health science programmes (see Dedeilia et al., 2020 ; Hao et al., 2022 ; Papa et al., 2022 ). Understanding the strategies used in other programmes and disciplines is critical for outlining higher education lecturers’ and students’ future online competency needs.

This study, therefore, presents a systematic literature review of the teaching and learning strategies that lecturers and students used to shift online in response to the pandemic and their consequent outcomes. The review was conducted through content analysis and thematic analysis of 36 peer-reviewed articles published from January 2020 to December 2021. It discusses how relevant online competencies for lecturers and students can be further developed in higher education.

Methodology

A Systematic and Tripartite Approach (STA) (Daniel & Harland, 2017 ) guided the review process. STA draws from systematic review approaches such as the Cochrane Review Methods, widely used in application-based disciplines such as the health sciences (Chandler & Hopewell, 2013 ). It develops systematic reviews through description (providing a summary of the review), synthesis (logically categorising research reviewed based on related ideas, connections and rationales), and critique (providing evidence to support, discard or offer new ideas about the literature).

Framing the review

The following research questions guided the review:

What strategies did higher education lecturers and students use when they shifted teaching and learning online in response to the pandemic?

What were the outcomes arising from these strategies?

Search strategy

Peer-reviewed articles were identified from databases indexing leading educational journals—Educational Database (ProQuest), Education Research Complete (EBSCOhost), ERIC (ProQuest), Scopus, Web of Science (Core Collection), and ProQuest Central. The following search terms were used to locate articles with empirical evidence of lecturers’ and/or students’ shifting online strategies:

(remote OR virtual OR emergency remote OR online OR digital OR eLearning) AND (teaching strateg* OR learning strateg* OR shifting online) AND (higher education OR tertiary OR university OR college) AND (covid*) AND (success OR challenge OR outcome OR effect OR case OR lesson or evidence OR reflection)

The following were the inclusion and exclusion criteria:

Review period—From January 2020 to December 2021, following the first reported case of COVID-19 (WHO, 2020 ).

Language—Only articles published in the English language were included.

Type of article—In order maintain rigour in the findings, only peer-reviewed journal articles and conference proceedings were included, and non-refereed articles and conference proceedings were excluded. Peer-reviewed articles reporting empirical data from the lecturer and/or student perspectives were included. Editorials and literature reviews were examined to deepen conceptual understanding but excluded from the review.

The article’s focus—Articles with adequate descriptions and evaluation of lecturers’ and students’ online teaching and learning strategies undertaken because of health advisories during the COVID-19 pandemic were included. K-12 studies, higher education studies with data gathered prior to January 2020, studies describing general online learning experiences that did not arise from COVID-19, studies describing the functionalities of online learning technologies, studies about tips and tricks for using online tools during COVID-19, studies about the public health impact of COVID-19, or studies purely describing online learning attitudes or successes and challenges during COVID-19 without corresponding descriptions of teaching and learning strategies and their outcomes were excluded.

A list of 547 articles published between January 2020 and December 2021 were extracted using keyword and manual search with a final list of 36 articles selected for review (see Fig. 1 ). The inclusion and exclusion criteria were applied to the PRISMA process (Moher et al., 2009 ). The articles and a summary of coding are found in Appendix .

Article screening with the PRISMA process

Data analysis

Content analysis (Weber, 1990 ) and thematic analysis (Braun & Clarke, 2006 ) were used to answer the research questions. Pertinent sections of each article outlining lecturers’ and/or students’ shifting online strategies were identified, read and re-read for data familiarisation. The first author used content analysis to generate eight teaching and learning strategies. These were verified through an inter-rater analysis where a random selection of eight articles was recoded by a second-rater (22.22% of total articles) and confirmed with adequate Cohen’s kappas (Teaching strategies: 0.88, Learning strategies: 0.78). Frequency counts were analysed to answer research question 1.

For the second research question, we first categorised the various shifting online outcomes described in each article and coded each outcome as “success”, “challenge”, or “mixed”. Successful outcomes include favourable descriptions of teaching, learning, or assessment experiences, minimal issues with technology/infrastructure, favourable test scores, or reasonable attendance/course completion rates, whereas challenging outcomes suggest otherwise. Mixed outcomes were not a success or challenge, for example, positive and negative experiences during learning, assessment or with learning infrastructure, or mixed learning outcomes such as positive test scores but lower ratings of professional confidence. Frequency distributions were used to compare the overall successes and challenges of shifting online (see Tables 1 and 2 of “ Findings ” section). Following this, the pertinent outcomes associated with each of the eight shifting online strategies were pinpointed through thematic analysis and critical relationships were visualised as theme maps. These were continually reviewed for internal homogeneity and external heterogeneity (Patton, 1990 ). To ensure trustworthiness and reliability (Creswell, 1998 ), there was frequent debriefing between the authors to refine themes and theme maps, followed by critical peer review with another lecturer specialising in higher education educational technology practices. Throughout this process, an audit trail was maintained to document the evolution of themes. These processes completed the description and synthesis aspects of the systematic literature review prior to critique and discussion (Daniel & Harland, 2017 ).

Descriptive characteristics

Descriptive characteristics of the articles are summarised in Table 1 .

Table 1 shows that articles about shifting online during the pandemic were published steadily between August 2020 and December 2021. About two-thirds of the articles were based on data from the United States of America, Asia, or Australasia, with close to 45% of the articles analysing shifting online strategies used in the disciplines of Natural Sciences and Medical and Health Sciences and around 60% focusing on degree programmes. While there was an exact representation of studies with sample sizes from below 50 to above 150, the majority were descriptive studies, with close to half based on quantitative data gathered through surveys. About half of the articles focused on teaching strategies, while around 40% also examined students' learning strategies. However, only about 20% of the articles had theoretical framing for their teaching strategies. Besides using self-developed theories, the authors also used established theories such as the Community of Inquiry Theory by Garrison et. al. ( 2010 ), the Interaction Framework for Distance Education by Moore ( 1989 ), self-regulated learning by Zimmerman ( 2002 ) and the 5E model of Bybee et. al. ( 2006 ). Different types of shifting online outcomes were reported in the articles. The majority documented the positive and negative experiences associated with synchronous or asynchronous online learning activities, online learning technology and infrastructure, or online assessment. A quarter of the articles reported data on student learning outcomes and attendance/completion rates, while a minority also described teaching workload effects. Table 2 shows other successes and challenges associated with shifting online. Of the articles that examined online learning experiences, over a quarter reported clear successes in terms of positive experiences while about half reported mixed experiences. Majority of the articles examining technology and infrastructure experiences or assessment experiences either reported challenging or mixed experiences. All the articles examining learning outcomes reported apparent successes but only half of those investigating attendance/completion rates found these to be acceptable. Only challenges were reported for teaching workload.

Teaching strategies and outcomes

Lecturers used five teaching strategies to shift online during the pandemic (see Table 3 ).

Online practical skills training

Lecturers had to create online practical skills training . With limited access to clinical, field-based, or laboratory settings, lecturers taught only the conceptual aspects of practical skills through online guest lectures, live skill demonstration sessions, video recordings of field trips, conceptual application exercises, or by substituting skills practice with new theoretical topics (Chan et al., 2020 ; de Luca et al., 2021 ; Dietrich et al., 2020 ; Dodson & Blinn, 2021 ; Garcia-Alberti et al., 2021 ; Gomez et al., 2020 ; Xiao et al., 2020 ). Only in three studies about forest operations, ecology, and nursing was it possible to practice hand skills in alternative locations such as public parks and students’ homes (Dodson & Blinn, 2021 ; Gerhart et al., 2021 ; Palmer et al., 2021 ).

Outcomes : Online practical skills training had different effects on learning experiences, test scores, and attendance/completion rates. Students can attain expected test scores through conceptual learning of practical skills (Garcia-Alberti et al., 2021 ; Gomez et al., 2020 ; Xiao et al., 2020 ). However, not all students had positive learning experiences as some appreciated deeper conceptual learning, but others felt disconnected from peers, anxious about losing hand skills proficiency, and could not maintain class attendance (de Luca et al., 2021 ; Dietrich et al., 2020 ; Gomez et al., 2020 ). Positive learning experiences, reasonable course attendance/completion rates, and higher confidence in content mastery were more achievable when students had opportunities to practice hand skills in alternative locations (Gerhart et al., 2021 ).

Online assessment integrity

Lecturers had to devise strategies to maintain online assessment integrity , primarily through different ways of preventing cheating (see Reedy et al., 2021 ). Pass/Fail grading, reducing examination weightage through a higher emphasis on daily work and class participation, and asking students to make academic integrity declarations were some changes to examination policies (e.g. Ali et al., 2020 ; Dicks et al., 2020 ). Randomising and scrambling questions, administering different versions of examination papers, using proctoring software, open-book examinations, and replacing multiple choice with written questions were other ways of preventing cheating during online examinations (Hall et al., 2021 ; Jaap et al., 2021 ; Reedy et al., 2021 ).

Outcomes : There was concern that shifting to online assessment had detrimental effects on learning outcomes, but several studies reported otherwise (Garcia-Alberti et al., 2021 ; Gomez et al., 2020 ; Hall et al., 2021 ; Jaap et al., 2021 ; Lapitan et al., 2021 ). Nevertheless, there were mixed assessment experiences. When lecturers changed multiple-choice to written critical thinking questions, it made students perceive that examinations have become harder (Garcia-Alberti et al., 2021 ; Khan et al., 2022 ). Some students were anxious about encountering technical problems during online examinations, while others felt less nervous taking examinations at home (Jaap et al., 2021 ). Students also became less confident about the integrity of assessment processes when lecturers failed to set clear rules for open-book examinations (Reedy et al., 2021 ). While Pass/Fail grading alleviated students’ test performance anxiety, some lecturers felt that this lowered academic standards (Dicks et al., 2020 ; Khan et al., 2022 ). More emphasis on daily work alleviated student anxiety as examination weightage was reduced, but students also perceived a corresponding increase in course workload as they had more assignments to complete (e.g. Dietrich et al., 2020 ; Swanson et al., 2021 ).

Classroom replication

Lecturers used classroom replication strategies to foster regularity, primarily through substituting classroom sessions with video conferencing under pre-pandemic timetables (Palmer et al., 2021 ; Simon et al., 2020 ; Zhu et al., 2021 ). Lecturers also annotated their presentation materials and decorated their teaching locations with content-related backdrops to emulate the ‘chalk and talk’ of physical classrooms (e.g. Chan et al., 2020 ; Dietrich et al., 2020 ; Xiao et al., 2020 ).

Outcomes : Regular video conferencing classes helped students to maintain course attendance/completion rates (e.g. Ahmed & Opoku, 2021 ; Garcia-Alberti et al., 2021 ; Gerhart et al., 2021 ). Student engagement improved when lecturers annotated on Powerpoint™ or digital whiteboards during video conferencing (Hew et al., 2020 ). However, screen fatigue commonly affected concentration, and lecturers had challenges assessing social cues effectively, especially when students turned off their cameras (Khan et al., 2022 ; Lapitan et al., 2021 ; Marshalsey & Sclater, 2020 ). Lecturers tried to shorten class duration with asynchronous activities, only to find students failing to complete their assigned tasks (Grimmer et al., 2020 ).

Learning access equity

Lecturers implemented learning access equity strategies so that those without stable network connections or conducive home environments could continue studying (Abou-Khalil et al., 2021 ; Ahmed & Opoku, 2021 ; Dodson & Blinn, 2021 ; Garcia-Alberti et al., 2021 ; Grimmer et al., 2020 ; Kapasia et al., 2020 ; Khan et al., 2022 ; Marshalsey & Sclater, 2020 ; Pagoto et al., 2021 ; Swanson et al., 2021 ; Yeung & Yau, 2021 ). They equalised learning access by making lecture recordings available, using chat to communicate during live classes, and providing supplementary asynchronous activities (e.g. Gerhart et al., 2021 ; Grimmer et al., 2020 ). Some lecturers only delivered lessons asynchronously through pre-recorded lectures and online resources (e.g. de Luca et al., 2021 ; Dietrich et al., 2020 ). In developing countries, lecturers created access opportunities by sending learning materials through both learning management systems and WhatsApp™ (Kapasia et al., 2020 ).

Outcomes : Learning access strategies maintained some level of student equity through asynchronous learning but created challenging student learning experiences. There is evidence that students could achieve expected test scores through asynchronous learning (Garcia-Alberti et al., 2021 ) but maintaining learning consistency was a challenge, especially for freshmen (e.g. Grimmer et al., 2020 ; Khan et al., 2022 ). Some students found it hard to understand difficult concepts without in-person lectures but they also did not actively attend the live question-and-answer sessions organised by lecturers (Ali et al., 2020 ; Dietrich et al., 2020 ; Gomez et al., 2020 ). Poorly designed lecture recordings and unclear online learning instructions from lecturers compounded these problems (Gomez et al., 2020 ; Yeung & Yau, 2021 ).

Student engagement

Lecturers used two kinds of student engagement strategies, one of which was through active learning. Hew et. al. ( 2020 ) fostered active learning through 5E activities (Bybee et al., 2006 ) that encouraged students to Engage, Explore, Explain, Elaborate, and Evaluate. Lapitan et. al. ( 2021 ) implemented active learning through their DLPCA process, where students Discover, Learn and Practice outside of class with content resources and Collaborate in class before Assessment. Chan et. al. ( 2020 ) used their Theory of Change to support active learning through shared meaning-making. Other studies emphasised active learning but did not reference theoretical frameworks (e.g. Martinelli & Zaina, 2021 ). Many described how lecturers used interactive tools such as Nearpod™, and Padlet™, online polling, and breakout room discussions to encourage active learning (e.g. Ali et al., 2020 ; Gomez et al., 2020 ).

Another student engagement strategy was through regular communication and support, where lecturers sent emails, announcements, and reminders to keep students in pace with assignments (e.g. Abou-Khalil et al., 2021 ). Support was also provided through virtual office hours, social media contact after class hours and uploading feedback over shared drives (e.g. Khan et al., 2022 ; Xiao et al., 2020 ).

Outcomes : Among the student engagement strategies, success in test scores tends to be associated with the use of active learning (Garcia-Alberti et al., 2021 ; Gomez et al., 2020 ; Hew et al., 2020 ; Lapitan et al., 2021 ; Lau et al., 2020 ; Xiao et al., 2020 ). On the other hand, positive learning experiences were more often reported when lecturers emphasised care and empathy through their communication (e.g. Chan et al., 2020 ; Conklin & Dikkers, 2021 ). Students felt this more strongly when lecturers used humour, conversational and friendly tone, provided assurance, set clear expectations, exercised flexibility, engaged their feedback to improve online lessons, and responded swiftly to their questions (e.g. Chan et al., 2020 ; Swanson et al., 2021 ). These interactions fostered the social presence of Garrison et. al.’s ( 2010 ) Community of Inquiry Theory (Conklin & Dikkers, 2021 ). However, keeping up with multiple communication channels increased teaching workload, especially when support requests arrived through social media after work hours (Garcia-Alberti et al., 2021 ; Khan et al. 2022 ; Marshalsey & Sclater, 2020 ).

Learning strategies and outcomes

Students used three learning strategies during the pandemic (see Table 4 ).

Online access

Students had to maintain online access , as institutional support for data and technology was rarely reported (Ahmed & Opoku, 2021 ; Laher et al., 2021 ). Students did so by switching to more reliable internet service providers, purchasing more data, borrowing computing equipment, or switching off webcams during class (Kapasia et al., 2020 ; Mahmud & German, 2021 ).

Outcomes : Unstable internet connections, noisy home environments, tight study spaces, and disruptions from family duties were challenges often reported in students’ learning environments (e.g. Castelli & Sarvary, 2021 ; Yeung & Yau, 2021 ). The power supply was unstable in developing countries and students also had limited financial resources to purchase data. To keep studying, these students relied on materials shared through WhatsApp™ groups or Google Drive™ and learnt using mobile phones even though their small screen sizes affected students’ learning quality (Kapasia et al., 2020 ).

Online participation

Students had to maintain online participation by redesigning study routines according to when lecturers posted lecture recordings, identifying personal productive hours, changing work locations at home to improve focus and concentration, and devising study strategies to use online resources effectively, such as through note-taking (e.g. Abou-Khalil et al., 2021 ; Mahmud & German, 2021 ; Marshalsey & Sclater, 2020 ). Students also adjusted their online communication style by taking the initiative to contact lecturers through email, discussion forums, or chat for support, and learning new etiquette for video conferencing (Abou-Khalil et al., 2021 ; Dietrich et al., 2020 ; Mahmud & German, 2021 ; Simon et al., 2020 ; Yeung & Yau, 2021 ). Students recognised the need for active online participation (Yeung & Yau, 2021 ) but most tended to switch off webcams and avoided speaking up during class (Ahmed & Opoku, 2021 ; Castelli & Sarvary, 2021 ; Dietrich et al., 2020 ; Khan et al., 2022 ; Lapitan et al., 2021 ; Marshalsey & Sclater, 2020 ; Munoz et al., 2021 ; Rajab & Soheib, 2021 ).

Outcomes : Mahmud and German ( 2021 ) found that students lack the confidence to plan their study strategies, seek help, and manage time. Students also lacked confidence and switched off webcams out of privacy concerns or because they felt self-conscious about their appearances and home environments (Marshalsey & Sclater, 2020 ; Rajab & Soheib, 2021 ). Too many turned off webcams and this became a group norm (Castelli & Sarvary, 2021 ). Classes eventually became dominated by more vocal students, making the quieter ones feel left out (Dietrich et al., 2020 ).

Positive coping

Students’ positive coping strategies included family support, rationalising their situation, focusing on their future, self-motivation, and making virtual social connections with classmates (Ando, 2021 ; Laher et al., 2021 ; Mahmud & German, 2021 ; Reedy et al., 2021 ; Simon et al., 2020 ).

Outcomes : Positive coping strategies helped students to improve learning experiences, maintain attendance/completion rates, and avoid academic integrity violations during online examinations (Ando, 2021 ; Reedy et al., 2021 ; Simon et al., 2020 ). However, these strategies cannot circumvent technology and infrastructure challenges (Mahmud & German, 2021 ), while the realities of economic, family, and health pressures during the pandemic threatened their educational continuity and caused some to manifest negative coping behaviours such as despondency and overeating (Laher et al., 2021 ).

Higher education online competencies

This systematic review outlined eight teaching and learning strategies for shifting online during the pandemic. Online teaching competency frameworks published before the pandemic advocate active learning, social interaction, and prompt feedback as critical indicators of online teaching quality (e.g. Bigatel et al., 2012 ; Crews et al., 2015 ). The findings suggest that lecturers’ student engagement strategies aligned with these standards, but they also needed to adjust practical skills training, assessment, learning access channels, and classroom teaching strategies. Students’ online participation and positive coping strategies reflected how online learners could effectively manage routines, schedules and their sense of isolation (Roper, 2007 ). Since most students had no choice over online learning during the pandemic (Dodson & Blinn, 2021 ), those lacking personal motivation or adequate infrastructure had to develop online participation and online access strategies to cope with the situation.

The eight teaching and learning strategies effectively maintained test scores and attendance/completion rates, but many challenges surfaced during teaching, learning, and assessment. Turnbull et. al. ( 2021 ) attribute lecturers’ and students’ pandemic challenges to online competency gaps, particularly in digital literacy or competencies for accessing information, analysing data, and communicating with technology (Blayone et al., 2018 ). However, the study findings show that digital literacy may not be enough for students to overcome infrastructure and home environment challenges in their learning environment. Lecturers can try helping students mitigate these challenges by providing asynchronous resource access through access equity strategies. Yet, students may not successfully learn asynchronously unless they can effectively self-direct learning. Lecturers may have pedagogical knowledge to create engaging active online learning experiences. How these strategies effectively counteract students’ inhibitions to turn on webcams and speak up during class remains challenging. Lectures may also have the skills to set up different online communication channels, but students may not actively engage if care and empathy are perceived to be lacking. Furthermore, lecturers’ online assessment strategies may not always balance academic integrity with test validity.

These findings show that online competencies are not just standardised technical or pedagogical skills (e.g. Goodyear et al., 2001 ) but “socially situated” (Alvarez et al., 2009 , p. 322) abilities for manoeuvring strategies according to situation and context (Hatano & Inagaki, 1986 ). It encompasses “dexterity” or finesse with skill performance (Merriam-Webster, n.d.). The pandemic demands one to be “flexible and adaptable” (Ally, 2019 , p. 312) amidst shifting national, institutional and learning contexts. Online dexterity is needed in several areas. Online learning during the pandemic is rarely unimodal. Establishing the appropriate synchronous-asynchronous blend is a critical pedagogical decision for lecturers. They need dexterity across learning modalities to create the “right” blend in different student, content, and technological contexts (Baran et al., 2013 ; Martin et al., 2019 ). Lecturers also need domain-related dexterity to preserve authentic learning experiences while converting subject content online (Fayer, 2014 ). Especially when teaching skill-based content under different social distancing requirements, competencies to maintain learning authenticity through simulations, alternative locations, or equipment may be critical (e.g. Schirmel, 2021 ). Dexterity with online assessment is also essential. Besides preventing cheating, lecturers need to ensure that online assessments retain test validity, improve learning processes and are effective for performance evaluation (AERA, 2014 ; Sadler & Reimann, 2018 ). Another area is the dexterity to engage in online communication that appropriately manifests care and empathy (Baran et al., 2013 ). Since online teaching increases lecturers’ workload (Watermeyer et al., 2021 ), dexterity to balance student care and self-care without compromising learning quality is also crucial.

Access to conducive learning environments critically affects students’ online learning success (Kapasia et al., 2020 ). While some infrastructure challenges cannot be prevented, students should have the dexterity to mitigate their effects. For example, when disconnected from class because of bandwidth fluctuations, students should be able to find alternative ways of catching up with the lecturer rather than remaining passive and frustrated (Ezra et al., 2021 ). Self-direction is critical during online learning because it is the ability to set learning goals, self-manage learning processes, self-monitor, self-motivate, and adjust learning strategies (Garrison, 1997 ). Students need the dexterity to manage self-direction processes across different courses, learning modalities, and learning schedules. Dexterity to create an active learning presence through using appropriate learning etiquette and optimising the affordances of text, audio, video, and shared documents during class is also essential. This can support students' cognitive, social, and emotional engagement across synchronous and asynchronous modalities, individually or in groups (Zilvinskis et al., 2017 ).

Future directions

Online learning is highly diverse and increasingly dynamic, making it challenging to cover all published work for review. In this study, we have analysed pandemic-related teaching and learning strategies and their outcomes but recognise that a third of the studies were from the United States and close to half from natural or health science programmes. The findings cannot fully elucidate the strategies implemented in unrepresented countries or disciplines. Recognising these limitations, we propose the following as future directions for higher education:

Validate post-pandemic relevance of online teaching and learning strategies

The eight strategies can be validated through longitudinal empirical studies, theoretical analyses or meta-synthesis of literature to establish their relevance for post-pandemic teaching and learning. Studies outside the United States and the natural and health science disciplines are especially needed. This could address the paucity of theoretical framing in the articles reviewed, even with theories developed before the pandemic (e.g. Garrison et al., 2010 ; Moore, 1989 ; Zimmerman, 2002 ).

Demarcate post-pandemic online competencies

The plethora of descriptive studies in the articles reviewed is inadequate for understanding the online competencies driving lecturers’ pedagogical decision-making and students’ learning processes. In situ studies adopting qualitative methods such as grounded theory or phenomenology can better demarcate lecturers’ and students’ competencies for “why and under which conditions certain methods have to be used, or new methods have to be devised” (Bohle Carbonell et al., 2014 , p. 15). A longitudinal comparison of these studies can provide a better understanding of relevant post-pandemic competencies.

Develop dexterity with respect to application of online competencies

Higher education institutions use technology workshops, mentoring, and instructional consultation to develop competencies in technology-enhanced learning (e.g. Baran, 2016 ). However, dexterity to manoeuvre contextual differences may be better fostered through exploration, discovery, and exposure to varied contexts of practice (Mylopoulos et al., 2018 ). Innovative ways of developing dexterity with respect to how online competencies can be applied and the efficacy of these methodologies are areas for further research.

The COVID-19 pandemic has significantly increased the adoption and utilisation of online learning. While the present review findings suggest that the strategies lecturers and students employed to shift online during the pandemic have contributed to maintaining educational continuity and test scores but many outstanding issues remained unresolved. These include failure for students to gain an enhanced learning experience, problems encountered in designing and implementing robust assessment and online examinations, cases of academic misconduct, inequitable access to digital technologies, and increased faculty workload. Lecturers and institutions need to tackle these issues to fully leverage the opportunities afforded by online teaching and learning. Further, our findings revealed that the level of online dexterity for both students and teachers need to be enhanced. Therefore, higher education institutions must understand and develop online dexterity institutional frameworks to ensure that pedagogical innovation through online learning can be continually sustained, both during the pandemic and beyond.

Availability of data and materials

All data generated or analysed during this study are included in this published article.

Abou-Khalil, V., Helou, S., Khalife, E., Chen, M. A., Majumdar, R., & Ogata, H. (2021). Emergency online learning in low-resource settings: Effective student engagement strategies. Education Sciences, 11 (24), 1–18. https://doi.org/10.3390/educsci11010024

Article Google Scholar

AERA. (2014). Standards for educational and psychological testing. https://www.testingstandards.net/uploads/7/6/6/4/76643089/standards_2014edition.pdf

Ahmed, V., & Opoku, A. (2021). Technology supported learning and pedagogy in times of crisis: The case of COVID-19 pandemic. Education and Information Technologies . https://doi.org/10.1007/s10639-021-10706-w

Ali, I., Narayan, A. K., & Sharma, U. (2020). Adapting to COVID-19 disruptions: Student engagement in online learning of accounting. Accounting Research Journal, 34 (3), 261–269. https://doi.org/10.1108/arj-09-2020-0293

Ally, M. (2019). Competency profile of the digital and online teacher in future education. International Review of Research in Open and Distributed Learning, 20 (2), 302–318. https://doi.org/10.19173/irrodl.v20i2.4206

Alvarez, I., Guasch, T., & Espasa, A. (2009). University teacher roles and competencies in online learning environments: A theoretical analysis of teaching and learning practices. European Journal of Teacher Education, 32 (3), 321–336. https://doi.org/10.1080/02619760802624104

Ando, S. (2021). University teaching and learning in a time of social distancing: A sociocultural perspective. Journal of Human Behavior in the Social Environment, 31 (1–4), 435–448. https://doi.org/10.1080/10911359.2020.1814928

Baran, E. (2016). Investigating faculty technology mentoring as a university-wide professional development model. Journal of Computing in Higher Education, 28 (1), 45–71. https://doi.org/10.1007/s12528-015-9104-7

Baran, E., Correia, A.-P., & Thompson, A. (2013). Tracing successful online teaching in higher education: Voices of exemplary online teachers. Teachers College Record, 115 (3), 1–41.

Bigatel, P. M., Ragan, L. C., Kennan, S., May, J., & Redmond, B. F. (2012). The identification of competencies for online teaching success. Journal of Asynchronous Learning Networks, 16 (1), 59–77.

Google Scholar

Blayone, T. J. B., Mykhailenko, O., Kavtaradze, M., Kokhan, M., vanOostveen, R., & Barber, W. (2018). Profiling the digital readiness of higher education students for transformative online learning in the post-soviet nations of Georgia and Ukraine. International Journal of Educational Technology in Higher Education, 15 , 37. https://doi.org/10.1186/s41239-018-0119-9

Bohle Carbonell, K., Stalmeijer, R. E., Könings, K. D., Segers, M., & van Merriënboer, J. J. G. (2014). How experts deal with novel situations: A review of adaptive expertise. Educational Research Review, 12 , 14–29. https://doi.org/10.1016/j.edurev.2014.03.001

Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3 (2), 77–101. https://doi.org/10.1191/1478088706qp063oa

Bybee, R., Taylor, J., Gardner, A., Van Scotter, P., Powell, J., Westbrook, A., & Landes, N. (2006). The BSCS 5E instructional model: Origins, effectiveness, and applications. Colorado Springs: BSCS. International Journal of Man-Machine Studies, 29 , 407–427.

Castelli, F. R., & Sarvary, M. A. (2021). Why students do not turn on their video cameras during online classes and an equitable and inclusive plan to encourage them to do so. Ecology and Evolution, 11 (8), 3565–3576. https://doi.org/10.1002/ece3.7123

Chan, B. C., Baker, J. L., Bunagan, M. R., Ekanger, L. A., Gazley, J. L., Hunter, R. A., O’Connor, A. R., & Triano, R. M. (2020). Theory of change to practice: How experimentalist teaching enabled faculty to navigate the COVID-19 disruption. Journal of Chemical Education, 97 (9), 2788–2792. https://doi.org/10.1021/acs.jchemed.0c00731

Chandler, J., & Hopewell, S. (2013). Cochrane methods—Twenty years experience in developing systematic review methods. Systematic Reviews, 2 (1), 76. https://doi.org/10.1186/2046-4053-2-76

Conklin, S., & Dikkers, A. G. (2021). Instructor social presence and connectedness in a quick shift from face-to-face to online instruction. Online Learning, 25 (1), 135–150. https://doi.org/10.24059/olj.v25i1.2482

Creswell, J. W. (1998). Qualitative inquiry and research design . SAGE Publications.

Crews, T. B., Wilkinson, K., & Neill, J. K. (2015). Principles for good practice in undergraduate education: Effective online course design to assist students’ success. Journal of Online Learning and Teaching, 11 (1), 87–103.

Daniel, B. K., & Harland, T. (2017). Higher education research methodology: A step-by-step guide to the research process . Routledge.

Book Google Scholar

de Luca, K., McDonald, M., Montgomery, L., Sharp, S., Young, A., Vella, S., Holmes, M. M., Aspinall, S., Brousseau, D., Burrell, C., Byfield, D., Dane, D., Dewhurst, P., Downie, A., Engel, R., Gleberzon, B., Hollandsworth, D., Nielsen, A. M., O’Connor, L., … French, S. D. (2021). COVID-19: How has a global pandemic changed manual therapy technique education in chiropractic programs around the world? Chiropractic & Manual Therapies, 29 (1), 11. https://doi.org/10.1186/s12998-021-00364-7

Dedeilia, A., Sotiropoulos, M. G., Hanrahan, J. G., Janga, D., Dedeilias, P., & Sideris, M. (2020). Medical and surgical education challenges and innovations in the COVID-19 era: A systematic review. In Vivo, 34 (3 suppl), 1603. https://doi.org/10.21873/invivo.11950

Dicks, A. P., Morra, B., & Quinlan, K. B. (2020). Lessons learned from the COVID-19 crisis: Adjusting assessment approaches within introductory organic courses. Journal of Chemical Education, 97 (9), 3406–3412. https://doi.org/10.1021/acs.jchemed.0c00529

Dietrich, N., Kentheswaran, K., Ahmadi, A., Teychene, J., Bessiere, Y., Alfenore, S., Laborie, S., Bastoul, D., Loubiere, K., Guigui, C., Sperandio, M., Barna, L., Paul, E., Cabassud, C., Line, A., & Hebrard, G. (2020). Attempts, successes, and failures of distance learning in the time of COVID-19. Journal of Chemical Education, 97 (9), 2448–2457. https://doi.org/10.1021/acs.jchemed.0c00717

Dodson, E. M., & Blinn, C. R. (2021). Forest operations instructor and student perspectives on rapid transition from face-to-face to online learning in the US. International Journal of Forest Engineering . https://doi.org/10.1080/14942119.2021.1907109

Ezra, O., Cohen, A., Bronshtein, A., Gabbay, H., & Baruth, O. (2021). Equity factors during the COVID-19 pandemic: Difficulties in emergency remote teaching (ert) through online learning. Education and Information Technologies, 26 , 7657–7681. https://doi.org/10.1007/s10639-021-10632-x

Fayer, L. (2014). A multi-case study of student perceptions of online course design elements and success. International Journal for the Scholarship of Teaching & Learning, 8 (1), 1–27. https://doi.org/10.20429/ijsotl.2014.080113

Garcia-Alberti, M., Suarez, F., Chiyon, I., & Feijoo, J. C. M. (2021). Challenges and experiences of online evaluation in courses of civil engineering during the lockdown learning due to the COVID-19 pandemic. Education Sciences, 11 (2), 19. https://doi.org/10.3390/educsci11020059

Garrison, D. R. (1997). Self-directed learning: Toward a comprehensive model. Adult Education Quarterly , 48 (1), 18–33. https://doi.org/10.1177/074171369704800103

Garrison, D. R., Anderson, T., & Archer, W. (2010). The first decade of the community of inquiry framework: A retrospective. The Internet and Higher Education, 13 (1), 5–9. https://doi.org/10.1016/j.iheduc.2009.10.003

Gerhart, L. M., Jadallah, C. C., Angulo, S. S., & Ira, G. C. (2021). Teaching an experiential field course via online participatory science projects: A COVID-19 case study of a UC California Naturalist course. Ecology and Evolution, 11 (8), 3537–3550. https://doi.org/10.1002/ece3.7187

Gomez, E., Azadi, J., & Magid, D. (2020). Innovation born in isolation: Rapid transformation of an in-person medical student radiology elective to a remote learning experience during the COVID-19 pandemic. Academic Radiology, 27 (9), 1285–1290. https://doi.org/10.1016/j.acra.2020.06.001

Goodyear, P., Salmon, G., Spector, J. M., Steeples, C., & Tickner, S. (2001). Competences for online teaching: A special report. Educational Technology Research and Development, 49 (1), 65–72.

Grimmer, R., Pollard, A., & Rolls, N. (2020). COVID-19 induced change in higher education: Reflections on rapidly transitioning a first-year undergraduate academic literacies unit from face-to-face to online. Journal of Academic Language and Learning, 14 , 95–105.

Hall, E. A. P., Spivey, C. P., Kendrex, H. P., & Havrda, D. E. P. (2021). Effects of remote proctoring on composite examination performance among Doctor of pharmacy students. American Journal of Pharmaceutical Education, 85 (8), 824–828. https://doi.org/10.5688/ajpe8410

Hao, X., Peng, X., Ding, X., Qin, Y., Lv, M., Li, J., & Li, K. (2022). Application of digital education in undergraduate nursing and medical interns during the COVID-19 pandemic: A systematic review. Nurse Education Today, 108 , 105183. https://doi.org/10.1016/j.nedt.2021.105183

Hatano, G., & Inagaki, K. (1986). Two courses of expertise. In H. A. H. Stevenson & K. Hakuta (Eds.), Child development and education in Japan (pp. 262–272). Freeman.

Hew, K. F., Jia, C., Gonda, D. E., & Bai, S. (2020). Transitioning to the “new normal” of learning in unpredictable times: Pedagogical practices and learning performance in fully online flipped classrooms. International Journal of Educational Technology in Higher Education, 17 (1), 1–22. https://doi.org/10.1186/s41239-020-00234-x

Hodges, C. B., Moore, S., Lockee, B. B., Trust, T., & Bond, M. A. (2020). The difference between emergency remote teaching and online learning . Educause Review. https://er.educause.edu/articles/2020/3/the-difference-between-emergency-remote-teaching-and-online-learning

Jaap, A., Dewar, A., Duncan, C., Fairhurst, K., Hope, D., & Kluth, D. (2021). Effect of remote online exam delivery on student experience and performance in applied knowledge tests. BMC Medical Education, 21 , 1–7. https://doi.org/10.1186/s12909-021-02521-1

Jaschik, S. (2021, 16 August). Delta variant raises questions as campuses start semester. Inside Higher Ed. https://www.insidehighered.com/news/2021/08/16/delta-variant-raises-questions-colleges-about-reopening-plans

Kapasia, N., Paul, P., Roy, A., Saha, J., Zaveri, A., Mallick, R., Barman, B., Das, P., & Chouhan, P. (2020). Impact of lockdown on learning status of undergraduate and postgraduate students during COVID-19 pandemic in West Bengal, India. Children and Youth Services Review, 116 (5), 105194. https://doi.org/10.1016/j.childyouth.2020.105194

Khan, S., Kambris, M. E., & Alfalahi, H. (2022). Perspectives of university students and faculty on remote education experiences during COVID-19—A qualitative study. Education and Information Technologies . https://doi.org/10.1007/s10639-021-10784-w

Kirkwood, A., & Price, L. (2014). Technology-enhanced learning and teaching in higher education: What is ‘enhanced’ and how do we know? A critical literature review. Learning, Media and Technology, 39 (1), 6–36. https://doi.org/10.1080/17439884.2013.770404

Laher, S., Bain, K., Bemath, N., de Andrade, V., & Hassem, T. (2021). Undergraduate psychology student experiences during COVID-19: Challenges encountered and lessons learnt. South African Journal of Psychology . https://doi.org/10.1177/0081246321995095

Lapitan, L. D., Jr., Tiangco, C. E., Sumalinog, D. A. G., Sabarillo, N. S., & Diaz, J. M. (2021). An effective blended online teaching and learning strategy during the COVID-19 pandemic. Education for Chemical Engineers, 35 , 116–131. https://doi.org/10.1016/j.ece.2021.01.012

Lau, P. N., Chua, Y. T., Teow, Y., & Xue, X. J. (2020). Implementing alternative assessment strategies in chemistry amidst COVID-19: Tensions and reflections. Education Sciences, 10 (11), 15. https://doi.org/10.3390/educsci10110323

Mahmud, Y. S., & German, E. (2021). Online self-regulated learning strategies amid a global pandemic: Insights from Indonesian university students. Malaysian Journal of Learning and Instruction, 18 (2), 45–68. https://doi.org/10.32890/mjli2021.18.2.2

Marshalsey, L., & Sclater, M. (2020). Together but apart: Creating and supporting online learning communities in an era of distributed studio education. International Journal of Art & Design Education, 39 (4), 826–840. https://doi.org/10.1111/jade.12331

Martin, F., Ritzhaupt, A., Kumar, S., & Budhrani, K. (2019). Award-winning faculty online teaching practices: Course design, assessment and evaluation, and facilitation. The Internet and Higher Education, 42 , 34–43. https://doi.org/10.1016/j.iheduc.2019.04.001

Martinelli, S. R., & Zaina, L. A. M. (2021). Learning HCI from a virtual flipped classroom: Improving the students’ experience in times of COVID-19. In ACM international conference proceeding series, virtual event. https://doi.org/10.1145/3472301.3484326

Merriam-Webster. (n.d.). Dexterity . Retrieved December 8, 2021, from https://www.merriam-webster.com/dictionary/dexterity

Moher, D., Liberati, A., Tetzlaff, J., Altman, D. G., & The, P. G. (2009). Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. PLOS Medicine, 6 (7), e1000097. https://doi.org/10.1371/journal.pmed.1000097

Moore, M. G. (1989). Three types of interaction. The American Journal of Distance Education, 3 (2), 1–6. https://doi.org/10.1080/08923648909526659

Munoz, K. E., Wang, M. J., & Tham, A. (2021). Enhancing online learning environments using social presence: Evidence from hospitality online courses during COVID-19. Journal of Teaching in Travel & Tourism . https://doi.org/10.1080/15313220.2021.1908871

Mylopoulos, M., Kulasegaram, K., & Woods, N. N. (2018). Developing the experts we need: Fostering adaptive expertise through education. Journal of Evaluation in Clinical Practice, 24 (3), 674–677. https://doi.org/10.1111/jep.12905

Pagoto, S., Lewis, K. A., Groshon, L., Palmer, L., Waring, M. E., Workman, D., De Luna, N., & Brown, N. P. (2021). STEM undergraduates’ perspectives of instructor and university responses to the COVID-19 pandemic in Spring 2020. PLoS ONE, 16 (8), 1–20. https://doi.org/10.1371/journal.pone.0256213

Palmer, T. J., Chisholm, L. J., Rolf, C. G., & Morris, C. R. (2021). Deliberate practice and self-recorded demonstration of skill proficiency: One baccalaureate nursing school’s response to the COVID-19 pandemic. Nurse Education in Practice, 53 , 1–5. https://doi.org/10.1016/j.nepr.2021.103071

Papa, V., Varotto, E., Galli, M., Vaccarezza, M., & Galassi, F. M. (2022). One year of anatomy teaching and learning in the outbreak: Has the Covid-19 pandemic marked the end of a century-old practice? A systematic review. Anatomical Sciences Education, 15 (2), 261–280. https://doi.org/10.1002/ase.2162

Patton, M. Q. (1990). Qualitative evaluation and research methods . SAGE Publications.

Rajab, M. H., & Soheib, M. (2021). Privacy concerns over the use of webcams in online medical education during the COVID-19 pandemic. Cureus, 13 (2), 8. https://doi.org/10.7759/cureus.13536

Reedy, A., Pfitzner, D., Rook, L., & Ellis, L. (2021). Responding to the COVID-19 emergency: Student and academic staff perceptions of academic integrity in the transition to online exams at three Australian universities. International Journal for Educational Integrity, 17 (1), 1–32. https://doi.org/10.1007/s40979-021-00075-9

Roper, A. R. (2007). How students develop online learning skills. Educause Quarterly, 30 (1), 62–65.

Sadler, I., & Reimann, N. (2018). Variation in the development of teachers’ understandings of assessment and their assessment practices in higher education. Higher Education Research & Development, 37 (1), 131–144. https://doi.org/10.1080/07294360.2017.1344199

Schirmel, J. (2021). COVID-19 pandemic turns life-science students into “citizen scientists”: Data indicate multiple negative effects of urbanization on biota. Sustainability, 13 (5), 16. https://doi.org/10.3390/su13052992

Simon, L. E., Genova, L. E., Kloepper, M. L. O., & Kloepper, K. D. (2020). Learning postdisruption: Lessons from students in a fully online nonmajors laboratory course. Journal of Chemical Education, 97 (9), 2430–2438. https://doi.org/10.1021/acs.jchemed.0c00778

Swanson, S. R., Davis, J. C., Gonzalez-Fuentes, M., & Robertson, K. R. (2021). In these unprecedented times: A critical incidents technique examination of student perceptions’ of satisfying and dissatisfying learning experiences. Marketing Education Review, 31 (3), 209–225. https://doi.org/10.1080/10528008.2021.1952082

Turnbull, D., Chugh, R., & Luck, J. (2021). Transitioning to e-learning during the COVID-19 pandemic: How have higher education institutions responded to the challenge? Education and Information Technologies, 26 (5), 6401–6419. https://doi.org/10.1007/s10639-021-10633-w

Watermeyer, R., Crick, T., Knight, C., & Goodall, J. (2021). COVID-19 and digital disruption in UK universities: Afflictions and affordances of emergency online migration. Higher Education, 81 (3), 623–641. https://doi.org/10.1007/s10734-020-00561-y

Weber, R. P. (1990). Basic content analysis . SAGE Publications.

WHO. (2020). Novel coronavirus (2019-nCoV) situation report -1 . World Health Organization. https://www.who.int/docs/default-source/coronaviruse/situation-reports/20200121-sitrep-1-2019-ncov.pdf?sfvrsn=20a99c10_4

Xiao, C. L., Cai, H., Su, Y. J., & Shen, L. M. (2020). Online teaching practices and strategies for inorganic chemistry using a combined platform based on DingTalk, Learning@ZJU, and WeChat. Journal of Chemical Education, 97 (9), 2940–2944. https://doi.org/10.1021/acs.jchemed.0c00642

Yeung, M. W. L., & Yau, A. H. Y. (2021). A thematic analysis of higher education students’ perceptions of online learning in Hong Kong under COVID-19: Challenges, strategies and support. Education and Information Technologies . https://doi.org/10.1007/s10639-021-10656-3

Zhu, X. Q., Shek, D. T. L., & Chan, C. H. M. (2021). Promoting service leadership qualities and well-being among university students through an online course during COVID-19 pandemic. International Journal of Environmental Research and Public Health, 18 (15), 8162. https://doi.org/10.3390/ijerph18158162

Zilvinskis, J., Masseria, A. A., & Pike, G. R. (2017). Student engagement and student learning: Examining the convergent and discriminant validity of the revised national survey of student engagement. Research in Higher Education, 58 (8), 880–903. https://doi.org/10.1007/s11162-017-9450-6

Zimmerman, B. J. (2002). Becoming a self-regulated learner: An overview. Theory into Practice, 41 (2), 64.

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Appendix: Selected articles and coding

SN	Author and article information	Teaching strategies					Learning strategies			Outcomes C—Challenge S—Success M—Mixed outcome
SN	Author and article information	ST	AI	CR	AE	SE	OA	OP	PC	LE	TIN	ASS	LO	AC	TW
1	Abou-Khalil et. al. ( ) Site: Multiple Level: Multiple Subject: Multiple Methodology: Survey N: 300–349 Published: Jan-21 Theory: Moore’s interaction framework				√	√	√	√		M	C
2	Ahmed and Opoku ( ) Site: Middle East Level: Multiple Subject: Engineering or Computer Science Methodology: Mixed methods N: 300–349 Published: Aug-21		√	√	√	√	√	√		M	M	M		S
3	Ali et. al. ( ) Site: Australasia Level: Multiple Subject: Commerce Methodology: Qualitative N: ≤ 50 Published: Oct-20		√	√	√	√				M				M
4	Ando ( ) Site: Asia Level: Postgrad Subject: Multiple Methodology: Qualitative N: ≤ 50 Published: Oct-20							√	√	M	C			S
5	Castelli and Sarvary ( ) Site: USA Level: Degree Subject: Natural Sciences Methodology: Survey—Student N: 250–299 Published: Nov-20					√	√	√		C
6	Chan et. al. ( ) Site: USA Level: Degree Subject: Natural Sciences Methodology: Qualitative N ≤ 50 Published: Aug-20 Theory: Theory of Change (ToC)	√		√	√	√				M
7	Conklin and Dikkers ( ) Site: USA Level: Multiple Subject: Multiple Methodology: Survey N: 400–449 Published: Mar-21 Theory: COI					√				S
8	de Luca et. al. ( ) Site: Multiple Level: Degree Subject: Medical and Health Sciences Methodology: Survey—Teacher N: ≤ 50 Published: Jan-21	√	√	√	√	√				C				C
9	Dicks et. al. ( ) Site: Others Level: Degree (1st yr) Subject: Natural Sciences Methodology: Mixed methods N: ≤ 50 Published: Aug-20		√								S	S
10	Dietrich et. al. ( ) Site: Europe Level: Degree Subject: Natural Sciences Methodology: Survey N: 100–149 Published: Aug-20	√	√	√	√		√	√		S
11	Dodson and Blinn ( ) Site: USA Level: Degree Subject: Natural Sciences Methodology: Survey N: 51–99 Published: Apr-21	√	√	√	√	√				M	C
12	Garcia-Alberti et. al. ( ) Site: Latin America Level: Multiple Subject: Engineering or Computer Science Methodology: Mixed methods N: ≤ 50 Published: Feb-21	√	√	√	√	√				C	C	C	S	S	C
13	Gerhart et. al. ( ) Site: USA Level: Degree Subject: Natural Sciences Methodology: Mixed methods N: ≤ 50 Published: Dec-20	√		√	√					S			S	S
14	Gomez et. al. ( ) Site: USA Level: Degree Subject: Medical and Health Sciences Methodology: Mixed methods N: ≤ 50 Published: Sep-20	√	√	√	√	√				M			S
15	Grimmer et. al. ( ) Site: Australasia Level: Degree (1st yr) Subject: Others Methodology: Qualitative N: 300–349 Published: Nov-20			√	√	√				M	C			M
16	Hall et. al. ( ) Site: USA Level: Postgrad Subject: Medical and Health Sciences Methodology: Quasi-experiment/correlational N: ≥ 450 Published: Sep-21		√										S
17	Hew et. al. ( ) Site: Asia Level: Postgrad Subject: Education Methodology: Quasi-experiment/correlational N: 51–99 Published: Dec-20 Theory: 5E			√		√				S			S
18	Jaap et. al. ( ) Site: Europe Level: Degree Subject: Medical and Health Sciences Methodology: Quasi-experiment/correlational N: 100–149 Published: Feb-21		√				√				S	M	S
19	Kapasia et. al. ( ) Site: Others Level: Multiple Subject: Multiple Methodology: Survey N: 200–249 Published: Sep-20				√	√	√				C
20	Khan et. al. ( ) Site: Middle East Level: Degree Subject: Natural Sciences Methodology: Qualitative N: 51–99 Published: Oct-21		√	√	√	√		√		M	M	C		M	C
21	Laher et. al. ( ) Site: Others Level: Degree Subject: Arts and Social Sciences Methodology: Survey N: 150–199 Published: Jun-21						√	√	√	C	C
22	Lapitan et. al. ( ) Site: Asia Level: Degree Subject: Engineering or Computer Science Methodology: Survey N: 150–199 Published: Jan-21 Theory: Discover, Learn, Practice, Collaborate and Assess (DLPCA)		√	√	√	√		√		M		M	S
23	Lau et. al. ( ) Site: Asia Level: Diploma Subject: Natural Sciences Methodology: Mixed methods N: 350–399 Published: Nov-20		√	√	√	√				S		C	S
24	Mahmud and German ( ) Site: Asia Level: Degree Subject: Others Methodology: Mixed methods N: 300–349 Published: Jul-21 Theory: Self-regulated Learning						√	√	√	M	C			M
25	Marshalsey and Sclater ( ) Site: Australasia Level: Degree Subject: Arts and Social Sciences Methodology: Qualitative N: 51–99 Published: Nov-20			√	√	√		√		M	C				C
26	Martinelli and Zaina ( ) Site: Latin America Level: Multiple Subject: Engineering or Computer Science Methodology: Mixed M: < 51 Published: Oct-21					√				S			S
27	Munoz et. al. ( ) Site: Asia Level: Postgrad Subject: Commerce Methodology: Qualitative N: ≤ 50 Published: Apr-21 Theory: COI				√	√		√		M
28	Pagoto et. al. ( ) Site: USA Level: Degree Subject: Multiple Methodology: Qualitative N: 51–99 Published: Aug-21		√	√	√	√				M	M
29	Palmer et. al. ( ) Site: USA Level: Degree Subject: Medical and Health Sciences Methodology: Survey N: ≤ 50 Published: May-21	√		√								S
30	Rajab and Soheib ( ) Site: Middle East Level: Multiple Subject: Medical and Health Sciences Methodology: Survey N: 300–349 Published: Feb-21							√		C
31	Reedy et. al. ( ) Site: Australasia Level: Multiple Subject: Multiple Methodology: Survey N: ≥ 450 Published: Mar-21		√			√		√	√			M
32	Simon et. al. ( ) Site: USA Level: Degree Subject: Natural Sciences Methodology: Survey N: ≤ 50 Published: Aug-20		√	√	√	√		√	√	S
33	Swanson et. al. ( ) Site: USA Level: Degree Subject: Commerce Methodology: Survey N: 300–349 Published: Jul-21		√		√	√				M	C	M
34	Xiao et. al. ( ) Site: Asia Level: Degree (1st yr) Subject: Natural Sciences Methodology: Mixed methods N: ≤ 50 Published: Aug-20	√	√	√	√	√							S	S
35	Yeung and Yau ( ) Site: Asia Level: Multiple Subject: Multiple Methodology: Survey N: 100–149 Publication month: Jun-21				√		√	√		C	C	C
36	Zhu et. al. ( ) Site: Asia Level: Degree Subject: Others Methodology: Quasi-experiment/correlational N: 200–249 Published: Aug-21			√	√	√				S

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Koh, J.H.L., Daniel, B.K. Shifting online during COVID-19: A systematic review of teaching and learning strategies and their outcomes. Int J Educ Technol High Educ 19 , 56 (2022). https://doi.org/10.1186/s41239-022-00361-7

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Online learning during the Covid-19 pandemic: How university students’ perceptions, engagement, and performance are related to their personal characteristics

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Published: 20 March 2023
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Kai Kaspar ORCID: orcid.org/0000-0002-5092-6798 1 ,
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University students faced unexpected challenges in online learning during the Covid-19 pandemic. Findings from early phases of the Covid-19 pandemic and before show that online learning experiences may vary from student to student and depend on several personal characteristics. However, the relative importance of different students’ personal characteristics for their online learning experiences at later phases of the Covid-19 pandemic is still unclear. This cross-sectional, correlational study investigates how personal characteristics of university students are related to five dimensions of online learning perception and to their engagement and performance in online courses. In an online survey, 413 students from German universities provided full information on their online learning experiences and personal characteristics in terms of demographic information, Big Five personality traits, self-regulation skills, three facets of self-efficacy, and two types of state anxiety. Results of multiple regression analyses show that students’ age was significantly positively related to all online learning perceptions and engagement in online courses. Our findings also confirm that self-regulation skills and academic and digital media self-efficacy are important factors in various online learning experiences. In contrast, students’ personality traits and state anxiety were less important for most online learning experiences. Noteworthy, several bivariate associations between personal characteristics and online learning experiences are not reflected in the multiple regression model. This underscores the need to consider relevant variables simultaneously to evaluate their relative importance and to identify key personal characteristics. Overall, our results show valuable starting points for theory development and educational interventions.

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Introduction

The sudden change from on-campus to online learning due to the Covid-19 pandemic has disrupted students’ established study routines and social life. Indeed, the Covid-19 pandemic has forced university students to learn online, even in the absence of infrastructural or didactical requirements (e.g., Hoss et al., 2021 , 2022 ; Radu et al., 2020 ). Consequently, university students have been coping with several technological, educational, and psychological problems related to online learning that have increased since the start of the Covid-19 pandemic (Batdı et al., 2021 ). How well students adapt to such life changes can heavily rely on their personal characteristics (Caspi & Moffitt, 1993 ; Pinquart & Silbereisen, 2004 ). In line with this theoretical assumption, several studies have found that age, gender, and personality traits are related to how strongly people are worried, perceive risks for their life and society, and accept to follow protective measures to counter the Covid-19 pandemic (Kaspar & Nordmeyer, 2022 ; Zettler et al., 2022 ). In fact, 56% of the variance in online learning outcomes at the start of the Covid-19 pandemic could be explained based on university students’ personality traits, educational level, and gender (Yu, 2021 ). Personal characteristics including anxiety were also found to explain a substantial amount of variance (68%) in pre-service teachers’ intention to use digital tools (Rüth et al., 2022 ). More specifically, university students’ anxiety and personality traits were found to be related to their ability to cope with the transition from on-campus learning to online learning at the start of the Covid-19 pandemic (Besser et al., 2022 ). So, there is some evidence on the importance of personal characteristics regarding the initial phase of the Covid-19 pandemic (e.g., Besser et al., 2022 , Yu, 2021 ; Zettler et al., 2022 ), but the role of personal characteristics might be different in later phases of the pandemic beyond the initial emergency phase. Moreover, several studies that examined the importance of personal characteristics for online learning experiences focused on small sets of potentially relevant variables (e.g., Audet et al., 2021 ; Besser et al., 2022 ; Yu, 2021 ). To this end, more personal characteristics may be relevant to students’ online learning experiences, but their relative importance is not yet clear. Hence, a larger set of personal characteristics needs to be considered to unravel their relative importance for university students’ online learning experiences.

Online learning experiences can be manifold, and we here focus on students’ perceptions, engagement, and performance. Indeed, how students perceive digital learning was found to be strongly related to their engagement and performance in digital learning contexts before the Covid-19 pandemic (Rodrigues et al., 2019 ). Regarding online learning perceptions, there are mixed findings: Students were found to have critical issues with connectivity, accessibility of digital resources, and compatibility of related tools at the start of the Covid-19 pandemic (Agung et al., 2020 ). Still, survey findings from 2020 based on 1,904 Chilean university students suggest that they experienced online learning in the first semester after the start of the Covid-19 pandemic more positively than they had initially expected, but not as positive as face-to-face education (Lobos et al., 2021 ). In addition, results of a survey from 2021 indicate that most of the 1,800 U.S. university students surveyed would recommend online learning (83%) and considered learning online to be better than learning on campus (39%) (BestColleges, 2021 ). These overall positive perceptions could be related to moderate effects of online learning on academic success that were reported in more recent meta-analyses (Ulum, 2022 ), also when early phases of the Covid-19 pandemic were considered (Batdı et al., 2021 ). However, it is still unclear which role personal characteristics play in students’ perceptions, engagement, and performance related to online learning at later phases of the Covid-19 pandemic.

To address these research gaps, this correlational study investigates how university students’ online learning experiences were related to a broad set of personal characteristics when the pandemic was more advanced and online learning was no longer in its initial emergency mode.

Theoretical background and the current study

In principle, motivation psychology emphasizes that a person’s motivation to strive for a certain goal (such as engaging and performing well in an online learning setting) is modulated by personal and situational factors (Heckhausen, 2020 ). However, Caspi and Moffitt ( 1993 ) stated in their accentuation hypothesis that personal characteristics “are accentuated when environmental events disrupt previously existing social equilibria” (p. 247) and that personal characteristics “should predict behavior best in novel, ambiguous, and uncertain circumstances” (p. 267). This idea fits well with the abrupt and unpredictable changes necessitated by the Covid-19 pandemic, with its particularly strong impact on university students who were used to learning face-to-face on campus and who encountered unprepared infrastructure and missing didactics for online learning under pandemic conditions (see Hoss et al., 2021 , 2022 ). Moreover, that personal characteristics of the individual learner are relevant for online learning is suggested by social cognitive theory with an emphasis on the role of personal agency (Bandura, 2006 ), by models on the role of self-regulation and self-efficacy (Bradley et al., 2017 ) as well as personality traits and emotions in online learning environments (Fatahi et al., 2016 ), and by study results on the role of personality traits in online learning experiences at early phases of the Covid-19 pandemic (Besser et al., 2022 ; Yu, 2021 ). Therefore, we assumed that the personal characteristics of university students should show a significant relationship with their perceptions, engagement, and performance under learning conditions in a later phase of the Covid-19 pandemic. However, specific theories modeling the relative importance of personal characteristics for online learning experiences have been lacking. Accordingly, current research in this area is still in an exploratory phase and focuses on identifying particularly relevant factors, often using multiple regression models (e.g., Audet et al., 2021 ; Besser et al., 2022 ; Crisci et al., 2021 ; Jojoa et al., 2021 ; Yu, 2021 ). The present correlational study connects here, bringing together five key factors that have been identified as promising candidates in previous studies: demographic variables, the Big Five personality traits, self-regulation, self-efficacy, and anxiety. Figure 1 provides an overview of the research model and a detailed overview of all hypotheses that we derive in the following.

Research model of the present study visualizing the expected relations between students’ personal characteristics and their perceptions, engagement, and performance regarding online learning

The role of demographic variables in online learning experiences

Demographic variables that have been frequently studied regarding students’ intention to use digital technology are age, gender, and experience (Venkatesh et al., 2016 ). Age, gender, and study experience were also found to play a role in students’ online learning experiences (e.g., Diep et al., 2016 ; Rizvi et al., 2019 ; Yu, 2021 ), but results are mixed:

First, age did not play a significant role in online course performance according to some studies (Diep et al., 2016 ; Ke & Xie, 2009 ). Still, other studies found that older students showed higher engagement (Chyung, 2007 ; Ke & Kwak, 2013 ) and performance in online courses (Dibiase & Kidwai, 2010 ; Hoskins & van Hooff, 2005 ; Rizvi et al., 2019 ). Older students were also found to have better self-regulation skills and to follow a deeper approach to learning (Kizilcec et al., 2017 ; Richardson, 2013 ). Still, the role of age needs to be further examined with respect to online learning experiences (cf. Rizvi et al., 2019 ). In addition, other studies did not examine relations between age and online learning experiences in terms of perceptions, engagement, and performance. Here, we expected that students’ age is positively related to these online learning experiences (H1a).

Second, there are inconsistent findings regarding the role of gender in online learning (e.g., Rizvi et al., 2019 ; Yu, 2021 ). Meta-analytic findings suggest that male students have a more positive attitude toward technology use (Cai et al., 2017 ), but female students were found to be more engaged in online learning (Diep et al., 2016 ; Shahzad et al., 2021 ), to achieve higher exam scores than male students in online learning contexts (Chyung, 2007 ; McSporran & Young, 2001 ), and to be more satisfied with the first semester of online learning after the start of the Covid-19 pandemic (Lobos et al., 2021 ). Still, the role of gender in online learning perceptions as well as engagement and performance in online courses is underexplored. Based on previous results, we expected that female students had more positive online learning experiences than male students (H1b).

Third, students’ educational level was found to play a role in online course performance, yet the importance of prior education may decrease throughout course participation (Rizvi et al., 2019 ). Nevertheless, students with a higher educational degree were found to be less engaged and less satisfied in online learning according to some studies (Diep et al., 2016 ; Ke & Kwak, 2013 ), but also more satisfied and successful according to other studies (Li, 2019 ; Maki & Maki, 2003 ; Yu, 2021 ). Overall, study experience can be a relevant factor in online learning, but relations with perceptions, engagement, and performance regarding online learning during the Covid-19 pandemic yet need to be examined. We expected that students’ study experience in terms of their semester of study is related to their online learning experiences (H1c).

The role of personality traits in online learning experiences

In general, meta-analyses have found significant associations between personality in terms of the Big Five traits (McCrae & Costa, 2008 ) and academic success, with conscientiousness being the most important factor (Poropat, 2009 ; Vedel, 2014 ). More specifically, the Big Five have been related to some online learning experiences, also regarding early phases of the Covid-19 pandemic (cf. Morfaki & Skotis, 2022 ):

First, students high in extraversion are outgoing and have strong social skills, which was found to be positively related to motivation and satisfaction in online learning (Shih et al., 2013 ). Extraversion was found to have no relation to online performance (Abe, 2020 ), yet other studies found a negative relation to students’ exam results in online courses in early phases of the Covid-19 pandemic and before (Maki & Maki, 2003 ; Rivers, 2021 ; Yu, 2021 ). Still, students high in extraversion also showed better adaptability and more positive learning experiences in synchronous online learning at the start of the Covid-19 pandemic (Besser et al., 2022 ). Overall, we expected significant relations between extraversion and online learning experiences (H2a).

Second, neuroticism was found to be negatively related to students’ academic success (Bahçekapili & Karaman, 2020 ). Students high in neuroticism tend to experience negative emotions and are more vulnerable to emotional stress, which was related to lower perceived value of online learning (Watjatrakul, 2016 ) and online learning satisfaction in an early phase of the Covid-19 pandemic (Sahinidis et al., 2020 ). Students high in neuroticism were also found to adapt worse to the Covid-19 pandemic and to have more negative online learning experiences at the start of the Covid-19 pandemic (Besser et al., 2022 ). So, we expected that neuroticism is negatively related to online learning experiences (H2b).

Third, agreeableness was found to be positively related to students’ academic success in online learning in early phases of the Covid-19 pandemic (Rivers, 2021 ; Vlachogianni & Tselios, 2022 ; Yu, 2021 ). Agreeable students are polite and willing to compromise and cooperate, and they were found to see more value in online learning for their career (Keller & Karau, 2013 ). Students high in agreeableness also had high adaptability to the Covid-19 pandemic, which was related to more positive online learning experiences at the start of the Covid-19 pandemic (Besser et al., 2022 ). Thus, we expected a positive relation between agreeableness and online learning experiences (H2c).

Fourth, conscientiousness was found to be positively related to students’ academic success in online learning (Abe, 2020 ; Bahçekapili & Karaman, 2020 ; Rivers, 2021 ; Yu, 2021 ). Conscientious students are highly aspired to achieve goals, and it was found that they frequently use learning management systems (Alkış & Temizel, 2018 ), have positive impressions of online learning (Keller & Karau, 2013 ; Tavitiyam et al., 2021 ), and report more satisfaction with online learning in an early phase of the Covid-19 pandemic (Sahinidis et al., 2020 ). Moreover, students high in conscientiousness could adapt better to the Covid-19 pandemic and had better online learning experiences at the start of the Covid-19 pandemic (Besser et al., 2022 ). Accordingly, we expected a positive relation between conscientiousness and online learning experiences (H2d).

Finally, openness was found to be positively related to students’ academic success in online learning (Abe, 2020 ; Bahçekapili & Karaman, 2020 ; Yu, 2021 ). Students high in openness are thought to be more creative and to need diverse and novel experiences, which was positively related to students’ perceived value of online learning for their career (Keller & Karau, 2013 ), as well as to engagement and satisfaction regarding online learning in early phases of the Covid-19 pandemic (Audet et al., 2021 ; Sahinidis et al., 2020 ). Openness was also positively related to adaptability to the Covid-19 pandemic as well as to online learning experiences at the start of the Covid-19 pandemic (Besser et al., 2022 ). Here, we expected a positive relation between openness and online learning experiences (H2e).

The role of self-regulation skills in online learning experiences

Online learning can make students more aware of self-regulation skills, such as how to plan, control, and evaluate learning processes (Barak et al., 2016 ). In line with social cognitive theory, self-regulation skills of students were found to contribute to their success in online courses (Bradley et al., 2017 ; Broadbent & Poon, 2015 ), and to be positively related to students’ performance in online learning at the start of the Covid-19 pandemic (Anthonysamy, 2021 ). In this regard, a lack of self-regulation skills was found to be one of the most frequently named problems by students at the start of the Covid-19 pandemic (Hoss et al., 2021 ). However, relations between self-regulation skills and online learning perceptions as well as engagement and performance in online courses at a later stage of the Covid-19 pandemic are still unclear. We expected that students’ self-regulation skills are positively related to these online learning experiences (H3).

The role of self-efficacy in online learning experiences

Students high in self-efficacy take constructive approaches in life and believe in their abilities to successfully solve tasks and challenges, as suggested by social cognitive theory (Bandura, 2006 ). More specifically, students have been dealing with problems related to their academic life, learning online, and the related use of novel digital tools since the start of the Covid-19 pandemic (Batdı et al., 2021 ). Thus, it seems reasonable that academic and digital media-related abilities have been of particular importance for positive online learning experiences. Accordingly, we considered students’ general self-efficacy, but expected even stronger relations between online learning experiences and students’ academic as well as digital media self-efficacy. Indeed, previous studies indicate the relevance of these three factors: First, students with higher general self-efficacy were found to have more success in online courses (Bahçekapili & Karaman, 2020 ). Second, several studies reported that academic self-efficacy positively relates to academic success (Honicke & Broadbent, 2016 ; Rivers, 2021 ; Yokoyama, 2019 ). Third, self-efficacy regarding technology and the internet was found to be positively related to online learning perceptions, course satisfaction, and performance in online courses (Bradley et al., 2017 ; Wang et al., 2013 ; Wei & Chou, 2020 ). However, evidence is lacking on the relative importance of general self-efficacy, academic self-efficacy, and digital media self-efficacy for online learning experiences at a later phase of the Covid-19 pandemic. Based on previous results, we expected positive relations between students’ online learning experiences and their general self-efficacy (H4a), academic self-efficacy (H4b), and digital media self-efficacy (H4c).

The role of anxiety in online learning experiences

Anxiety is a negative emotion that can hamper cognitive performance in terms of lower engagement in tasks and information processing, as suggested by attentional control theory (Eysenck et al., 2007 ). In line with this assumption, more anxious students were found to have more negative perceptions of online learning and more negative educational experiences at the start of the Covid-19 pandemic (Jojoa et al., 2021 ; Zhao et al., 2021 ). Anxiety was higher in students during online learning at the start of the Covid-19 pandemic compared to face-to-face learning (Besser et al., 2022 ), but anxiety was also found to decrease with experience in online courses (Abdous, 2019 ). In this regard, it is of particular interest to find out how strongly anxiety is related to online learning experiences in later phases of the pandemic. In addition, it remains unclear whether negative online learning experiences are more strongly related to students’ anxiety in general or regarding the Covid-19 pandemic. Taken together, we examined these relations and expected that students’ online learning experiences are negatively related to their nonspecific state anxiety (H5a) and specific Covid-19 anxiety (H5b).

Participants and procedure

The minimum sample size was n = 194 for detecting a significant R 2 , based on a test power of 0.95, a significance level of 0.05, and a medium-sized effect of f² = 0.15. A medium-sized effect was expected based on the outlined theoretical relations between the personal characteristics and online learning experiences as well as on effect sizes from related studies on the role of personal characteristics such as personality traits in academic performance and online learning experiences (e.g., Morfaki & Skotis, 2022 ; Vedel, 2014 ). In total, 439 students from German universities participated in this online survey. We excluded 26 people from the analyses because of study at a distance university ( n = 13), study location abroad ( n = 9), early termination of the study ( n = 3), and one person who reported “diverse” as their gender, representing a too small subgroup to be reasonably included in gender-based analyses. The final sample thus consisted of 413 participants (354 female) who were between 18 and 61 years old ( M = 25.47, SD = 7.18). Most participants studied psychology and health-related subjects (community health, human medicine, physiotherapy, and ergotherapy). We invited participants via social media groups aimed at university students by means of convenience sampling and snowball sampling. At the beginning of the study, participants were informed that all students at German universities who were regularly enrolled in the winter semester 2020/2021 and who are at least 18 years old are eligible to participate. They were informed that all data will be collected anonymously and used for research purposes only. Participants who prematurely stopped the survey were not included in the analyses and all their data were deleted. Informed consent to participate in this study was provided by clicking a corresponding box, and participation was voluntary in all cases.

After the participants were informed about the study content and gave their consent, they provided demographic information. They then evaluated their perceptions, engagement, and performance regarding online learning and reported about their personal characteristics (see Fig. 1 ). The study ran from February 23 to April 26, 2021, so that pandemic-related online teaching and learning was by now an integral part of the study program and beyond the initial emergency phase. Data are available through Open Science Framework ( https://osf.io/td5ap/ ). All instruments in English were translated from English into German and vice versa to ensure that the translated version matches the original version (translation-back translation method) (Brislin, 1970 ; Maneesriwongul & Dixon, 2004 ).

Students’ evaluation of online learning experiences (dependent variables)

Online learning perceptions.

We used the Online Learning Perception Scale ( OLPS , Wei & Chou, 2020 ) to examine five dimensions of online learning perception: Accessibility represents students’ perception of free and unlimited access to online materials and resources (e.g., “Online learning provides various multimedia learning resources”, Cronbach’s α = 0.83). Interactivity refers to students’ perception of interactions with instructors (i.e., lecturers) and peers in the context of online learning (e.g., “Online learning enables me to interact directly with other learners”, α = 0.85). Adaptability refers to students’ perception of their own control over learning time, place, and process in online learning (e.g., “Online learning enables me to decide on the best time to learn”, α = 0.83). Knowledge acquisition addresses students’ perception that online learning promotes acquisition of knowledge in desired competence areas (e.g., “Online learning can broaden my common knowledge base”, α = 0.87). Ease of loading refers to students’ perception of reduced stress and burdens in online learning (e.g., “Online learning environments are less stressful”, α = 0.87). Content validity was ensured by two researchers with experience in online learning, and the five-factor structure was found via exploratory factor analyses (Wei & Chou, 2020 ). Online learning perceptions were found to be strongly positively related to students’ online learning readiness in different settings (convergent validity), and weakly related to teachers’ online learning readiness (discriminant validity) (Sarfraz et al., 2022 ; Wei & Chou, 2020 ). The 23 items are rated on a scale ranging from 1 ( strongly disagree ) to 5 ( strongly agree ).

Online course engagement

Students’ engagement in online courses was assessed by the corresponding subscale of the Online Course Impression instrument (Keller & Karau, 2013 ). The factorial structure of the instrument was not reported, yet it consists of face-valid items. Empirical findings indicate good construct validity, as student engagement was found to be positively related to conscientiousness, intrinsic motivation, and social presence (convergent validity), and negatively related to amotivation and external regulation (discriminant validity) (Baker & Moyer, 2019 ; Keller & Karau, 2013 ). The six items (e.g., “Online courses are very motivating to me” and “I find online courses engaging”, α = 0.84) are rated on a scale ranging from 1 ( strongly disagree ) to 5 ( strongly agree ).

Online course performance

Due to the lack of existing measures, we created an instrument measuring perceived performance in online university courses. We considered students’ individual self-evaluation, students’ self-evaluation based on feedback from instructors, and students’ self-evaluation based on feedback from fellow students. While these performance measures are more relevant in ungraded online learning contexts, students were also asked about their course grades considered the status quo performance measure in graded (online) learning contexts (cf. Poropat et al., 2009 ; Vedel, 2014 ). Accordingly, participants rated their performance via four items, namely “How would you rate your performance in online courses in general?”, “How would you rate your overall performance in online courses based on feedback from instructors?”, “How would you rate your overall performance in online courses based on student feedback?”, and “How would you rate your performance in online exams based on your grades overall?”. In sum, these items covered student performance in ungraded and graded online courses. Item wording was in German language and the scale’s internal consistency was very good (α = 0.87). The response scale has five steps according to the German grade system (1 = very good , 2 = good , 3 = satisfactory , 4 = sufficient , 5 = poor ). In order to facilitate interpretation of results, the scale was finally inverted so that higher values indicate a better performance.

Students’ personal characteristics (independent variables)

Personality traits.

Personality traits were assessed via the short version of the Big Five Inventory (Rammstedt & John, 2005 ). This is a standardized and economical instrument for applied settings with good construct validity. Its factorial structure was validated in both homogeneous student samples and larger heterogeneous samples using exploratory structural equation modelling (Kovaleva et al., 2013 ). Moreover, the relations between the five factors and age, gender, and education reported by Kovaleva et al. ( 2013 ) were consistent with previous results. The Big Five Inventory comprises 21 items (1 = disagree strongly , 5 = agree strongly ), measuring extraversion (e.g., “I get out of myself, I am sociable”, α = 0.84), neuroticism (e.g., “I get depressed easily, dejected”, α = 0.81), agreeableness (e.g., “I trust others easily, believe in the good in people”, α = 0.65), conscientiousness (e.g., “I complete tasks thoroughly”, α = 0.70), and openness (e.g., “I am interested in many things”, α = 0.71). Personality traits are measured by four to five items each.

Self-regulation skills

Self-regulation skills were assessed by the Self-Regulation Scale, an instrument with good predictive validity regarding academic performance and goal commitment as well as good construct validity (Diehl et al., 2006 ; Luszczynska et al., 2004 ): self-regulation skills show moderate to strong positive correlations with general self-efficacy, proactive coping, and positive affect (convergent validity), and low to moderate negative correlations with negative affect and depressive symptoms (discriminant validity). The scale comprises ten items (e.g., “I can concentrate on one activity for a long time, if necessary” and “After an interruption, I don’t have any problem resuming my concentrated style of working”, α = 0.84). We used this scale in its original German version (Diehl et al., 2006 ), with response options ranging from 1 ( not at all true ) to 4 ( completely true ).

Self-efficacy

General self-efficacy was measured by means of the validated short general self-efficacy scale (Beierlein et al., 2012 ). The scale’s content validity was ensured with the help of experts, and its factorial validity was evaluated by means of confirmatory factory analyses. The construct validity of the scale is good in terms of positive associations with another general self-efficacy scale and internal control beliefs (convergent validity), and in terms of negative associations with external control beliefs and neuroticism (discriminant validity). The scale covers three items (e.g., “In difficult situations I can rely on my abilities”, α = 0.83) and uses a 5-point format (1 = strongly disagree , 5 = strongly agree ).

Academic self-efficacy was assessed via seven items of the German Generalized Self-Efficacy Scale (Schwarzer & Jerusalem, 1995 ) adapted and shortened by Pumptow and Brahm ( 2021 ) (e.g., “I face difficulties in my studies calmly because I can rely on my coping abilities”, α = 0.91). Digital media self-efficacy was measured via seven items of the scale for digital media self-efficacy expectation (Pumptow & Brahm, 2021 ) (e.g., “It’s not difficult for me to reach the objectives I have associated with a media application”, α = 0.96). Both instruments have a unidimensional factorial structure according to exploratory factor analyses and show good construct validity (Pumptow & Brahm, 2021 ): Academic self-efficacy was found to be positively related to intrinsic motivation (convergent validity) and negatively related to anxiety when studying (discriminant validity); digital media self-efficacy was positively related to usage frequency of digital media and related digital skills (convergent validity), and negatively related to anxiety when studying (discriminant validity). Response scales range from 1 ( strongly disagree ) to 7 ( strongly agree ).

Nonspecific state anxiety was rated using the short version of the validated State-Trait Anxiety Inventory that shows good construct validity (Englert et al., 2011 ): Nonspecific state anxiety was found to be positively related to negative affect (convergent validity), and there was no significant association with positive affect (discriminant validity). Moreover, the factorial validity of the instrument was shown using confirmatory factor analysis. The instrument has five items (e.g., “I am worried that something might go wrong”, α = 0.89) and a response scale ranging from 1 ( not at all ) to 4 ( very ).

Covid-19 anxiety was measured via the validated Coronavirus Anxiety Scale (Lee, 2020a , b ). The content validity of the scale was ensured by formulating items that cover clinically relevant symptoms of fear and anxiety, and confirmatory factor analysis supports the scale’s unidimensional factorial structure. Further, there is good construct validity based on positive associations between Covid-19 anxiety and disability, distress, and negative coping mechanisms (convergent validity), and no association with history of anxiety (discriminant validity). The scale consists of five items (e.g., “I felt dizzy, lightheaded, or faint, when I read or listened to news about the coronavirus”, α = 0.85) and uses a response scale ranging from 0 ( not at all ) to 4 ( nearly every day over the last 2 weeks ). The response scale was re-scaled before the analyses for consistency reasons, using a range from 1 to 5.

Data analysis

First, we examined construct validity in terms of intercorrelations between personal characteristics (independent variables). Second, we analyzed the intercorrelations between online learning experiences (dependent variables). Third, we checked the expected relations between personal characteristics and online learning experiences (H1-H5) by means of multiple regression analyses for each dimension of online learning perception, for online course engagement, and for online course performance. All relevant statistical assumptions concerning multiple regression analysis were met (cf. Poole & O’Farrell, 1971 ): linearity, normality, and homoscedasticity, but no autocorrelation and no multicollinearity (VIF ≤ 2.61), and we have routinely used bootstrapping for inferential tests as suggested (cf. Hayes & Cai, 2007 ).

Intercorrelations between personal characteristics

As shown by Table 1 , intercorrelations between independent variables of the regression models were rather low with few remarkable exceptions (i.e., r ≥ .30): Neuroticism was negatively correlated with self-regulation skills ( r = –.47), general self-efficacy ( r = –.42) and academic self-efficacy ( r = –.53), but it was positively correlated with state anxiety ( r = .53) and Covid-19 anxiety ( r = .34). In contrast, conscientiousness was positively correlated with self-regulation skills ( r = .50), general self-efficacy ( r = .42), and academic self-efficacy ( r = .39). Self-regulation skills were also positively correlated with general ( r = .56), academic ( r = .52), and digital media self-efficacy ( r = .30), but negatively correlated with state anxiety ( r = –.48). In addition, general self-efficacy was positively correlated with academic ( r = .71) and digital media self-efficacy ( r = .36), but it had a negative correlation with state anxiety ( r = –.37). Academic self-efficacy had a positive correlation with digital media self-efficacy ( r = .39) but a negative correlation with state anxiety ( r = –.43). Finally, state anxiety and Covid-19 anxiety were positively correlated ( r = .35). Overall, these correlations indicate good construct validity.

Intercorrelations between online learning experiences

As shown by Table 2 , intercorrelations between the five dimensions of online learning perception were moderate to high ( r s ≥ 0.33), and the strongest correlations were between, on the one hand, knowledge acquisition and, on the other hand, accessibility ( r = .63), interactivity ( r = .61), and adaptability ( r = .61). Online course engagement also showed moderate to high correlations with dimensions of online learning perception ( r s ≥ 0.42) and online course performance ( r = .39). Finally, online course performance was moderately correlated with knowledge acquisition ( r = .32), whereas correlations with all other dimensions of online learning perception were only small to moderate (ranging from r = .18 to r = .26).

Relations between personal characteristics and online learning experiences

The central regression analyses revealed the joint contribution of all personal characteristics to online learning experiences. As shown by Table 3 , all personal characteristics explained a significant amount of variance in participants’ online learning perceptions (from 10 to 22% for the five dimensions, all p < .001), online course engagement (22%, p < .001), and online course performance (29%, p < .001). Importantly, when adjusting for multiple testing via Bonferroni correction for all dependent variables ( p = .007), the results of the corresponding multiple regression analyses (explained variance) remained statistically significant. At the level of individual factors (i.e., independent variables) we found the following pattern of results:

University students’ age was positively related to all online learning perceptions and online course engagement. In contrast, students in higher semesters of their studies were less engaged in online learning and evaluated the relieving effect of online learning lower (ease of loading). Some associations of age and semester of study were significant in the regression models but not on the level of bivariate correlations between independent and dependent variables (see Table 4 ). Gender did not show a bivariate correlation with any of the online learning experiences and gender was not a relevant factor in the multiple regression models, except that female (versus male) students perceived more own control over learning time, place, and process in online learning (adaptability).

Overall, students’ personality traits showed only few significant relations to their online learning experiences in the multiple regression models: Extraversion was negatively related to perceived adaptability of online learning and to students’ perception that online learning promotes acquisition of knowledge in desired competence areas (knowledge acquisition). In addition, extraversion showed a positive bivariate correlation with online course performance, but this relation was not significant in the multiple regression model. Neuroticism had negative bivariate correlations with all online learning experiences, these were significant for perceived interactivity with instructors and peers (interactivity), knowledge acquisition, ease of loading, and online course performance. Nevertheless, in the multiple regression models, neuroticism was positively related to students’ perception of free and unlimited access to online materials and resources (accessibility) and knowledge acquisition, as well as to online course engagement and performance. Agreeableness did not show associations with online learning experiences in the regression models and at the level of bivariate correlations. The multiple regression models revealed that conscientiousness was positively related to engagement and performance in online courses. At the level of bivariate correlations, conscientiousness was additionally significantly associated with all dimensions of online learning perception. Openness was only positively related to students’ perception of knowledge acquisition in the multiple regression model and in terms of a bivariate correlation. Additionally, positive bivariate correlations were found between openness and engagement and performance in online courses, but these relations did not reach significance in the regression models.

Students with higher self-regulation skills perceived more interactivity in online learning, and they reported higher engagement and performance in online courses in the multiple regression models. Positive relations to all other online learning perceptions were significant only at the bivariate correlation level. General self-efficacy showed positive bivariate correlations with all dependent variables except ease of loading, yet only the relation with students’ online course engagement was significant and negative in the multiple regression model. The more specific academic self-efficacy and digital media self-efficacy showed positive bivariate correlations with all online learning experiences. A different picture emerged for the multiple regression models: Academic self-efficacy was positively related to students’ perception of accessibility and knowledge acquisition as well as online course engagement and online course performance. Digital media self-efficacy was positively related to all students’ online learning perceptions and to online course engagement. At the same time, digital media self-efficacy was the most relevant independent variable in these domains (except ease of loading), as indicated by the standardized regression coefficients.

Finally, we found negative bivariate correlations between nonspecific state anxiety and all online learning experiences. In the multiple regression models, nonspecific state anxiety was substantially and negatively related to all online learning perceptions, except students’ perception of adaptability. However, no significant relations between nonspecific state anxiety and online course engagement and performance were visible in the multiple regression models. Interestingly, perceived adaptability was the only dimension of online learning perception that was significantly (negatively) related to the more specific Covid-19 anxiety. At the level of bivariate correlations, Covid-19 anxiety showed significant negative correlations with all online learning perceptions, but no significant correlation with online course engagement and performance. Overall, nonspecific state anxiety was rather low (mean value across all participants), and Covid-19 anxiety was even lower (see Table 1 ).

Notably, Table 4 substantiates the construct validity of the instruments as, for instance, students’ engagement and performance in online courses were positively related to conscientiousness, self-regulation skills, and facets of self-efficacy (convergent validity) and negatively related to nonspecific state anxiety (discriminant validity).

The Covid-19 pandemic has changed university students’ lives worldwide in negative and positive ways (Nowrouzi-Kia et al., 2022 ). Personal characteristics are thought to relate to how well students can adapt to such changes in their lives, and the focus of this study was to unravel the relations between students’ personal characteristics and their online learning experiences in a later phase of the Covid-19 pandemic when online learning had become an integral part of the study program. In this correlational study, we found that the online learning experiences students had gained over a period of up to one year after the start of the Covid-19 pandemic were significantly related to several of their personal characteristics. The joint consideration of demographic variables, personality traits, self-regulation skills, self-efficacy, and anxiety in the present research model explained a significant amount of variance in all five dimensions of online learning perception (10–22%), in online course engagement (22%), and in online course performance (29%). Thus, we found that a larger set of students’ personal characteristics played a significant role in their online learning experiences. Previous findings suggest that the explanatory value of a reduced model was even greater at the onset of the pandemic – at least with respect to online learning outcomes (Yu, 2021 ). One explanation for this difference, apart from cross-cultural and other study differences, could be that students have become used to the online learning scenario, so the role of personality characteristics may have diminished as learning conditions became more familiar (cf. Caspi & Moffitt, 1993 ; Pinquart & Silbereisen, 2004 ). Overall, our results extend the available evidence on the role of personal characteristics in online learning experiences in terms of perceptions, engagement, and performance.

With respect to the interrelation between students’ online learning experiences, we found moderate to strong associations between perceptions and engagement as well as between engagement and performance. These findings contribute to the ongoing need to better understand the associations between online learning perceptions and engagement (Rodrigues et al., 2019 ). Moreover, that perceptions and performance showed the weakest (but significant) intercorrelations is in line with previous work that did not find a significant association between online learning perceptions and scores in online courses (Wei & Chou, 2020 ). Based on this association pattern, we may assume that there is a greater distance between students’ online learning perceptions and self-rated online course performance, whereas self-rated online course engagement is closer to both aspects, reflected in higher correlations. This interesting finding might be a fruitful starting point for modeling the relationship between perceptions, engagement, and performance based on cognitive and behavioral proximity.

Before discussing the results in detail, we highlight three key findings from the multiple regression analyses central to this study, which simultaneously considered the relative importance of several personal characteristics of university students for their online learning experiences.

First, the most frequent positive associations between facets of online learning experiences and personal characteristics occurred with age and digital media self-efficacy, followed by academic self-efficacy and self-regulation skills. These results expand previously found relations between self-regulation skills and self-efficacy for online courses, internet use, and self-regulated learning (Bradley et al., 2017 ). In line with social cognitive theory, general, academic, and digital media self-efficacy also had strong positive associations with self-regulation skills. Overall, students who report higher self-regulation skills as well as academic and digital media self-efficacy seem to have more positive online learning experiences.

Second, regarding the Big Five personality traits, neuroticism had the most frequent significant positive associations with facets of online learning experiences, and only neuroticism and conscientiousness seem to play a significant role in online course engagement and performance. This expands previous findings by Besser et al. ( 2022 ) according to which neuroticism showed the most frequent and strongest relations to several facets of learning experiences in synchronous online learning in the initial phase of the Covid-19 pandemic. However, Besser et al. ( 2022 ) also observed many negative correlations between neuroticism and online learning experiences, whereas our regression analyses revealed positive associations between neuroticism and some online learning experiences. Noteworthy, also Audet et al. ( 2021 ) and Yu ( 2021 ) found some relations between the Big Five traits and facets of online learning experiences that differ from the result pattern of the present study. Importantly, these earlier studies only considered the Big Five or included few additional variables in their multiple regression models. In contrast, the set of personal characteristics was considerably expanded in the present study, so the relative importance of the Big Five must be interpreted on this broader basis. Moreover, the operationalization of the dependent variables differs between studies. Nonetheless, based on the results of the present study, we may conclude that the Big Five traits were of minor importance for university students’ online learning experiences during a later phase of the Covid-19 pandemic.

Third, the most frequent negative associations occurred between the dimensions of online learning perception and nonspecific state anxiety (rather than the more specific Covid-19 anxiety). In addition, we found that university students reported low levels of nonspecific state anxiety and Covid-19 anxiety. Previous work also found that anxiety was low to medium in university students (Besser et al., 2022 ; Zhao et al., 2021 ), and our findings show that Covid-19 anxiety was particularly low and of little importance to university students’ online learning experiences in a later phase of the Covid-19 pandemic.

The relevance of demographic variables for online learning experiences

Our findings suggest that age is a crucial factor that should be considered in online learning, whereas gender and semester of study seem to play a minor role.

First, the multiple regression analyses revealed that students’ age had significant positive relations to all five online learning perceptions. Older (versus younger) students reported more positive perceptions regarding accessibility of learning material, interactivity with others, adaptability of online learning, knowledge acquisition, and ease of loading. Further, older students reported a higher online course engagement, which corroborates previous findings (Cole et al., 2021 ). Still, age was not associated with higher self-regulation skills, as one might have expected considering the andragogical model of learning (cf. Dibiase & Kidwai, 2010 ) or since older students were found to have more self-regulation skills and follow a deeper approach to learning (Kizilcec et al., 2017 ; Richardson, 2013 ). Moreover, age was not significantly associated with reported online course performance. This contrasts with the finding that older students expected to be less successful in online learning during the pandemic (Hoss et al., 2022 ), and that age is thought to be an important factor in predicting grades in online course exams (Rizvi et al., 2019 ). In contrast to the multiple regression models, bivariate correlations between university students’ age and the different facets of online learning experiences were small and non-significant in several cases, indicating the danger of misjudging the relative importance of individual factors in the concert of many factors if only bivariate correlations are used. Overall, we found that older students had more positive online learning perceptions and reported higher engagement in online courses at a later stage of the Covid-19 pandemic.

Second, according to multiple regression analyses, female students reported higher perceived control over learning time, place, and process in online learning (adaptability). In contrast, self-regulation skills showed no significant association with gender, although female students were found to have higher self-regulation skills in previous works on online learning (Alghamdi et al., 2020 ; Li, 2019 ). A more general explanation for this relation between perceived adaptability and gender could be that female students were more willing to adapt to changing conditions in the context of the Covid-19 pandemic, as also reflected by higher acceptance and compliance with some protective measures (Kaspar & Nordmeyer, 2022 ; Zettler et al., 2022 ). In sum, gender did not play an important role in explaining inter-personal variance in online learning experiences during the Covid-19 pandemic, corroborating previous results (Abdullah et al., 2022 ; Harvey et al., 2017 ; Rizvi et al., 2019 ; Yu, 2021 ).

Third, semester of study was negatively related to ease of loading and online course engagement in the multiple regression models (but not at the bivariate correlation level). At the same time, older students stated that they had more positive experiences in terms of higher ease of loading and online course engagement. These contrasting findings indicate that it is important to distinguish between students’ age and the number of study-related opportunities to learn. With respect to performance in online courses, semester of study played no significant role. Overall, these results support previous work that reported more experienced students to be less engaged (Diep et al., 2016 ; Ke & Kwak, 2013 ), but contradict studies that found a positive association between students’ experience and performance (Li, 2019 ; Maki & Maki, 2003 ; Yu, 2021 ).

The different roles of personality traits in online learning experiences

The Big Five personality traits were of varying relevance in explaining inter-individual variance in students’ online learning experiences.

First, multiple regression analyses (but not the bivariate correlations) indicate that students high in extraversion perceived reduced own control over learning time, place, and process in online learning (adaptability) and reduced perception that online learning promotes knowledge acquisition in desired competence areas. Previous work indicated that students high in extraversion are able to adapt well to the pandemic and can have positive online learning experiences (Besser et al., 2022 ). In contrast, the results of the multiple regression model suggest that extraverted students had rather negative online learning perceptions. One explanation for these negative perceptions could be the quality of online learning: extraverted students were found to prefer more group work (Pavalache-Ilie & Cocorada, 2014 ) and people high in extraversion also reported lower motivation to continue computer-mediated communication in the future at the start of the Covid-19 pandemic (Meier et al., 2021 ). Accordingly, online learning may not have provided sufficient social interaction in the view of some (more extraverted) students. At the bivariate correlation level, extraversion showed a positive relationship with perceived performance in online courses, but in the multiple regression models, extraversion showed no significant relationship with performance as well as engagement in online courses, which corroborates previous findings (Abe, 2020 ) but also contrasts other findings (Maki & Maki, 2003 ; Rivers, 2021 ; Yu, 2021 ).

Second, when considered simultaneously with all other personal characteristics in the multiple regression model, students high in neuroticism reported higher engagement and performance in online courses. They also reported a stronger perception that online learning supports knowledge acquisition and that it provides access to free and unlimited learning materials and resources (accessibility). These findings are in contrast with our expectation and with previous results that suggested a negative relation between neuroticism and academic success (Bahçekapili & Karaman, 2020 ), perceived value of online learning (Watjatrakul, 2016 ), online learning satisfaction (Sahinidis et al., 2020 ), and online learning experiences (Besser et al., 2022 ). One explanation for our findings could be that people high in neuroticism are more engaged in information seeking online (Kaspar & Müller-Jensen, 2021 ) and that neuroticism is positively related to individuals’ tendency to express their self-aspects in online environments (Seidman, 2013 ). However, at the bivariate correlation level, neuroticism showed negative correlations with all facets of online learning experiences examined here, although not statistically significant in all cases, indicating that one or more variables in the regression models might have acted as suppressors. This result once again underscores the fact that considering individual variables in isolation from other factors can lead to incorrect assessments of the relative importance of the variables. In this sense, our findings expand previous work that did not find an association between neuroticism and how students evaluate online courses (Keller & Karau, 2013 ). Previous findings suggested that students high in neuroticism are less able to adapt to the Covid-19 pandemic and are more worried about related consequences (Besser et al., 2022 ; Zettler et al., 2022 ). Nevertheless, our findings indicate that higher levels of neuroticism were associated with more positive online learning experiences when other personal characteristics were considered simultaneously (multiple regression model).

Third, we found no significant relation between agreeableness and online learning experiences, neither at the level of bivariate correlations nor in the multiple regression model. By contrast, previous work found positive relations between agreeableness and online learning experiences via adaptability to the Covid-19 pandemic (Besser et al., 2022 ) as well as students’ engagement in online courses, their perceived value of online learning for their career, and their overall evaluation of online courses (Keller & Karau, 2013 ). Noteworthy, at the onset of the Covid-19 pandemic, agreeableness had the strongest association of all the Big Five with online learning outcomes in a multiple regression model (Yu, 2021 ), and was found to play a significant role in online course performance based on path analysis and hierarchical regression (Rivers, 2021 ; Vlachogianni & Tselios, 2022 ). Explanations for these differences from our results include that our multiple regression model was more complex in terms of significantly more variables, and that the importance of agreeableness for online learning decreased over the course of the pandemic. In sum, our results put the relative importance of agreeableness into a new perspective.

Fourth and as expected, more conscientious students reported higher engagement and performance in online courses. One explanation could be that people with higher conscientiousness were found to comply more with some Covid-19 restrictions (Krupić et al., 2021 ), and that better adaptability to the Covid-19 pandemic was positively related to online learning experiences (Besser et al., 2022 ). More generally, conscientiousness is more related to goal attainment than to compromises and was also found to be consistently related to students’ academic success in online learning (e.g., Abe, 2020 ; Bahçekapili & Karaman, 2020 ; Rivers, 2021 ; Yu, 2021 ). At the bivariate level, conscientiousness was also significantly positively correlated with all five dimensions of online learning perception. However, conscientiousness was of relatively low importance for online learning perceptions in the multiple regression models, which only partially supports that more conscientious students have more positive impressions of online learning (Keller & Karau, 2013 ; Tavitiyam et al., 2021 ). Taken together, our findings support the important role of conscientiousness in positive online learning experiences.

Finally, in the multiple regression model, students with higher openness only perceived higher value in how online learning supports knowledge acquisition. Despite possible differences across courses and universities, one might suspect that students with higher openness also valued the new experiences that online learning offered them, for example, in terms of different digital learning materials and online course designs. Still, we could not confirm the previously found positive associations between openness and online learning engagement (Audet et al., 2021 ) and online course performance (Abe, 2020 ; Bahçekapili & Karaman, 2020 ; Yu, 2021 ) in the multiple regression model, but we found positive associations at the bivariate correlation level. Importantly, these earlier findings are based on student experiences from the start of the pandemic or before, whereas online learning for students was less of a novel experience in our study. In principle, it could be that openness to (new) experiences as a trait played a more important role in online learning at the beginning of the pandemic than in its later phases. However, this possibility is very difficult to trace based on different cross-sectional studies at different points in time and with different (multiple regression) models. In fact, adding more variables to the model beyond the Big Five traits or replacing variables with other constructs can drastically change the relative importance of each factor because of their complex intercorrelations.

The benefits of self-regulation for online learning engagement and performance

Independent of a bivariate or multiple regression approach, we found that students with high self-regulation skills perceived online learning to be more interactive and reported higher engagement and performance in online courses. In fact, standardized regression coefficients revealed that self-regulation skills was the second strongest factor regarding engagement and performance in online courses, which corroborates that self-regulation skills are key for successful online learning (cf. Anthonysamy, 2021 ; Bradley et al., 2017 ). Self-regulation skills have been conceptualized and operationalized quite differently so far, for example, specifically with reference to self-regulated learning (Panadero, 2017 ). In contrast, our findings refer to more general self-regulation skills regarding attentional control and goal pursuit (Diehl et al., 2006 ). Hence, general self-regulation skills are important in the context of online learning, although a causal interpretation of this relationship cannot be derived from the present correlative data.

The benefits of different facets of self-efficacy for online learning experiences

General, academic, and digital media self-efficacy showed consistently (with only one exception) significant positive correlations with all facets of online learning experiences at the bivariate level. Looking at the multiple regression models, however, the picture was somewhat different: University students’ general self-efficacy was positively associated with engagement in online courses, but this association was negative in the context of the multiple regression model. In this regard, self-efficacy and performance were found to be negatively associated when students were overconfident in their performance in using digital technology (Moores & Chang, 2009 ). Hence, while our findings are only correlational, one explanation could be that students with high confidence in their abilities show less engagement in online courses.

University students’ performance in online courses was also positively associated with general self-efficacy, but not significantly in case of the multiple regression model. This finding partially contrasts that higher general self-efficacy relates to better performance in online courses (Bahçekapili & Karaman, 2020 ; Bradley et al., 2017 ). However, only students’ academic self-efficacy was significantly positively related to perceived performance in online courses, and their digital media self-efficacy was positively related to all five online learning perceptions. These findings emphasize the key role of academic self-efficacy in learning successfully online and on campus (cf. Honicke & Broadbent, 2016 ; Yokoyama, 2019 ), and are in line with a positive association between digital media self-efficacy and self-assessed skills in digital learning applications (Pumptow & Brahm, 2021 ). In addition, these findings illustrate the significance of considering more specific facets of self-efficacy that are directly related to required skills. Overall, academic and digital media self-efficacy are of particular importance for online learning experiences.

The role of anxiety in online learning perceptions

University students’ anxiety showed negative bivariate correlations with all facets of online learning experiences, being significant in most cases. In contrast, in the multiple regression models, students’ anxiety only played a significant role in explaining online learning perceptions but not online course engagement and performance. Nonspecific state anxiety showed significant negative relations to all dimensions of online learning perception, except adaptability. Adaptability was instead the only online learning experience that was significantly negatively related to the more specific Covid-19 anxiety. Noteworthy, students in our sample had up to a year of experience with online learning in the context of the Covid-19 pandemic, and anxiety was found to decrease with experience in online courses (Abdous, 2019 ). In sum, anxiety was negatively related to perceptions of online learning but not associated with worse performance in online courses, which is in line with other works (Abe, 2020 ; Jojoa et al., 2021 ). Hence, the importance of specific Covid-19 anxiety appears negligible, whereas the role of nonspecific state anxiety in perceptions of online learning could be of more general importance.

Limitations and future research

Our findings may be associated with some limitations. First, our data are cross-sectional so that experimental and longitudinal research is needed to disentangle causal influences of personal characteristics on online learning experiences. In this regard, personal characteristics may lead to certain experiences and experiences could change personal characteristics (cf. Specht et al., 2011 ). Second, the available data do not allow to determine whether age itself is a relevant variable or rather other factors that might be related to age, such as underlying differences in using technology or a larger portfolio of general learning strategies. Still, we found that age broadly contributed to explaining several online learning perceptions and online course engagement, and that at least the semester of study and the associated subject-specific knowledge level was not the decisive factor. Third, a distinction could have been made between students who study entirely online and students who take some courses back on campus. However, our focus was to gain an overall picture of online learning during the Covid-19 pandemic. Fourth, regarding our measurement instruments, internal consistency was rather poor for the agreeableness scale (α = 0.65), but good to very good for all other scales (α ≥ 0.70). In addition, our data are based on self-reports, so estimates by third parties (e.g., fellow students or instructors) of students’ actual engagement and performance in online courses could be different. Nevertheless, our results provide important evidence of relevant variables in the context of online learning that could be targeted or, at a minimum, should be considered in terms of different learning trajectories when designing learning opportunities. In this regard, the minor role of personality traits allows future research to limit the focus to the core variables we identified: self-regulation skills, academic self-efficacy, and digital media self-efficacy. Moreover, we may speculate that some of the relations found here are not unique to the Covid-19 pandemic situation. Concerning future research, the amount of explained variance suggests that we missed some constructs that play a role in students’ online learning experiences. For instance, according to the general extended motivational model, personal and situational factors as well as person-situation interactions can motivate or constrain human development and action (Heckhausen, 2020 ). Moreover, online learning experiences during the Covid-19 pandemic may have included situational, organizational, and interpersonal aspects, such as interactions that are not authentic and of low quality (Niemi & Kousa, 2020 ; Tzankova et al., 2022 ). Overall, this exploratory study focused on role of personal characteristics in online learning experiences in terms of explained variance and relative importance, whereas future research could develop and test theoretical assumptions against specific models.

Practical implications

Attitudes towards online learning have become positive across higher education institutions compared to the start the Covid-19 pandemic and before (Bay View Analytics, 2021 ; Lobos et al., 2021 ). Relatedly, infrastructural conditions and educational resources will remain subject to permanent change and technological development (e.g., European Commission, 2022 ; OECD, 2021 ). Concerning decisions between online learning and on-campus learning, meta-analytic findings suggest that students who participate in online learning outperform students who learn on campus (e.g., Ebner & Gegenfurtner, 2019 ; Means et al., 2013 ). In this context, it will remain crucial to investigate how students perceive and experience the quality of online learning. Hence, the core variables we identified regarding online learning experiences should not only be further investigated but also considered and fostered in practice. Self-regulation skills, academic self-efficacy, and digital media self-efficacy could be addressed, for instance, by means of video lectures, quiz games, or other digital tools for self-study and collaborative learning (e.g., Jansen et al., 2020 ; Pérez-Álvarez et al., 2018 ; Rüth et al., 2021 ). Moreover, training programs were found to have a positive small-to-moderate effect on university students’ self-regulation skills, such as metacognitive, resource management, and cognitive strategies, as well as on academic performance and motivational outcomes (Theobald, 2021 ). In addition, students’ self-efficacy could be fostered by provision of more elaborate feedback, social interactions in online learning, and motivational mechanisms (Peechapol et al., 2018 ). In contrast, it seems less effective to consider personal characteristics that were of minor importance in our study, such as to design online learning environments based on students’ gender (cf. Yu, 2021 ). Instead, our results support taking a more general approach that saves resources in terms of development and distribution of online learning environments (Harvey et al., 2017 ). In this regard, the overall online learning experience can be improved by considering didactical and technological relations, referencing to theoretical frameworks, and addressing changes and barriers in educational institutions (e.g., Rodrigues et al., 2019 ; Rüth & Kaspar, 2017 ).

Changes in university students’ academic and social life such as the Covid-19 pandemic can emphasize the role of their personal characteristics in coping with these situations. However, the assessment of the relevance of individual characteristics should not take place in isolation from other factors, as the relative importance of the factors only becomes apparent when they are considered simultaneously in basic or advanced research models. Following this approach, we found that the age of students plays a key role in their online learning experiences, with older students having more positive perceptions of online learning and being more engaged in online courses. This suggests putting a stronger focus on the needs, expectations, and experiences of younger students in online learning. In addition, students seem to be more engaged and successful in online courses when they have high self-regulation skills and academic self-efficacy. Engagement in online courses can also be higher when students believe in their abilities related to digital media. In contrast, students’ personality traits played a rather subordinate role in their online learning experiences in a later phase of the Covid-19 pandemic, beyond the initial emergency online learning phase. Moreover, nonspecific state anxiety is negatively related to online learning perceptions. To conclude, online learning has become a common part of higher education, and more positive online learning experiences are related to key malleable personal characteristics of students, specifically self-regulation skills and academic and digital media self-efficacy.

Data Availability

Data are available through Open Science Framework ( https://osf.io/td5ap/ ).

References

Abdous, M. H. (2019). Influence of satisfaction and preparedness on online students’ feelings of anxiety. The Internet and Higher Education , 41 , 34–44. https://doi.org/10.1016/j.iheduc.2019.01.001

Article Google Scholar

Abdullah, S. I. N. W., Arokiyasamy, K., Goh, S. L., Culas, A. J., & Manaf, N. M. A. (2022). University students’ satisfaction and future outlook towards forced remote learning during a global pandemic. Smart Learning Environments , 9 , 15. https://doi.org/10.1186/s40561-022-00197-8

Article PubMed Central Google Scholar

Abe, J. A. A. (2020). Big five, linguistic styles, and successful online learning. The Internet and Higher Education , 45 , 100724. https://doi.org/10.1016/j.iheduc.2019.100724

Agung, A. S. N., Surtikanti, M. W., & Quinones, C. A. (2020). Students’ perception of online learning during COVID-19 pandemic: a case study on the english students of STKIP Pamane Talino. SOSHUM: Jurnal Sosial Dan Humaniora , 10 , 225–235. https://doi.org/10.31940/soshum.v10i2.1316

Alghamdi, A., Karpinski, A. C., Lepp, A., & Barkley, J. (2020). Online and face-to-face classroom multitasking and academic performance: Moderated mediation with self-efficacy for self-regulated learning and gender. Computers in Human Behavior , 102 , 214–222. https://doi.org/10.1016/j.chb.2019.08.018

Alkış, N., & Temizel, T. T. (2018). The impact of motivation and personality on academic performance in online and blended learning environments. Journal of Educational Technology & Society , 21 , 35–47. https://www.jstor.org/stable/26458505

Google Scholar

Anthonysamy, L. (2021). The use of metacognitive strategies for undisrupted online learning: preparing university students in the age of pandemic. Education and Information Technologies , 26 , 6881–6899. https://doi.org/10.1007/s10639-021-10518-y

Article PubMed PubMed Central Google Scholar

Audet, É. C., Levine, S. L., Metin, E., Koestner, S., & Barcan, S. (2021). Zooming their way through university: which big 5 traits facilitated students’ adjustment to online courses during the COVID-19 pandemic. Personality and Individual Differences , 180 , 110969. https://doi.org/10.1016/j.paid.2021.110969

Bahçekapili, E., & Karaman, S. (2020). A path analysis of five-factor personality traits, self-efficacy, academic locus of control and academic achievement among online students. Knowledge Management & E-Learning , 12 , 191–208. https://doi.org/10.34105/j.kmel.2020.12.010

Baker, K. Q., & Moyer, D. M. (2019). The relationship between students’ characteristics and their impressions of online courses. American Journal of Distance Education , 33 , 16–28. https://doi.org/10.1080/08923647.2019.1555301

Bandura, A. (2006). Toward a psychology of human agency. Perspectives on Psychological Science , 1 , 164–180. https://doi.org/10.1111/j.1745-6916.2006.00011.x

Article PubMed Google Scholar

Barak, M., Hussein-Farraj, R., & Dori, Y. J. (2016). On-campus or online: examining self-regulation and cognitive transfer skills in different learning settings. International Journal of Educational Technology in Higher Education , 13 , 1–18. https://doi.org/10.1186/s41239-016-0035-9

Batdı, V., Doğan, Y., & Talan, T. (2021). Effectiveness of online learning: a multi-complementary approach research with responses from the COVID-19 pandemic period. Interactive Learning Environments , 1–34. https://doi.org/10.1080/10494820.2021.1954035

Bay View Analytics (2021). Teaching During a Pandemic. Spring Transition, Fall Continuation, Winter Evaluation https://www.bayviewanalytics.com/reports/teachingduringapandemic.pdf

Beierlein, C., Kovaleva, A., Kemper, C. J., & Rammstedt, B. (2012). Ein Messinstrument zur Erfassung subjektiver Kompetenzerwartungen: Allgemeine Selbstwirksamkeit Kurzskala (ASKU) [A measurement tool to assess subjective expectations of competence: General Self-Efficacy Short Scale (ASKU)]. GESIS-Working Papers . https://nbn-resolving.org/urn:nbn:de:0168-ssoar-292351

Besser, A., Flett, G. L., & Zeigler-Hill, V. (2022). Adaptability to a sudden transition to online learning during the COVID-19 pandemic: understanding the challenges for students. Scholarship of Teaching and Learning in Psychology , 8 , 85–105. https://doi.org/10.1037/stl0000198

BestColleges (2021). 2021 Online Education Trends Report . https://www.bestcolleges.com/app/uploads/2021/07/2021-Online-Trends-in-Education-Report-BestColleges.pdf

Bradley, R. L., Browne, B. L., & Kelley, H. M. (2017). Examining the influence of self-efficacy and self-regulation in online learning. College Student Journal , 51 , 518–530. https://www.ingentaconnect.com/content/prin/csj/2017/00000051/00000004/art00008

Brislin, R. W. (1970). Back-translation for cross-cultural research. Journal of cross-cultural Psychology , 1 , 185–216. https://doi.org/10.1177/135910457000100301

Broadbent, J., & Poon, W. L. (2015). Self-regulated learning strategies & academic achievement in online higher education learning environments: a systematic review. The Internet and Higher Education , 27 , 1–13. https://doi.org/10.1016/j.iheduc.2015.04.007

Cai, Z., Fan, X., & Du, J. (2017). Gender and attitudes toward technology use: a meta-analysis. Computers & Education , 105 , 1–13. https://doi.org/10.1016/j.compedu.2016.11.003

Caspi, A., & Moffitt, T. E. (1993). When do individual differences matter? A paradoxical theory of personality coherence. Psychological Inquiry , 4 , 247–271. https://doi.org/10.1207/s15327965pli0404_1

Chyung, S. Y. Y. (2007). Age and gender differences in online behavior, self-efficacy, and academic performance. Quarterly Review of Distance Education , 8 , 213–222. https://www.learntechlib.org/p/106649/

Cole, A. W., Lennon, L., & Weber, N. L. (2021). Student perceptions of online active learning practices and online learning climate predict online course engagement. Interactive Learning Environments , 29 , 866–880. https://doi.org/10.1080/10494820.2019.1619593

Crisci, G., Mammarella, I. C., Moscardino, U. M., Roch, M., & Thorell, L. B. (2021). Distance Learning Effects among Italian Children and Parents during COVID-19 Related School Lockdown. Frontiers in Psychiatry , 12 , 782353. https://doi.org/10.3389/fpsyt.2021.782353

Dibiase, D., & Kidwai, K. (2010). Wasted on the young? Comparing the performance and attitudes of younger and older US adults in an online class on geographic information: JGHE annual lecture. Journal of Geography in Higher Education , 34 , 299–326. https://doi.org/10.1080/03098265.2010.490906

Diehl, M., Semegon, A. B., & Schwarzer, R. (2006). Assessing attention control in goal pursuit: a component of dispositional self-regulation. Journal of Personality Assessment , 86 , 306–317. https://doi.org/10.1207/s15327752jpa8603_06

Diep, N. A., Cocquyt, C., Zhu, C., & Vanwing, T. (2016). Predicting adult learners’ online participation: Effects of altruism, performance expectancy, and social capital. Computers & Education , 101 , 84–101. https://doi.org/10.1016/j.compedu.2016.06.002

Ebner, C., & Gegenfurtner, A. (2019). Learning and satisfaction in webinar, online, and face-to-face instruction: a meta-analysis. Frontiers in Education , 4 , 92. https://doi.org/10.3389/feduc.2019.00092

Englert, C., Bertrams, A., & Dickhäuser, O. (2011). Entwicklung der Fünf-Item-Kurzskala STAI-SKD zur Messung von Zustandsangst [Development of the five-item short scale STAI-SKD for the assessment of state anxiety]. Zeitschrift für Gesundheitspsychologie , 19 , 173–180. https://doi.org/10.1026/0943-8149/a000049

European Commission (2022). Digital Education Action Plan (2021 – 2027) https://education.ec.europa.eu/focus-topics/digital/education-action-plan

Eysenck, M. W., Derakshan, N., Santos, R., & Calvo, M. G. (2007). Anxiety and cognitive performance: attentional control theory. Emotion , 7 , 336–353. https://doi.org/10.1037/1528-3542.7.2.336

Fatahi, S., Moradi, H., & Kashani-Vahid, L. (2016). A survey of personality and learning styles models applied in virtual environments with emphasis on e-learning environments. Artificial Intelligence Review , 46 , 413–429. https://doi.org/10.1007/s10462-016-9469-7

Harvey, H. L., Parahoo, S., & Santally, M. (2017). Should gender differences be considered when assessing student satisfaction in the online learning environment for millennials? Higher Education Quarterly , 71 , 141–158. https://doi.org/10.1111/hequ.12116

Hayes, A. F., & Cai, L. (2007). Using heteroskedasticity-consistent standard error estimators in OLS regression: an introduction and software implementation. Behavior Research Methods , 39 , 709–722. https://doi.org/10.3758/BF03192961

Heckhausen, J. (2020). Integrating and instigating research on person and situation, motivation and volition, and their development. Motivation Science , 6 , 185–188. https://doi.org/10.1037/mot0000166

Honicke, T., & Broadbent, J. (2016). The influence of academic self-efficacy on academic performance: a systematic review. Educational Research Review , 17 , 63–84. https://doi.org/10.1016/j.edurev.2015.11.002

Hoskins, S. L., & van Hooff, J. C. (2005). Motivation and ability: which students use online learning and what influence does it have on their achievement? British Journal of Educational Technology , 36 , 177–192. https://doi.org/10.1111/j.1467-8535.2005.00451.x

Hoss, T., Ancina, A., & Kaspar, K. (2021). Forced remote learning during the COVID-19 pandemic in Germany: a mixed-methods study on students’ positive and negative expectations. Frontiers in Psychology , 12 , 642616. https://doi.org/10.3389/fpsyg.2021.642616

Hoss, T., Ancina, A., & Kaspar, K. (2022). German University Students’ perspective on remote learning during the COVID-19 pandemic: a quantitative Survey Study with Implications for Future Educational Interventions. Frontiers in Psychology , 13 , 734160. https://doi.org/10.3389/fpsyg.2022.734160

Jansen, R. S., van Leeuwen, A., Janssen, J., Conijn, R., & Kester, L. (2020). Supporting learners’ self-regulated learning in massive Open Online Courses. Computers & Education , 146 , 103771. https://doi.org/10.1016/j.compedu.2019.103771

Jojoa, M., Lazaro, E., Garcia-Zapirain, B., Gonzalez, M. J., & Urizar, E. (2021). The impact of COVID 19 on university staff and students from Iberoamerica: online learning and teaching experience. International Journal of Environmental Research and Public Health , 18 , 5820. https://doi.org/10.3390/ijerph18115820

Kaspar, K., & Müller-Jensen, M. (2021). Information seeking behavior on Facebook: the role of censorship endorsement and personality. Current Psychology , 40 , 3848–3859. https://doi.org/10.1007/s12144-019-00316-8

Kaspar, K., & Nordmeyer, L. (2022). Personality and motivation to Comply with COVID-19 Protective Measures in Germany. Frontiers in Psychology , 13 , 893881. https://doi.org/10.3389/fpsyg.2022.893881

Ke, F., & Kwak, D. (2013). Online learning across ethnicity and age: a study on learning interaction participation, perception, and learning satisfaction. Computers & Education , 61 , 43–51. https://doi.org/10.1016/j.compedu.2012.09.003

Ke, F., & Xie, K. (2009). Toward deep learning for adult students in online courses. The Internet and Higher Education , 12 , 136–145. https://doi.org/10.1016/j.iheduc.2009.08.001

Keller, H., & Karau, S. J. (2013). The importance of personality in students’ perceptions of the online learning experience. Computers in Human Behavior , 29 , 2494–2500. https://doi.org/10.1016/j.chb.2013.06.007

Kizilcec, R. F., Pérez-Sanagustín, M., & Maldonado, J. J. (2017). Self-regulated learning strategies predict learner behavior and goal attainment in massive Open Online Courses. Computers & Education , 104 , 18–33. https://doi.org/10.1016/j.compedu.2016.10.001

Kovaleva, A., Beierlein, C., Kemper, C. J., & Rammstedt, B. (2013). Psychometric properties of the BFI-K: a cross-validation study. The International Journal of Educational and Psychological Assessment , 13 , 34–50.

Krupić, D., Žuro, B., & Krupić, D. (2021). Big Five traits, approach-avoidance motivation, concerns and adherence with COVID-19 prevention guidelines during the peak of pandemic in Croatia. Personality and Individual Differenc es, 179 , 110913. https://doi.org/10.1016/j.paid.2021.110913

Lee, S. A. (2020a). Coronavirus anxiety scale: a brief mental health screener for COVID-19 related anxiety. Death Studies , 44 , 393–401. https://doi.org/10.1080/07481187.2020.1748481

Lee, S. A. (2020b). Replication analysis of the coronavirus anxiety scale. Dusunen Adam: The Journal of Psychiatry and Neurological Sciences , 33 , 203–205. https://doi.org/10.14744/DAJPNS.2020.00079

Li, K. (2019). MOOC learners’ demographics, self-regulated learning strategy, perceived learning and satisfaction: a structural equation modeling approach. Computers & Education , 132 , 16–30. https://doi.org/10.1016/j.compedu.2019.01.003

Lobos, K., Cobo-Rendón, R., Mella-Norambuena, J., Maldonado-Trapp, A., & Jofré, B. (2021). Expectations and Experiences with Online Education during the COVID-19 Pandemic in University Students. Frontiers in Psychology , 12 . https://doi.org/10.3389/fpsyg.2021.815564

Luszczynska, A., Diehl, M., Gutiérrez-Dona, B., Kuusinen, P., & Schwarzer, R. (2004). Measuring one component of dispositional self-regulation: attention control in goal pursuit. Personality and Individual Differences , 37 , 555–566. https://doi.org/10.1016/j.paid.2003.09.026

Maki, R. H., & Maki, W. S. (2003). Prediction of learning and satisfaction in web-based and lecture courses. Journal of Educational Computing Research , 28 , 197–219. https://doi.org/10.2190/DXJU-7HGJ-1RVP-Q5F2

Maneesriwongul, W., & Dixon, J. K. (2004). Instrument translation process: a methods review. Journal of Advanced Nursing , 48 , 175–186. https://doi.org/10.1111/j.1365-2648.2004.03185.x

McCrae, R. R., & Costa, P. T. Jr. (2008). The five-factor theory of personality. In O. P. John, R. W. Robins, & L. A. Pervin (Eds.), Handbook of personality: theory and research (pp. 159–181). The Guilford Press.

McSporran, M., & Young, S. (2001). Does gender matter in online learning? ALT-J: Research in Learning Technology , 9 , 3–15. https://doi.org/10.1080/0968776010090202

Means, B., Toyama, Y., Murphy, R., & Baki, M. (2013). The effectiveness of online and blended learning: a meta-analysis of the empirical literature. Teachers College Record , 115 , 1–47. https://doi.org/10.1177/016146811311500307

Meier, J. V., Noel, J. A., & Kaspar, K. (2021). Alone together: computer-mediated communication in leisure time during and after the COVID-19 pandemic. Frontiers in Psychology , 12 , 666655. https://doi.org/10.3389/fpsyg.2021.666655

Moores, T. T., & Chang, J. C. J. (2009). Self-efficacy, overconfidence, and the negative effect on subsequent performance: a field study. Information & Management , 46 , 69–76. https://doi.org/10.1016/j.im.2008.11.006

Morfaki, C., & Skotis, A. (2022). Academic online learning experience during COVID-19-a systematic literature review based on personality traits. Higher Education, Skills and Work-Based Learning . https://doi.org/10.1108/HESWBL-03-2022-0062

Niemi, H. M., & Kousa, P. (2020). A case study of students’ and teachers’ perceptions in a finnish high school during the COVID pandemic. International Journal of Technology in Education and Science (IJTES) , 4 , 352–369. https://doi.org/10.46328/ijtes.v4i4.167

Nowrouzi-Kia, B., Osipenko, L., Eftekhar, P., Othman, N., Alotaibi, S., Schuster, A. M., Suh, H. S., & Duncan, A. (2022). The early impact of the global lockdown on post-secondary students and staff: a global, descriptive study. SAGE Open Medicine . https://doi.org/10.1177/20503121221074480

OECD (2021). OECD Digital Education Outlook 2021 https://doi.org/10.1787/589b283f-en

Panadero, E. (2017). A review of self-regulated learning: six models and four directions for research. Frontiers in Psychology , 8 , 422. https://doi.org/10.3389/fpsyg.2017.00422

Pavalache-Ilie, M., & Cocorada, S. (2014). Interactions of students’ personality in the online learning environment. Procedia – Social and Behavioral Sciences , 128 , 117–122. https://doi.org/10.1016/j.sbspro.2014.03.128

Peechapol, C., Na-Songkhla, J., Sujiva, S., & Luangsodsai, A. (2018). An exploration of factors influencing self-efficacy in Online Learning: a systematic review. International Journal of Emerging Technologies in Learning , 13 , 64–86. https://doi.org/10.3991/ijet.v13i09.8351

Pérez-Álvarez, R., Maldonado-Mahauad, J., & Pérez-Sanagustín, M. (2018). Tools to support self-regulated learning in online environments: Literature review. In V. Pammer-Schindler, M. Pérez-Sanagustín, H. Drachsler, R. Elferink, & M. Scheffel (Eds.) Lifelong Technology-Enhanced Learning. EC-TEL 2018. Lecture Notes in Computer Science (Vol. 11082, pp. 16–30). Springer. https://doi.org/10.1007/978-3-319-98572-5_2

Pinquart, M., & Silbereisen, R. (2004). Human development in times of social change: theoretical considerations and research needs. International Journal of Behavioral Development , 28 , 289–298. https://doi.org/10.1080/01650250344000406

Poole, M. A., & O‘Farrell, P. N. (1971). The assumptions of the linear regression model. Transactions of the Institute of British Geographers , 52 , 145–158. https://doi.org/10.2307/621706

Poropat, A. E. (2009). A meta-analysis of the five-factor model of personality and academic performance. Psychological Bulletin , 135 , 322–338. https://doi.org/10.1037/a0014996

Pumptow, M., & Brahm, T. (2021). Students’ digital media self-efficacy and its importance for higher education institutions: development and validation of a survey instrument. Technology Knowledge and Learning , 26 , 555–575. https://doi.org/10.1007/s10758-020-09463-5

Radu, M. C., Schnakovszky, C., Herghelegiu, E., Ciubotariu, V. A., & Cristea, I. (2020). The impact of the COVID-19 pandemic on the quality of educational process: a student survey. International Journal of Environmental Research and Public Health , 17 , 7770. https://doi.org/10.3390/ijerph17217770

Rammstedt, B., & John, O. P. (2005). Kurzversiondes Big Five Inventory (BFI-K): Entwicklung und Validierung eines ökonomischen Inventars zur Erfassung der fünf Faktoren der Persönlichkeit. [Short version of the Big Five Inventory (BFI-K): Development and validation of an economic inventory for assessment of the five factors of personality]. Diagnostica , 51 , 195–206. https://doi.org/10.1026/0012-1924.51.4.195

Richardson, J. T. (2013). Approaches to studying across the adult life span: evidence from distance education. Learning and Individual Differences , 26 , 74–80. https://doi.org/10.1016/j.lindif.2013.04.012

Rivers, D. J. (2021). The role of personality traits and online academic self-efficacy in acceptance, actual use and achievement in Moodle. Education and Information Technologies , 26 , 4353–4378. https://doi.org/10.1007/s10639-021-10478-3

Rizvi, S., Rienties, B., & Khoja, S. A. (2019). The role of demographics in online learning; a decision tree based approach. Computers & Education , 137 , 32–47. https://doi.org/10.1016/j.compedu.2019.04.001

Rodrigues, H., Almeida, F., Figueiredo, V., & Lopes, S. L. (2019). Tracking e-learning through published papers: a systematic review. Computers & Education , 136 , 87–98. https://doi.org/10.1016/j.compedu.2019.03.007

Rüth, M., & Kaspar, K. (2017). The E-Learning setting Circle: first steps toward theory development in e-learning research. The Electronic Journal of e-Learning , 15 , 94–103. https://academic-publishing.org/index.php/ejel/article/view/1822

Rüth, M., Breuer, J., Zimmermann, D., & Kaspar, K. (2021). The effects of different feedback types on learning with mobile quiz apps. Frontiers in Psychology , 12 , 665144. https://doi.org/10.3389/fpsyg.2021.665144

Rüth, M., Birke, A., & Kaspar, K. (2022). Teaching with digital games: how intentions to adopt game-based learning are related to personal characteristics of pre-service teachers. British Journal of Educational Technology , 53 , 1412–1429. https://doi.org/10.1111/bjet.13201

Sahinidis, A. G., Tsaknis, P. A., Gkika, E., & Stavroulakis, D. (2020). The influence of the big five personality traits and risk aversion on entrepreneurial intention. In: A. Kavoura, E. Kefallonitis, & P. Theodoridis (Eds.), Strategic innovative marketing and tourism (pp. 215–224). Springer. https://doi.org/10.1007/978-3-030-36126-6_24

Sarfraz, M., Hussain, G., Shahid, M., Riaz, A., Muavia, M., Fahed, Y. S., & Abdullah, M. T. (2022). Medical students’ online learning perceptions, online learning readiness, and learning outcomes during COVID-19: the moderating role of teacher’s readiness to teach online. International Journal of Environmental Research and Public Health , 19 , 3520. https://doi.org/10.3390/ijerph19063520

Schwarzer, R., & Jerusalem, M. (1995). Generalized self-efficacy scale. In J. Weinman, S. Wright, & M. Johnston (Eds.), Measures in Health psychology: a user’s portfolio. Causal and control beliefs (pp. 35–37). NFER-NELSON.

Seidman, G. (2013). Self-presentation and belonging on Facebook: how personality influences social media use and motivations. Personality and Individual Differences , 54 , 402–407. https://doi.org/10.1016/j.paid.2012.10.009

Shahzad, A., Hassan, R., Aremu, A. Y., Hussain, A., & Lodhi, R. N. (2021). Effects of COVID-19 in E-learning on higher education institution students: the group comparison between male and female. Quality & Quantity , 55 , 805–826. https://doi.org/10.1007/s11135-020-01028-z

Shih, H. F., Chen, S. H. E., Chen, S. C., & Wey, S. C. (2013). The relationship among tertiary level EFL students’ personality, online learning motivation and online learning satisfaction. Procedia-Social and Behavioral Sciences , 103 , 1152–1160. https://doi.org/10.1016/j.sbspro.2013.10.442

Specht, J., Egloff, B., & Schmukle, S. C. (2011). Stability and change of personality across the life course: the impact of age and major life events on mean-level and rank-order stability of the big five. Journal of Personality and Social Psychology , 101 , 862–882. https://doi.org/10.1037/a0024950

Tavitiyaman, P., Ren, L., & Fung, C. (2021). Hospitality students at the online classes during COVID-19–How personality affects experience? Journal of Hospitality Leisure Sport & Tourism Education , 28 , 100304. https://doi.org/10.1016/j.jhlste.2021.100304

Theobald, M. (2021). Self-regulated learning training programs enhance university students’ academic performance, self-regulated learning strategies, and motivation: a meta-analysis. Contemporary Educational Psychology , 66 , 101976. https://doi.org/10.1016/j.cedpsych.2021.101976

Tzankova, I., Compare, C., Marzana, D., Guarino, A., Di Napoli, I., Rochira, A., Calandri, E., Barbieri, I., Procentese, F., Gatti, F., Marta, E., Fedi, A., Aresi, G., & Albanesi, C. (2022). Emergency online school learning during COVID-19 lockdown: a qualitative study of adolescents’ experiences in Italy. Current Psychology . https://doi.org/10.1007/s12144-021-02674-8

Ulum, H. (2022). The effects of online education on academic success: a meta-analysis study. Education and Information Technologies , 27 , 429–450. https://doi.org/10.1007/s10639-021-10740-8

Vedel, A. (2014). The big five and tertiary academic performance: a systematic review and meta-analysis. Personality and Individual Differences , 71 , 66–76. https://doi.org/10.1016/j.paid.2014.07.011

Venkatesh, V., Thong, J. Y., & Xu, X. (2016). Unified theory of acceptance and use of technology: a synthesis and the road ahead. Journal of the Association for Information Systems , 17 , 328–376. https://ssrn.com/abstract=2800121

Vlachogianni, P., & Tselios, N. (2022). The relationship between perceived usability, personality traits and learning gain in an e-learning context. The International Journal of Information and Learning Technology , 39 , 70–81. https://doi.org/10.1108/IJILT-08-2021-0116

Wang, C. H., Shannon, D. M., & Ross, M. E. (2013). Students’ characteristics, self-regulated learning, technology self-efficacy, and course outcomes in online learning. Distance Education , 34 , 302–323. https://doi.org/10.1080/01587919.2013.835779

Watjatrakul, B. (2016). Online learning adoption: effects of neuroticism, openness to experience, and perceived values. Interactive Technology and Smart Education , 13 , 229–243. https://doi.org/10.1108/ITSE-06-2016-0017

Wei, H. C., & Chou, C. (2020). Online learning performance and satisfaction: do perceptions and readiness matter? Distance Education , 41 , 48–69. https://doi.org/10.1080/01587919.2020.1724768

Yokoyama, S. (2019). Academic self-efficacy and academic performance in online learning: a mini review. Frontiers in Psychology , 9 , 2794. https://doi.org/10.3389/fpsyg.2018.02794

Yu, Z. (2021). The effects of gender, educational level, and personality on online learning outcomes during the COVID-19 pandemic. International Journal of Educational Technology in Higher Education , 18 , 1–17. https://doi.org/10.1186/s41239-021-00252-3

Zettler, I., Schild, C., Lilleholt, L., Kroencke, L., Utesch, T., Moshagen, M., & Geukes, K. (2022). The role of personality in COVID-19-related perceptions, evaluations, and behaviors: findings across five samples, nine traits, and 17 criteria. Social Psychological and Personality Science , 13 , 299–310. https://doi.org/10.1177/19485506211001680

Zhao, T., Fu, Z., Lian, X., Ye, L., & Huang, W. (2021). Exploring emotion regulation and Perceived Control as Antecedents of anxiety and its Consequences during Covid-19 full remote learning. Frontiers in Psychology , 12 , 675910. https://doi.org/10.3389/fpsyg.2021.675910

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Kai Kaspar, Kateryna Burtniak & Marco Rüth

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Kaspar, K., Burtniak, K. & Rüth, M. Online learning during the Covid-19 pandemic: How university students’ perceptions, engagement, and performance are related to their personal characteristics. Curr Psychol 43 , 16711–16730 (2024). https://doi.org/10.1007/s12144-023-04403-9

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Rewiring the classroom: How the COVID-19 pandemic transformed K-12 education

Subscribe to the brown center on education policy newsletter, brian a. jacob and brian a. jacob walter h. annenberg professor of education policy; professor of economics, and professor of education - university of michigan, former brookings expert cristina stanojevich cs cristina stanojevich doctoral student - michigan state university.

August 26, 2024

The pandemic changed K-12 classrooms through new technologies, instructional practices, and parent-teacher communications, along with an emphasis on social-emotional learning.
Less tangibly, COVID-19 might have shifted perceptions of the value and purposes of K-12 schooling.
The durability and effects of these changes remain unclear and will depend on how educational leaders and policymakers manage them.

In March 2020, virtually all public school districts in the U.S. shut their doors. For the next 18 months, schooling looked like it never had before. Homes became makeshift classrooms; parents became de facto teachers. But by fall 2022, many aspects of K-12 education had returned to “normal.” Schools resumed in-person classes, extracurricular activities flourished, and mask mandates faded.

But did schools really return to what they were before the COVID-19 pandemic? Our research suggests not. We interviewed teachers, school leaders, and district administrators across 12 districts in two states, and then we surveyed a nationally representative set of veteran educators in May 2023. We found that the COVID-19 pandemic transformed K-12 education in fundamental ways.

Below, we describe how the pandemic reshaped the educational landscape in these ways and we consider the opportunities and challenges these changes present for students, educators, and policymakers.

Accelerated adoption of technology

One of the most immediate and visible changes brought about by the pandemic was the rapid integration of technology into the classroom. Before COVID-19, many schools were easing into the digital age. The switch to remote learning in March 2020 forced schools to fully embrace Learning Management Systems (LMS), Zoom, and educational software almost overnight.

When students returned to in-person classrooms, the reliance on these digital tools persisted. Over 70% of teachers in our survey report that students are now assigned their own personal device (over 80% for secondary schools). LMS platforms like Google Classroom and Schoology remain essential in many schools. An assistant superintendent of a middle-income district remarked, “Google Classroom has become a mainstay for many teachers, especially middle school [and] high school.”

The platforms serve as hubs for posting assignments, accessing educational content, and enabling communication between teachers, students, and parents. They have become popular among parents as well. One teacher, who has school-age children herself, noted :

“Whereas pre-COVID…you’re hoping and praying your kids bring home information…[now] I can go on Google classroom and be like, ‘Oh, it says you worked on Mesopotamia today. What was that lesson about?’”

Transformed instructional practices

The pandemic’s impact on student learning was profound. Reading and math scores dropped precipitously, and the gap widened between more and less advantaged students. Many schools responded by adjusting their schedules or adopting new programs. Several mentioned adopting “What I need” (WIN) or “Power” blocks to accommodate diverse learning needs. During these blocks, teachers provide individualized support to students while others work on independent practice or extension activities.

Teachers report placing greater emphasis on small-group instruction and personalized learning. They spend less time on whole-class lecture and rely more on educational software (e.g., Lexia for reading and Zearn for math) to tailor instruction to individual student needs. A third-grade teacher in a low-income district explained:

“The kids are in so many different places, Lexia is very prescriptive and diagnostic, so it will give the kids specifically what level and what skills they need. [I] have a student who’s working on Greek and Latin roots, and then I have another kid who’s working on short vowel sounds. [It’s] much easier for them to get it through Lexia than me trying to get, you know, 18 different reading lessons.”

Teachers aren’t just using technology to personalize instruction. Having spent months gaining expertise with educational software, more teachers find it natural to integrate those programs into their classrooms today. Those teachers who used ed tech before report doing so even more now. They describe using software like Flowcabulary and Prodigy to make learning more engaging, and games such as Kahoot to give students practice with various skills. Products like Nearpod let them create presentations that integrate instruction with formative assessment. Other products, like Edpuzzle, help teachers monitor student progress.

Some teachers discovered how to use digital tools to save time and improve their communications to students. One elementary teacher, for example, explains even when her students complete an assignment by hand, she has them take a picture of it and upload it to her LMS:

“I can sort them, and I can comment on them really fast. So it’s made feedback better. [I have] essentially a portfolio of all their math, rather than like a hard copy that they could lose…We can give verbal feedback. I could just hit the mic and say, ‘Hey, double check number 6, your fraction is in fifths, it needs to be in tenths.’”

Increased emphasis on social-emotional learning

The pandemic also revealed and exacerbated the social-emotional challenges that students face. In our survey, nearly 40% of teachers report many more students struggling with depression and anxiety than before the COVID-19 pandemic; over 80% report having at least a few more students struggling.

These student challenges have changed teachers’ work. When comparing how they spend class time now versus before the pandemic, most teachers report spending more time on activities relating to students’ social-emotional well-being (73%), more time addressing behavioral issues (70%), and more time getting students caught up and reviewing routines and procedures (60%).

In response, schools have invested in social-emotional learning (SEL) programs and hired additional counselors and social workers. Some districts turned to online platforms such as Class Catalyst and CloseGap that allow students to anonymously report their emotional state on a daily basis, which helps school staff track students’ mental health.

Teachers also have been adapting their expectations of students. Many report assigning less homework and providing students more flexibility to turn in assignments late and retake exams.

Facilitated virtual communication between parents and teachers

The pandemic also radically reshaped parent-teacher communications. Mirroring trends across society, videoconferencing has become a go-to option. Schools use videoconferencing for regular parent-teacher conferences, along with meetings to discuss special education placements and disciplinary incidents. In our national survey, roughly one-half of teachers indicate that they conduct a substantial fraction of parent-teacher conferences online; nearly a quarter of teachers report that most of their interactions with parents are virtual.

In our interviews, teachers and parents gushed about the convenience afforded by videoconferencing, and some administrators believe it has increased overall parent participation. (One administrator observed, “Our attendance rates [at parent-teacher conferences] and interaction with parents went through the roof.”)

An administrator from a low-income district shared the benefits of virtual Individualized Education Plan (IEP) meetings:

“It’s rare that we have a face-to-face meeting…everything is Docusigned now. Parents love it because I can have a parent that’s working—a single mom that’s working full time—that can step out during her lunch break…[and] still interact with everybody.”

During the pandemic, many districts purchased a technology called Remind that allows teachers to use their personal smartphones to text with parents while blocking their actual phone number. We heard that teachers continue to text with parents, citing the benefits for quick check-ins or questions. Remind and many LMS also have translation capabilities that makes it easier for teachers and parents to overcome language barriers.

Moving forward

The changes described above have the potential to improve student learning and increase educational equity. They also carry risks. On the one hand, the growing use of digital tools to differentiate instruction may close achievement gaps, and the ubiquity of video conferencing could allow working parents to better engage with school staff. On the other hand, the overreliance on digital tools could harm students’ fine motor skills (one teacher remarked, “[T]heir handwriting sucks compared to how it used to be”) and undermine student engagement. Some new research suggests that relying on digital platforms might impede learning relative to the old-fashioned “paper and pencil” approach. And regarding virtual conferences, the superintendent of a small, rural district told us, “There’s a disconnect when we do that…No, I want the parents back in our buildings, I want people back. We’re [the school] a community center.”

Of course, some of the changes we observed may not persist. For example, fewer teachers may rely on digital tools to tailor instruction once the “COVID cohorts” have aged out of the system. As the emotional scars of the pandemic fade, schools may choose to devote fewer resources to SEL programming. It’s important to note, too, that many of the changes we found come from the adoption of new technology, and the technology available to educators will continue to evolve (e.g., with the integration of new AI technologies into personalized tutoring systems). That being said, now that educators have access to more instructional technology and—perhaps more importantly—greater familiarity with using such tools, they might continue to rely on them.

The changes brought about by the COVID-19 pandemic provide a unique opportunity to rethink and improve the structure of K-12 education. While the integration of technology and the focus on social-emotional learning offer promising avenues for enhancing student outcomes, they also require continuous evaluation. Indeed, these changes raise some questions beyond simple cost-benefit calculations. For example, the heightened role of ed tech raises questions about the proper role of the private sector in public education. As teachers increasingly “outsource” the job of instruction to software products, what might be lost?

Educational leaders and policymakers must ensure that these pandemic-inspired changes positively impact learning and address the evolving needs of students and teachers. As we navigate this new educational landscape, the lessons learned from this unprecedented time can serve as a guide for building a more resilient, equitable, and effective educational system for the future.

Beyond technological changes, COVID-19 shifted perspectives about K-12 schooling. A middle-school principal described a new mentality among teachers in her district, “I think we have all become more readily able to adapt…we’ve all learned to assess what we have in front of us and make the adjustments we need to ensure that students are successful.” And a district administrator emphasized how the pandemic highlighted the vital role played by schools:

“…we saw that when students were not in school. From a micro and macro level, the environment that a school creates to support you growing up…we realized how needed this network is…both academically and socially, in growing our citizens up to be productive in the world. And we are happy to have everyone back.”

At the end of the day, this realization may be one of the pandemic’s most enduring legacies.

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FDA Approves and Authorizes Updated mRNA COVID-19 Vaccines to Better Protect Against Currently Circulating Variants

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Today, the U.S. Food and Drug Administration approved and granted emergency use authorization (EUA) for updated mRNA COVID-19 vaccines (2024-2025 formula) to include a monovalent (single) component that corresponds to the Omicron variant KP.2 strain of SARS-CoV-2. The mRNA COVID-19 vaccines have been updated with this formula to more closely target currently circulating variants and provide better protection against serious consequences of COVID-19, including hospitalization and death. Today’s actions relate to updated mRNA COVID-19 vaccines manufactured by ModernaTX Inc. and Pfizer Inc.

In early June, the FDA advised manufacturers of licensed and authorized COVID-19 vaccines that the COVID-19 vaccines (2024-2025 formula) should be monovalent JN.1 vaccines. Based on the further evolution of SARS-CoV-2 and a rise in cases of COVID-19, the agency subsequently determined and advised manufacturers that the preferred JN.1-lineage for the COVID-19 vaccines (2024-2025 formula) is the KP.2 strain, if feasible.

“Vaccination continues to be the cornerstone of COVID-19 prevention,” said Peter Marks, M.D., Ph.D., director of the FDA’s Center for Biologics Evaluation and Research. “These updated vaccines meet the agency’s rigorous, scientific standards for safety, effectiveness, and manufacturing quality. Given waning immunity of the population from previous exposure to the virus and from prior vaccination, we strongly encourage those who are eligible to consider receiving an updated COVID-19 vaccine to provide better protection against currently circulating variants.”

The updated mRNA COVID-19 vaccines include Comirnaty and Spikevax, both of which are approved for individuals 12 years of age and older, and the Moderna COVID-19 Vaccine and Pfizer-BioNTech COVID-19 Vaccine, both of which are authorized for emergency use for individuals 6 months through 11 years of age.

What You Need to Know

Unvaccinated individuals 6 months through 4 years of age are eligible to receive three doses of the updated, authorized Pfizer-BioNTech COVID-19 Vaccine or two doses of the updated, authorized Moderna COVID-19 Vaccine.
Individuals 6 months through 4 years of age who have previously been vaccinated against COVID-19 are eligible to receive one or two doses of the updated, authorized Moderna or Pfizer-BioNTech COVID-19 vaccines (timing and number of doses to administer depends on the previous COVID-19 vaccine received).
Individuals 5 years through 11 years of age regardless of previous vaccination are eligible to receive a single dose of the updated, authorized Moderna or Pfizer-BioNTech COVID-19 vaccines; if previously vaccinated, the dose is administered at least 2 months after the last dose of any COVID-19 vaccine.
Individuals 12 years of age and older are eligible to receive a single dose of the updated, approved Comirnaty or the updated, approved Spikevax; if previously vaccinated, the dose is administered at least 2 months since the last dose of any COVID-19 vaccine.
Additional doses are authorized for certain immunocompromised individuals ages 6 months through 11 years of age as described in the Moderna COVID-19 Vaccine and Pfizer-BioNTech COVID-19 Vaccine fact sheets.

Individuals who receive an updated mRNA COVID-19 vaccine may experience similar side effects as those reported by individuals who previously received mRNA COVID-19 vaccines and as described in the respective prescribing information or fact sheets. The updated vaccines are expected to provide protection against COVID-19 caused by the currently circulating variants. Barring the emergence of a markedly more infectious variant of SARS-CoV-2, the FDA anticipates that the composition of COVID-19 vaccines will need to be assessed annually, as occurs for seasonal influenza vaccines.

For today’s approvals and authorizations of the mRNA COVID-19 vaccines, the FDA assessed manufacturing and nonclinical data to support the change to include the 2024-2025 formula in the mRNA COVID-19 vaccines. The updated mRNA vaccines are manufactured using a similar process as previous formulas of these vaccines. The mRNA COVID-19 vaccines have been administered to hundreds of millions of people in the U.S., and the benefits of these vaccines continue to outweigh their risks.

On an ongoing basis, the FDA will review any additional COVID-19 vaccine applications submitted to the agency and take appropriate regulatory action.

The approval of Comirnaty (COVID-19 Vaccine, mRNA) (2024-2025 Formula) was granted to BioNTech Manufacturing GmbH. The EUA amendment for the Pfizer-BioNTech COVID-19 Vaccine (2024-2025 Formula) was issued to Pfizer Inc.

The approval of Spikevax (COVID-19 Vaccine, mRNA) (2024-2025 Formula) was granted to ModernaTX Inc. and the EUA amendment for the Moderna COVID-19 Vaccine (2024-2025 Formula) was issued to ModernaTX Inc.

Related Information

Comirnaty (COVID-19 Vaccine, mRNA) (2024-2025 Formula)
Spikevax (COVID-19 Vaccine, mRNA) (2024-2025 Formula)
Moderna COVID-19 Vaccine (2024-2025 Formula)
Pfizer-BioNTech COVID-19 Vaccine (2024-2025 Formula)
FDA Resources for the Fall Respiratory Illness Season
Updated COVID-19 Vaccines for Use in the United States Beginning in Fall 2024
June 5, 2024, Meeting of the Vaccines and Related Biological Products Advisory Committee

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Prevention and Control of Seasonal Influenza with Vaccines: Recommendations of the Advisory Committee on Immunization Practices — United States, 2024–25 Influenza Season

Recommendations and Reports / August 29, 2024 / 73(5);1–25

Lisa A. Grohskopf, MD 1 ; Jill M. Ferdinands, PhD 1 ; Lenee H. Blanton, MPH 1 ; Karen R. Broder, MD 2 ; Jamie Loehr, MD 3 ( View author affiliations )

Introduction

Primary changes and updates, recommendations for the use of influenza vaccines, 2024–25, influenza vaccine composition and available vaccines, storage and handling of influenza vaccines, additional sources of information regarding influenza and influenza vaccines, acknowledgments, acip influenza vaccine work group.

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This report updates the 2023–24 recommendations of the Advisory Committee on Immunization Practices (ACIP) concerning the use of seasonal influenza vaccines in the United States (MMWR Recomm Rep 2022;72[No. RR-2]:1–24). Routine annual influenza vaccination is recommended for all persons aged ≥6 months who do not have contraindications. Trivalent inactivated influenza vaccines (IIV3s), trivalent recombinant influenza vaccine (RIV3), and trivalent live attenuated influenza vaccine (LAIV3) are expected to be available. All persons should receive an age-appropriate influenza vaccine (i.e., one approved for their age), with the exception that solid organ transplant recipients aged 18 through 64 years who are receiving immunosuppressive medication regimens may receive either high-dose inactivated influenza vaccine (HD-IIV3) or adjuvanted inactivated influenza vaccine (aIIV3) as acceptable options (without a preference over other age-appropriate IIV3s or RIV3). Except for vaccination for adults aged ≥65 years, ACIP makes no preferential recommendation for a specific vaccine when more than one licensed and recommended vaccine is available. ACIP recommends that adults aged ≥65 years preferentially receive any one of the following higher dose or adjuvanted influenza vaccines: trivalent high-dose inactivated influenza vaccine (HD-IIV3), trivalent recombinant influenza vaccine (RIV3), or trivalent adjuvanted inactivated influenza vaccine (aIIV3). If none of these three vaccines is available at an opportunity for vaccine administration, then any other age-appropriate influenza vaccine should be used.

Primary updates to this report include the following two topics: the composition of 2024–25 U.S. seasonal influenza vaccines and updated recommendations for vaccination of adult solid organ transplant recipients. First, following a period of no confirmed detections of wild-type influenza B/Yamagata lineage viruses in global surveillance since March 2020, 2024–25 U.S. influenza vaccines will not include an influenza B/Yamagata component. All influenza vaccines available in the United States during the 2024–25 season will be trivalent vaccines containing hemagglutinin derived from 1) an influenza A/Victoria/4897/2022 (H1N1)pdm09-like virus (for egg-based vaccines) or an influenza A/Wisconsin/67/2022 (H1N1)pdm09-like virus (for cell culture-based and recombinant vaccines); 2) an influenza A/Thailand/8/2022 (H3N2)-like virus (for egg-based vaccines) or an influenza A/Massachusetts/18/2022 (H3N2)-like virus (for cell culture-based and recombinant vaccines); and 3) an influenza B/Austria/1359417/2021 (Victoria lineage)-like virus. Second, recommendations for vaccination of adult solid organ transplant recipients have been updated to include HD-IIV3 and aIIV3 as acceptable options for solid organ transplant recipients aged 18 through 64 years who are receiving immunosuppressive medication regimens (without a preference over other age-appropriate IIV3s or RIV3).

This report focuses on recommendations for the use of vaccines for the prevention and control of seasonal influenza during the 2024–25 influenza season in the United States. A brief summary of the recommendations and a link to the most recent Background Document containing additional information are available at https://www.cdc.gov/acip-recs/hcp/vaccine-specific/flu.html?CDC_AAref_Val=https://www.cdc.gov/vaccines/hcp/acip-recs/vacc-specific/flu.html . These recommendations apply to U.S.-licensed influenza vaccines. Updates and other information are available from CDC’s influenza website ( https://www.cdc.gov/flu ). Vaccination and health care providers should check this site periodically for additional information.

Influenza viruses typically circulate annually in the United States, most commonly from the late fall through the early spring. Most persons who become ill after influenza virus infection recover without serious complications or sequelae. However, influenza can be associated with serious illnesses, hospitalizations, and deaths, particularly among older adults, very young children, pregnant persons, and persons of all ages with certain chronic medical conditions ( 1 – 7 ). Influenza also is an important cause of missed work and school ( 8 – 10 ).

Routine annual influenza vaccination for all persons aged ≥6 months who do not have contraindications has been recommended by CDC and the Advisory Committee on Immunization Practices (ACIP) since 2010 ( 11 ). Vaccination provides important protection from influenza illness and its potential complications. The effectiveness of influenza vaccination varies depending on multiple factors such as the age and health of the recipient, the type of vaccine administered, the types and subtypes of influenza viruses circulating in the community, and the degree of similarity between circulating viruses and those included in the vaccine ( 12 ). During each of the six influenza seasons from 2010–11 through 2015–16, influenza vaccination prevented an estimated 1.6–6.7 million illnesses, 790,000–3.1 million outpatient medical visits, 39,000–87,000 hospitalizations, and 3,000–10,000 respiratory and circulatory deaths each season in the United States ( 13 ). During the severe 2017–18 season, notable for an unusually long duration of widespread high influenza activity throughout the United States and higher rates of outpatient visits and hospitalizations compared with recent seasons, vaccination prevented an estimated 7.1 million illnesses, 3.7 million medical visits, 109,000 hospitalizations, and 8,000 deaths ( 14 ), despite an overall estimated vaccine effectiveness of 38% (62% against influenza A[H1N1]pdm09 viruses, 22% against influenza A[H3N2] viruses, and 50% against influenza B viruses) ( 14 ).

This report updates the 2023–24 ACIP recommendations regarding the use of seasonal influenza vaccines ( 15 ) and provides recommendations and guidance for vaccination providers regarding the use of influenza vaccines in the United States for the 2024–25 season. Various formulations of influenza vaccines are available ( Table 1 ). Contraindications and precautions for the use of influenza vaccines are summarized ( Tables 2 and 3 ). Abbreviations are used in this report to denote the various types of vaccines ( Box ). A summary of these recommendations and a Background Document containing additional information on influenza, influenza-associated illness, and influenza vaccines are available at https://www.cdc.gov/acip-recs/hcp/vaccine-specific/flu.html?CDC_AAref_Val=https://www.cdc.gov/vaccines/hcp/acip-recs/vacc-specific/flu.html .

ACIP provides annual recommendations for the use of influenza vaccines for the prevention and control of seasonal influenza in the United States. The ACIP Influenza Work Group meets by teleconference once to twice per month throughout the year. Work Group membership includes multiple voting members of ACIP, representatives of ACIP liaison organizations, and consultants. Discussions include topics such as influenza surveillance, vaccine effectiveness and safety, vaccination coverage, program feasibility, cost effectiveness, and vaccine supply. Presentations are requested from invited experts and published and unpublished data are discussed.

The Background Document that supplements this report contains literature related to recommendations made in previous seasons. The information included in the Background Document for such topics is not a systematic review; it is intended to provide an overview of background literature and is periodically updated with literature being identified primarily through a broad search for English-language articles on influenza and influenza vaccines. In general, longstanding recommendations in this document that were made in previous seasons reflect expert opinion, and systematic review and assessment of evidence was not performed. Systematic review and evidence assessment are not performed for minor wording changes to existing recommendations, changes in the Food and Drug Administration (FDA)-recommended viral antigen composition of seasonal influenza vaccines, and minor changes in guidance for the use of influenza vaccines (e.g., guidance for timing of vaccination and other programmatic issues, guidance for dosage in specific populations, guidance for selection of vaccines for specific populations that are already recommended for vaccination, and changes that reflect use that is consistent with FDA-licensed indications and prescribing information).

Typically, systematic review and evaluation of evidence using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach ( 16 ) are performed for new recommendations or substantial changes in the current recommendations (e.g., expansion of the recommendation for influenza vaccination to new populations not previously recommended for vaccination or potential preferential recommendations for specific vaccines).

Evidence is reviewed by the ACIP influenza Work Group, and Work Group considerations are included within the ACIP Evidence to Recommendations framework (EtR) ( 17 ) to inform the development of recommendations that are proposed for vote by the ACIP. Systematic review, GRADE, and the ACIP EtR framework were used in the development of the updated recommendations for adult solid organ transplant recipients discussed in this report.

Primary changes and updates to the recommendations described in this report include 1) the composition of 2024–25 U.S. seasonal influenza vaccines and 2) updated recommendations for vaccination of adult solid organ transplant recipients. Information relevant to these changes includes the following:

The composition of the 2024–25 U.S. seasonal influenza vaccines includes an update to the influenza A(H3N2) component. For the 2024–25 season, U.S.-licensed influenza vaccines will contain hemagglutinin (HA) derived from 1) an influenza A/Victoria/4897/2022 (H1N1)pdm09-like virus (for egg-based vaccines) or an influenza A/Wisconsin/67/2022 (H1N1)pdm09-like virus (for cell culture-based and recombinant vaccines, 2) an influenza A/Thailand/8/2022 (H3N2)-like virus (for egg-based vaccines) or an influenza A/Massachusetts/18/2022 (H3N2)-like virus (for cell culture-based and recombinant vaccines), and 3) an influenza B/Austria/1359417/2021 (Victoria lineage)-like virus (for egg-based, cell culture-based, and recombinant vaccines). Recommendations for the composition of Northern Hemisphere influenza vaccines are made by the World Health Organization (WHO), which organizes a consultation, usually in February of each year. Surveillance data are reviewed, and candidate vaccine viruses are discussed. Information about the WHO meeting of February 2024 for selection of the 2024–25 Northern Hemisphere influenza vaccine composition is available at https://www.who.int/publications/m/item/recommended-composition-of-influenza-virus-vaccines-for-use-in-the-2024-2025-northern-hemisphere-influenza-season . Subsequently, FDA, which has regulatory authority over vaccines in the United States, convenes a meeting of its Vaccines and Related Biological Products Advisory Committee (VRBPAC). This committee considers the recommendations of WHO, reviews and discusses similar data, and makes a final decision regarding the composition of influenza vaccines licensed and marketed in the United States. Materials from the VRBPAC discussion on March 5, 2024, during which the composition of the 2024–25 U.S. influenza vaccines was discussed, are available at https://www.fda.gov/advisory-committees/advisory-committee-calendar/vaccines-and-related-biological-products-advisory-committee-march-5-2024-meeting-announcement . For the 2024–25 influenza season, FDA has recommended that the U.S. seasonal influenza vaccine composition no longer include influenza B/Yamagata, as there have been no confirmed detections of influenza B/Yamagata viruses in global influenza surveillance since March 2020 ( 18 , 19 ).
Recommendations for vaccination of adult solid organ transplant recipients have been updated to include HD-IIV3 and aIIV3 as acceptable options for solid organ transplant recipients aged 18 through 64 years who are receiving immunosuppressive medication regimens (without a preference over other age-appropriate IIVs or RIV3). To inform this recommendation, a systematic review and GRADE of evidence concerning effectiveness and safety of HD-IIV3 and aIIV3 compared with standard-dose unadjuvanted inactivated influenza vaccines was conducted. A summary of this review and the GRADE evidence tables is available at https://www.cdc.gov/vaccines/acip/recs/grade/influenza-solid-organ-transplant.html . A summary of the ACIP EtR framework is available at https://www.cdc.gov/vaccines/acip/recs/grade/influenza-solid-organ-transplant-etr.html .

Groups Recommended for Vaccination

Routine annual influenza vaccination of all persons aged ≥6 months who do not have contraindications continues to be recommended. All persons should receive an age-appropriate influenza vaccine (one that is approved for their age), with the exception that solid organ transplant recipients aged 18 through 64 years who are receiving immunosuppressive medication regimens may receive either HD-IIV3 or aIIV3 as acceptable options (without a preference over other age-appropriate IIV3s or RIV3) (see Immunocompromised Persons). Influenza vaccines expected to be available for the 2024–25 season, their age indications, and their presentations are described (Table 1). ACIP makes no preferential recommendation for the use of any one influenza vaccine over another when more than one licensed and recommended vaccine is available, except for selection of influenza vaccines for persons aged ≥65 years (see Older Adults). Recommendations regarding timing of vaccination, considerations for specific populations, the use of specific vaccines, and contraindications and precautions are summarized in the sections that follow.

Timing of Vaccination

Timing of the onset, peak, and decline of influenza activity varies from season to season ( 20 ). Decisions about timing need to consider the unpredictability of the influenza season, possible waning of vaccine-induced immunity over the course of a season, and practical considerations. For most persons who need only 1 dose of influenza vaccine for the season, vaccination should ideally be offered during September or October. However, vaccination should continue after October and throughout the influenza season as long as influenza viruses are circulating and unexpired vaccine is available. To avoid missed opportunities for vaccination, providers should offer vaccination during routine health care visits and hospitalizations. Revaccination (i.e., providing a booster dose) to persons who have been fully vaccinated for the season is not recommended, regardless of when the current season vaccine was received.

Influenza vaccines might be available as early as July or August; however, vaccination during July and August is not recommended for most groups because of potential waning of immunity over the course of the influenza season ( 21 – 40 ), particularly among older adults ( 21 , 22 , 24 , 31 , 34 , 40 ). However, vaccination during July or August can be considered for any recipient for whom there is concern that they will not be vaccinated at a later date. Considerations for timing of vaccination include the following:

For most adults (particularly adults aged ≥65 years) and for pregnant persons in the first or second trimester: Vaccination during July and August should be avoided unless there is concern that vaccination later in the season might not be possible.
Children who require 2 doses: Certain children aged 6 months through 8 years require 2 doses of influenza vaccine for the season (see Children Aged 6 Months Through 8 Years: Number of Influenza Vaccine Doses) ( Figure ). These children should receive their first dose as soon as possible (including during July and August, if vaccine is available) to allow the second dose (which must be administered ≥4 weeks later) to be received, ideally, by the end of October.
Children who require only 1 dose: Vaccination during July and August can be considered for children of any age who need only 1 dose of influenza vaccine for the season. Although waning of immunity after vaccination over the course of the season has been observed among all age groups ( 21 – 40 ), there are fewer published studies reporting results specifically among children ( 21 , 30 , 32 , 33 , 37 , 39 , 40 ). Moreover, children in this group might visit health care providers during the late summer months for medical examinations before the start of school. Vaccination can be considered at this time because it represents a vaccination opportunity.
Pregnant persons in the third trimester: Vaccination during July and August can be considered for pregnant persons who are in the third trimester during these months because vaccination has been associated in multiple studies with reduced risk for influenza illness in their infants during the first months after birth, when they are too young to receive influenza vaccine ( 41 – 44 ). For pregnant persons in the first or second trimester during July and August, waiting to vaccinate until September or October is preferable, unless there is concern that later vaccination might not be possible.

An increasing number of observational studies ( 21 – 40 ) have reported decreased vaccine effectiveness with increasing time after vaccination within an influenza season. The rate of waning effectiveness observed in these studies varied considerably and waning effects were inconsistent across age groups, seasons, and influenza virus types and subtypes; although several studies reported faster waning against influenza A(H3N2) viruses than against influenza A(H1N1) or influenza B viruses ( 25 , 31 , 35 , 40 ). A meta-analysis of 14 studies examining waning of influenza vaccine effectiveness using the test-negative design found a significant decline in effectiveness after vaccination against influenza A(H3N2) and influenza B but not against influenza A(H1N1) ( 45 ). In that study, VE against influenza A(H3N2) declined, on average, by 32 percentage points, from 45% during the first 3 months to 13% in the fourth to sixth months after vaccination. The rate of waning effectiveness also might vary with age; in several studies, waning was more pronounced among older adults ( 21 , 22 , 24 , 31 , 34 , 40 ). Several recent multiseason studies of waning protection found that the odds of influenza infection increased by 9% to 28% per month after vaccination among vaccinees of all ages and by 12% to 29% per month among vaccinees aged ≥65 years ( 33 , 39 , 40 ). There are fewer studies of waning specifically among children, with some reporting waning effectiveness ( 21 , 32 , 33 , 37 , 40 ) and others finding no evidence of waning effectiveness ( 30 , 39 ). Complicating the interpretation of studies of waning effectiveness is the fact that observed decreases in protection might be at least partially due to bias, unmeasured confounding, or emergence of antigenic drift variants of influenza viruses that are less well-matched to the vaccine viruses.

Community vaccination programs should balance persistence of vaccine-induced protection through the season with avoiding missed opportunities to vaccinate or vaccinating after onset of influenza circulation occurs. Although delaying vaccination might result in greater immunity later in the season, deferral might result in missed opportunities to vaccinate as well as difficulties in vaccinating a population within a more constrained period. Modeling studies examining the consequences of delaying vaccination (until September or October) among older adults in the United States found that delaying vaccination is beneficial if the delay does not cause a substantial reduction in overall vaccination coverage (because of failure of some persons who would prefer earlier vaccination to get vaccinated later in the fall) ( 46 – 48 ). Among older adults, delayed vaccination would be beneficial, on balance, if vaccine coverage declines by no more than 6% in a mild season ( 47 ) or by about 15% in a moderately severe season ( 46 , 48 ). However, these results are sensitive to many factors, especially the rate of waning of vaccine effectiveness, about which there remains considerable uncertainty.

Vaccination efforts should continue throughout the season because the duration of the influenza season varies, and influenza activity might not occur in certain communities until February, March, or later ( 20 ). Providers should offer influenza vaccine at health care visits to those not yet vaccinated, and organized vaccination campaigns should continue throughout the influenza season, including after influenza activity has begun in the community. Although vaccination by the end of October is recommended, vaccine administered in December or later, even if influenza activity has already begun, might be beneficial in most influenza seasons. Providers should offer influenza vaccination to unvaccinated persons who have already become ill with influenza during the season because the vaccine might protect them against other circulating influenza viruses.

Guidance for Influenza Vaccination in Specific Populations and Situations

Populations at higher risk for medical complications attributable to severe influenza.

All persons aged ≥6 months who do not have contraindications should be vaccinated annually. However, vaccination to prevent influenza is particularly important for persons who are at increased risk for severe illness and complications from influenza and for influenza-related outpatient, emergency department, or hospital visits. When vaccine supply is limited, vaccination efforts should focus on vaccination of persons at higher risk for medical complications attributable to severe influenza who do not have contraindications. These persons include the following (order of listing does not imply hierarchy or prioritization among these populations):

All children aged 6 through 59 months.
All persons aged ≥50 years.
Adults and children who have chronic pulmonary (including asthma), cardiovascular (excluding isolated hypertension), renal, hepatic, neurologic, hematologic, or metabolic disorders (including diabetes mellitus).
Persons who are immunocompromised due to any cause (including but not limited to immunosuppression caused by medications or HIV infection).
Persons who are or will be pregnant during the influenza season.
Children and adolescents (aged 6 months through 18 years) who are receiving aspirin- or salicylate-containing medications and who might be at risk for experiencing Reye syndrome after influenza virus infection.
Residents of nursing homes and other long-term care facilities.
American Indian or Alaska Native persons.
Persons who are extremely obese (body mass index ≥40 for adults).

IIV3 or RIV3 are suitable for all persons recommended for vaccination, including those in the risk groups listed. LAIV3 is not recommended for certain populations, including certain of these listed groups. Contraindications and precautions for the use of LAIV3 are noted (Table 2).

Persons Who Live with or Care for Persons at Higher Risk for Influenza-Related Complications

All persons aged ≥6 months without contraindications should be vaccinated annually. However, emphasis also should be placed on vaccination of persons who live with or care for those who are at increased risk for medical complications attributable to severe influenza. When vaccine supply is limited, vaccination efforts should focus on administering vaccination to persons at higher risk for influenza-related complications as well as persons who live with or care for such persons, including the following:

Health care personnel, including all paid and unpaid persons working in health care settings who have the potential for exposure to patients or to infectious materials. These personnel might include but are not limited to physicians, nurses, nursing assistants, nurse practitioners, physician assistants, therapists, technicians, emergency medical service personnel, dental personnel, pharmacists, laboratory personnel, autopsy personnel, students and trainees, contractual staff members, and others not directly involved in patient care but who might be exposed to infectious agents (e.g., clerical, dietary, housekeeping, laundry, security, maintenance, administrative, billing staff, and volunteers). ACIP guidance for vaccination of health care personnel has been published previously ( 49 ).
Household contacts (including children aged ≥6 months) and caregivers of children aged ≤59 months (<5 years) and adults aged ≥50 years, particularly contacts of children aged <6 months.
Household contacts (including children aged ≥6 months) and caregivers of persons with medical conditions that put them at higher risk for severe complications from influenza.

Health care personnel and persons who are contacts of persons in these groups (except for of contacts of severely immunocompromised persons who require a protected environment) can receive any influenza vaccine that is otherwise indicated. Persons who care for severely immunocompromised persons requiring a protected environment should not receive LAIV3. ACIP and the Healthcare Infection Control Practices Advisory Committee (HICPAC) have previously recommended that health care personnel who receive LAIV should avoid providing care for severely immunocompromised persons requiring a protected environment for 7 days after vaccination and that hospital visitors who have received LAIV should avoid contact with such persons for 7 days after vaccination ( 50 ). However, such persons need not be restricted from caring for or visiting less severely immunocompromised persons.

Children Aged 6 Through 35 Months: Influenza Vaccine Dose Volumes

Five IIV3s are approved for children aged ≥6 months (Table 1). Four of these vaccines are egg based (Afluria, Fluarix, FluLaval, and Fluzone), and one is cell culture–based (Flucelvax). For these vaccines, the approved dose volumes for children aged 6 through 35 months are as follows ( Table 4 ):

Afluria: 0.25 mL per dose. However, 0.25-mL prefilled syringes are no longer available. For children aged 6 through 35 months, a 0.25-mL dose must be obtained from a multidose vial ( 51 ).
Fluarix: 0.5 mL per dose ( 52 ).
Flucelvax: 0.5 mL per dose ( 53 ).
FluLaval: 0.5 mL per dose ( 54 ).
Fluzone: Either 0.25 mL or 0.5 mL per dose. Per the package insert, each dose can be given at either volume ( 55 ); however, 0.25-mL prefilled syringes are no longer available.

For all of these IIV3s, persons aged ≥36 months (≥3 years) should receive 0.5 mL per dose. Alternatively, healthy children aged ≥24 months (≥2 years) can receive LAIV3, 0.2 mL intranasally (0.1 mL in each nostril) ( 56 ). LAIV3 is not recommended for certain populations and is not approved for children aged <2 years or adults >49 years (see Contraindications and Precautions for the Use of LAIV3) (Table 2). RIV3 is not approved for children aged <18 years ( 57 ). High-dose inactivated influenza vaccine (HD-IIV3) ( 58 ) and adjuvanted inactivated influenza vaccine (aIIV3) ( 59 ) are not approved for persons aged <65 years.

Care should be taken to administer an age-appropriate vaccine at the appropriate volume for each dose. For IIV3s, the recommended volume can be administered from a prefilled syringe containing the appropriate volume (as supplied by the manufacturer) or a multidose vial. Multidose vials should be used only for the maximum number of doses specified in the package insert. Any vaccine remaining in a vial after the maximum number of doses has been removed should be discarded, regardless of the volume of the doses obtained or any remaining volume in the vial.

Children Aged 6 Months Through 8 Years: Number of Influenza Vaccine Doses

Children aged 6 months through 8 years require 2 doses of influenza vaccine administered a minimum of 4 weeks apart during their first season of vaccination for optimal protection ( 60 – 63 ). Determination of the number of doses needed is based on 1) the child’s age at the time of the first dose of 2024–25 influenza vaccine and 2) the number of doses of influenza vaccine received in previous influenza seasons.

Those who have previously received ≥2 total doses of trivalent or quadrivalent influenza vaccine ≥4 weeks apart before July 1, 2024, require only 1 dose for the 2024–25 season. The previous 2 doses of influenza vaccine do not need to have been received in the same season or consecutive seasons.
Those who have not previously received ≥2 doses of trivalent or quadrivalent influenza vaccine ≥4 weeks apart before July 1, 2024, or whose previous influenza vaccination history is unknown, require 2 doses for the 2024–25 season. The interval between the 2 doses should be ≥4 weeks. Children aged 6 months through 8 years who require 2 doses of influenza vaccine should receive their first dose as soon as possible (including during July and August, if vaccine is available) to allow the second dose (which must be administered ≥4 weeks later) to be received, ideally, by the end of October. For children aged 8 years who require 2 doses of vaccine, both doses should be administered even if the child turns age 9 years between receipt of dose 1 and dose 2.
Adults and children aged ≥9 years need only 1 dose of influenza vaccine for the 2024–25 season.

Pregnant Persons

Pregnant and postpartum persons are at higher risk for severe illness and complications from influenza, particularly during the second and third trimesters. Influenza vaccination during pregnancy is associated with reduced risk for respiratory illness and influenza among pregnant and postpartum persons as well as infants during the first months of life ( 41 – 44 , 64 ). ACIP and the American College of Obstetricians and Gynecologists recommend that persons who are pregnant or who might be pregnant or postpartum during the influenza season receive influenza vaccine ( 65 ). IIV3 or RIV3 can be used. LAIV3 should not be used during pregnancy but can be used postpartum. Influenza vaccine can be administered at any time during pregnancy (i.e., during any trimester), before and during the influenza season. Early vaccination (i.e., during July and August) can be considered for persons who are in the third trimester during these months if vaccine is available because this can provide protection for the infant during the first months of life when they are too young to be vaccinated ( 41 – 44 , 64 ).

Although experience with the use of IIVs during pregnancy is substantial, data specifically reflecting administration of influenza vaccines during the first trimester are limited. Most studies have not noted an association between influenza vaccination and adverse pregnancy outcomes, including spontaneous abortion (miscarriage) ( 66 – 76 ). One observational Vaccine Safety Datalink (VSD) study conducted during the 2010–11 and 2011–12 seasons noted an association between receipt of IIV containing influenza A(H1N1)pdm09 and risk for miscarriage in the 28 days after receipt of IIV, when an H1N1pdm09-containing vaccine also had been received the previous season ( 77 ). However, in a larger VSD follow-up study, IIV was not associated with an increased risk for miscarriage during the 2012–13, 2013–14, and 2014–15 seasons, regardless of previous season vaccination ( 78 ).

There is less experience with the use of more recently licensed influenza vaccines (e.g., cell culture-based and recombinant vaccines) during pregnancy compared with previously available products. For ccIIV, a review of Vaccine Adverse Event Reporting System (VAERS) reports from 2013 through 2020 ( 79 ) and a prospective cohort study conducted from 2017 through 2020 ( 80 ) did not reveal unexpected safety events among pregnant persons. Data from a randomized controlled trial (RCT) conducted at Clinical Immunization Safety Assessment (CISA) Project sites comparing the safety of RIV4 versus IIV4 in 382 pregnant persons supported the safety of RIV4 in pregnancy ( https://stacks.cdc.gov/view/cdc/122379 ) ( 81 ). Pregnancy registries and surveillance studies exist for certain products, for which information can be found in package inserts.

Older Adults

ACIP recommends that adults aged ≥65 years preferentially receive any one of the following higher dose or adjuvanted influenza vaccines: high-dose inactivated influenza vaccine (HD-IIV3), recombinant influenza vaccine (RIV3), or adjuvanted inactivated influenza vaccine (aIIV3). If none of these three vaccines is available at an opportunity for vaccine administration, then any other age-appropriate influenza vaccine should be administered ( 82 , 83 ).

Older adults (aged ≥65 years) are at increased risk for severe influenza-associated illness, hospitalization, and death compared with younger persons ( 4 , 84 , 85 ). Influenza vaccines are often less effective in this population ( 12 ). HD-IIV, RIV, and aIIV have been evaluated in comparison with nonadjuvanted SD-IIVs in this age group. Two of these vaccines, HD-IIV and RIV, are higher dose vaccines, which contain an increased dose of HA antigen per vaccine virus compared with nonadjuvanted SD-IIVs (60 μ g for HD-IIV3 and 45 μ g for RIV3, compared with 15 μ g for standard-dose inactivated vaccines) ( 57 , 58 ). The adjuvanted vaccine contains 15 μ g of HA per virus, similarly to nonadjuvanted SD-IIVs, but contains the adjuvant MF59 ( 59 ).

HD-IIV, RIV, and aIIV have shown relative benefit compared with SD-IIVs in certain studies, with the most evidence available for HD-IIV3. Randomized efficacy studies comparing these vaccines with nonadjuvanted SD-IIVs against laboratory-confirmed influenza outcomes are few in number ( 86 – 88 ) and cover few influenza seasons. Observational studies, predominantly retrospective cohort studies using diagnostic code–defined (rather than laboratory-confirmed) influenza outcomes, are more numerous and include more influenza seasons ( 89 – 99 ). Certain observational studies have reported relative benefit for HD-IIV, RIV, and aIIV in comparison with nonadjuvanted SD-IIVs, particularly in prevention of influenza-associated hospitalizations. The size of this relative benefit has varied from season to season and is not observed in all studies in all seasons, making it difficult to generalize the findings to all or most seasons. Studies directly comparing HD-IIV, RIV, and aIIV with one another are few and do not support a conclusion that any one of these vaccines is consistently superior to the others across seasons ( 89 – 91 , 94 , 100 , 101 ).

Immunocompromised Persons

ACIP recommends that persons with compromised immunity (including but not limited to persons with congenital and acquired immunodeficiency states, persons who are immunocompromised due to medications, and persons with anatomic and functional asplenia) should receive IIV3 or RIV3. All persons should receive an age-appropriate influenza vaccine (i.e., one approved for their age), with the exception that solid organ transplant recipients aged 18 through 64 years who are receiving immunosuppressive medication regimens may receive either HD-IIV3 or aIIV3 as acceptable options (without a preference over other age-appropriate IIV3s or RIV3). ACIP recommends that LAIV3 not be used for immunocompromised persons because of the uncertain but biologically plausible risk for disease attributable to the live vaccine virus. Use of LAIV3 in persons with these and other conditions is discussed in more detail (see Dosage, Administration, Contraindications, and Precautions) (Table 2).

Regarding solid organ transplant recipients specifically, a systematic review and meta-analysis including seven studies pertaining to use of higher dose (HD-IIV, double-dose SD-IIV, and RIV) and MF59-adjuvanted influenza vaccines compared with SD-IIV in this population noted no difference in likelihood of influenza-associated hospitalization (GRADE certainty level Low). However, evidence suggested potentially improved immunogenicity, with greater likelihood of seroconversion for both HD-IIV3 and aIIV3 relative to SD-IIV (GRADE certainty level Moderate for HD-IIV3 vs SD-IIV and Low for aIIV3 vs SD-IIV) for the influenza A(H1N1), influenza A(H3N2), and influenza B vaccine components. There was no evidence of increased risk of graft rejection with either HD-IIV3 or aIIV3 relative to SD-IIV (GRADE certainty level Moderate). Only one study included children. No evidence was available for RIV vs SD-IIV ( https://www.cdc.gov/vaccines/acip/recs/grade/influenza-solid-organ-transplant.html ; https://www.cdc.gov/vaccines/acip/recs/grade/influenza-solid-organ-transplant-etr.html ).

Immunocompromised states comprise a heterogeneous range of conditions with varying risks for severe infections. In many instances, limited data are available regarding the effectiveness of influenza vaccines in the setting of specific immunocompromised states ( 102 ). Timing of vaccination might be a consideration (e.g., vaccinating during a period either before or after an immunocompromising intervention). The Infectious Diseases Society of America has published detailed guidance for the selection and timing of vaccines for persons with specific immunocompromising conditions ( 103 ). Immune response to influenza vaccines might be blunted in persons with certain conditions, such as congenital immune deficiencies, and in persons receiving cancer chemotherapy, posttransplant regimens, or immunosuppressive medications.

Persons with a History of Guillain-Barré Syndrome After Influenza Vaccination

A history of Guillain-Barré syndrome (GBS) within 6 weeks of a previous dose of any type of influenza vaccine is considered a precaution for influenza vaccination (Table 2). Persons who are not at higher risk for severe influenza complications (see Populations at Higher Risk for Medical Complications Attributable to Severe Influenza) and who are known to have experienced GBS within 6 weeks of a previous influenza vaccination typically should not be vaccinated. As an alternative to vaccination, providers might consider using influenza antiviral chemoprophylaxis for these persons ( 104 ). However, the benefits of influenza vaccination might outweigh the possible risks for certain persons who have a history of GBS within 6 weeks after receipt of influenza vaccine and who also are at higher risk for severe complications from influenza.

Persons with a History of Egg Allergy

ACIP recommends that all persons aged ≥6 months with egg allergy should receive influenza vaccine. Any influenza vaccine (egg based or nonegg based) that is otherwise appropriate for the recipient’s age and health status can be used ( https://www.cdc.gov/vaccines/acip/recs/grade/influenza-egg-allergy.html ; https://www.cdc.gov/vaccines/acip/recs/grade/influenza-egg-allergy-etr.html ). Egg allergy alone necessitates no additional safety measures for influenza vaccination beyond those recommended for any recipient of any vaccine, regardless of severity of previous reaction to egg. All vaccines should be administered in settings in which personnel and equipment needed for rapid recognition and treatment of acute hypersensitivity reactions are available.

Most available influenza vaccines, with the exceptions of RIV3 (Flublok, licensed for persons aged ≥18 years) and ccIIV3 (Flucelvax, licensed for persons aged ≥6 months), are prepared by propagation of virus in embryonated eggs and might contain trace amounts of egg proteins, such as ovalbumin. Among those U.S.-licensed influenza vaccines for which ovalbumin content is reported, quantities are generally small (≤1 μ g/0.5mL dose) ( 51 , 52 , 54 – 56 , 58 , 59 ). Reviews of studies of administration of egg-based influenza vaccines to persons with egg allergy have noted no cases of anaphylaxis or serious hypersensitivity reactions ( 105 , 106 ). Severe allergic reactions after administration of the egg-free vaccine RIV to egg-allergic persons have been noted in VAERS reports ( 107 – 109 ). These reports highlight both the possibility that observed reactions after egg-based influenza vaccines might be caused by substances other than egg proteins and the importance of being prepared to recognize and manage serious hypersensitivity reactions when administering any vaccine to any recipient (regardless of allergy history).

Severe and life-threatening reactions to vaccines can rarely occur with any vaccine and in any vaccine recipient, regardless of allergy history. Providers are reminded that all vaccines should be administered in settings in which personnel and equipment needed for rapid recognition and treatment of acute hypersensitivity reactions are available. All vaccination providers should be familiar with their office emergency plan and be certified in cardiopulmonary resuscitation ( 110 ). No postvaccination observation period is recommended specifically for egg-allergic persons. However, ACIP recommends that vaccination providers consider observing patients (seated or supine) for 15 minutes after administration of any vaccine to decrease the risk for injury should syncope occur ( 110 ).

Although egg allergy is neither a contraindication nor precaution to the use of any influenza vaccine, there are contraindications and precautions related to allergies to vaccine components other than egg and to previous allergic reactions to influenza vaccines (see Persons with Previous Allergic Reactions to Influenza Vaccines and Dosage, Administration, Contraindications, and Precautions) (Tables 2 and 3).

Persons with Previous Allergic Reactions to Influenza Vaccines

As is the case for all vaccines, influenza vaccines contain various components that might cause allergic and anaphylactic reactions. Most influenza vaccine package inserts list among contraindications to their use a history of previous severe allergic reaction (e.g., anaphylaxis) to any component of the vaccine or to a previous dose of any influenza vaccine ( 51 , 52 , 54 – 56 , 58 , 59 ). For ccIIV3 and RIV3, a history of a severe allergic reaction to any vaccine component is listed as a contraindication; no labeled contraindication is specified for a history of allergic reaction to any other influenza vaccine ( 53 , 57 ). However, severe allergic reactions, although rare, can occur after influenza vaccination, even among persons with no previous reactions or known allergies. Vaccine components and excipients can be found in package inserts. However, identifying the causative agent without further evaluation (i.e., through evaluation and testing for specific allergies) can be difficult. Severe allergic reactions after vaccination with RIV have been reported to VAERS, certain of which have occurred among persons reporting previous allergic reactions to egg or to influenza vaccines and that might represent a predisposition to allergic manifestations in affected persons ( 107 – 109 ). Because these rare but severe allergic reactions can occur, ACIP recommends the following for persons with a history of severe allergic reaction to a previous dose of an influenza vaccine (Table 3):

A history of severe allergic reaction (e.g., anaphylaxis) to any influenza vaccine (i.e., any egg-based IIV, ccIIV, RIV, or LAIV of any valency) is a contraindication to future receipt of all egg-based IIV3s and LAIV3. Each individual egg-based IIV3 and LAIV3 is also contraindicated for persons who have had a severe allergic reaction (e.g., anaphylaxis) to any component of that vaccine (excluding egg; see Persons with a History of Egg Allergy).
A history of a severe allergic reaction (e.g., anaphylaxis) to any egg-based IIV, RIV, or LAIV of any valency is a precaution for the use of ccIIV3. If ccIIV3 is administered in such instances, vaccination should occur in an inpatient or outpatient medical setting and should be supervised by a health care provider who is able to recognize and manage severe allergic reactions. Providers also can consider consultation with an allergist to help determine the vaccine component responsible for the allergic reaction.
A history of a severe allergic reaction (e.g., anaphylaxis) to any ccIIV of any valency or to any component of ccIIV3 is a contraindication to future receipt of ccIIV3.
A history of a severe allergic reaction (e.g., anaphylaxis) to any egg-based IIV, ccIIV, or LAIV of any valency is a precaution for the use of RIV3. If RIV3 is administered in such instances, vaccination should occur in an inpatient or outpatient medical setting and should be supervised by a health care provider who is able to recognize and manage severe allergic reactions. Providers can also consider consultation with an allergist to help determine the vaccine component responsible for the allergic reaction.
A history of a severe allergic reaction (e.g., anaphylaxis) to any RIV of any valency or to any component of RIV3 is a contraindication to future receipt of RIV3.

Vaccination Issues for Travelers

In temperate climate regions of the Northern and Southern Hemispheres, influenza activity is seasonal, occurring during approximately October–May in the Northern Hemisphere and April–September in the Southern Hemisphere. In the tropics, influenza might occur throughout the year ( 111 ). The timing of influenza activity and predominant types and subtypes of influenza viruses in circulation vary by geographic region ( 112 ). Travelers can be exposed to influenza when traveling to an area where influenza is circulating or when traveling as part of large tourist groups (e.g., on cruise ships) that include persons from areas of the world where influenza viruses are circulating ( 113 – 116 ).

Travelers who want to reduce their risk for influenza should consider influenza vaccination, preferably at least 2 weeks before departure. In particular, persons who live in the United States and are at higher risk for influenza complications and who were not vaccinated with influenza vaccine during the previous Northern Hemisphere fall or winter should consider receiving influenza vaccination before departure if they plan to travel to the tropics, to the Southern Hemisphere during the Southern Hemisphere influenza season (April–September), or with organized tourist groups or on cruise ships to any location. Persons at higher risk who received the previous season’s influenza vaccine before travel should consult with their health care provider to discuss the risk for influenza and other travel-related diseases before embarking on travel during the summer. All persons (regardless of risk status) who are vaccinated in preparation for travel before the upcoming influenza season’s vaccine is available, or who received the immediately preceding Southern Hemisphere influenza vaccine, should receive the current U.S. seasonal influenza vaccine the following fall or winter.

Influenza vaccine formulated for the Southern Hemisphere might differ in viral composition from the Northern Hemisphere vaccine. For persons traveling to the Southern Hemisphere during the Southern Hemisphere influenza season, receipt of a current U.S.-licensed Southern Hemisphere influenza vaccine formulation before departure might be reasonable but might not be feasible because of limited access to or unavailability of Southern Hemisphere formulations in the United States. Most Southern Hemisphere influenza vaccine formulations are not licensed in the United States, and they are typically not commercially available. More information on influenza vaccines and travel is available at https://wwwnc.cdc.gov/travel/diseases/influenza-seasonal-zoonotic-and-pandemic . Additional information on global influenza surveillance by region is available at https://www.who.int/tools/flunet .

Use of Influenza Antiviral Medications

Administration of any IIV3 or RIV3 to persons receiving influenza antiviral medications for treatment or chemoprophylaxis of influenza is acceptable. Data concerning vaccination with LAIV3 in the setting of influenza antiviral use are not available. However, influenza antiviral medications might interfere with the action of LAIV3 because this vaccine contains live influenza viruses.

The package insert for LAIV3 notes that influenza antiviral agents might reduce the effectiveness of the vaccine if administered within the interval from 48 hours before to 14 days after vaccination ( 56 ). However, the newer influenza antivirals peramivir and baloxavir have longer half-lives than oseltamivir and zanamivir, approximately 20 hours for peramivir ( 117 ) and 79 hours for baloxavir ( 118 ), and could potentially interfere with the replication of LAIV3, if administered >48 hours before vaccination. Potential interactions between influenza antivirals and LAIV3 have not been studied, and the ideal intervals between administration of these medications and LAIV3 are not known. Assuming a period of at least 5 half-lives for substantial decrease in drug levels ( 119 ), a reasonable assumption is that peramivir might interfere with the mechanism of LAIV3 if administered from 5 days before through 2 weeks after vaccination and baloxavir might interfere if administered from 17 days before through 2 weeks after vaccination. The interval between influenza antiviral receipt and LAIV3 during which interference might occur could be further prolonged in the presence of medical conditions that delay medication clearance (e.g., renal insufficiency). Persons who receive these medications during these periods before or after receipt of LAIV3 should be revaccinated with another appropriate influenza vaccine (e.g., IIV3 or RIV3).

Administration of Influenza Vaccines with Other Vaccines

IIV3s and RIV3 can be administered simultaneously or sequentially with other inactivated vaccines or live vaccines. Injectable vaccines that are given concomitantly should be administered at separate anatomic sites. Vaccines that are administered at the same time as influenza vaccines that might be more likely to be associated with local injection site reactions (e.g., HD-IIV3 and aIIV3) should be given in different limbs, if possible. LAIV3 can be administered simultaneously with other live or inactivated vaccines. However, if two live vaccines are not given simultaneously, at least 4 weeks should pass after administration of one live vaccine (such as LAIV3) before another live vaccine is administered ( 110 ).

In recent years, multiple vaccines containing nonaluminum adjuvants have been licensed for use in the United States for the prevention of various infectious diseases. Examples include AS01 B (in Shingrix, recombinant zoster subunit vaccine [RZV]) ( 120 ), AS01 E (in Arexvy, respiratory syncytial virus vaccine) ( 121 ) MF59 (in Fluad [aIIV3]) ( 59 ), and cytosine phosphoguanine oligodeoxynucleotide (in Heplisav-B, recombinant hepatitis B surface antigen vaccine) ( 122 ). Data are limited regarding coadministration of these vaccines with other adjuvanted or nonadjuvanted vaccines, including COVID-19 vaccines. Coadministration of RZV with nonadjuvanted IIV4 has been studied, and no evidence of decreased immunogenicity or safety concerns was noted ( 123 ). A CISA RCT in persons aged ≥65 years found that the proportion of participants with at least one severe local or systemic reaction was not higher after simultaneous administration of RZV dose 1 and quadrivalent adjuvanted inactivated influenza vaccine compared with simultaneous administration of RZV dose 1 and quadrivalent high-dose inactivated influenza vaccine ( 124 ). Data on the immunogenicity and safety of simultaneous or sequential administration of two nonaluminum adjuvant–containing vaccines are limited, and the ideal interval between such vaccines when given sequentially is not known. In the study of Shingrix and nonadjuvanted IIV4 ( 123 ), most reactogenicity symptoms resolved within 4 days. Because of the limited data on the safety of simultaneous administration of two or more vaccines containing nonaluminum adjuvants and the availability of nonadjuvanted influenza vaccine options, selection of a nonadjuvanted influenza vaccine can be considered in situations in which influenza vaccine and another vaccine containing a nonaluminum adjuvant are to be administered concomitantly. However, influenza vaccination should not be delayed if a specific vaccine is not available. As recommended for all vaccines, vaccines with nonaluminum adjuvants should be administered at separate anatomic sites from other vaccines that are given concomitantly ( 110 ).

For more recently introduced and new vaccines, data informing simultaneous administration with influenza vaccines might be limited or evolving. Providers should consult current CDC/ACIP recommendations and guidance for up-to-date information.

Influenza Vaccine Composition for the 2024–25 Season

All influenza vaccines licensed in the United States will contain components derived from influenza viruses antigenically similar to those recommended by FDA ( https://www.fda.gov/advisory-committees/advisory-committee-calendar/vaccines-and-related-biological-products-advisory-committee-march-5-2024-meeting-announcement ) ( 125 ). All influenza vaccines expected to be available in the United States for the 2024–25 season will be trivalent vaccines. For the 2024–25 season, U.S. egg-based influenza vaccines (i.e., vaccines other than ccIIV3 and RIV3) will contain HA derived from

an influenza A/Victoria/4897/2022 (H1N1)pdm09-like virus,
an influenza A/Thailand/8/2022 (H3N2)-like virus, and
an influenza B/Austria/1359417/2021 (Victoria lineage)-like virus.

For the 2024–25 season, U.S. cell culture–based inactivated (ccIIV3) and recombinant (RIV3) influenza vaccines will contain HA derived from

an influenza A/Wisconsin/67/2022 (H1N1)pdm09-like virus,
an influenza A/Massachusetts/18/2022 (H3N2)-like virus, and
an influenza B/Austria/1359417/2021 (Victoria lineage)-like virus

Vaccines Available for the 2024–25 Season

Availability of specific types and brands of licensed seasonal influenza vaccines in the United States is determined by the manufacturers of the vaccines. Information presented concerning vaccines expected to be available and their approved indications and usage reflects current knowledge and is subject to change.

Various influenza vaccines will be available for the 2024–25 season (Table 1). For many vaccine recipients, more than one type or brand of vaccine might be appropriate within approved indications and ACIP recommendations. Current prescribing information and ACIP recommendations should be consulted for up-to-date information. Contraindications and precautions for the different types of influenza vaccines are summarized (Tables 2 and 3), as are dose volumes (Table 4).

Not all influenza vaccines are likely to be uniformly available in any specific practice setting or geographic locality. Vaccination should not be delayed to obtain a specific product when an appropriate one is available. Within these guidelines and approved indications, ACIP makes no preferential recommendation for the use of any one influenza vaccine over another when more than one licensed and recommended vaccine is available, except for selection of influenza vaccines for persons aged ≥65 years (see Older Adults).

Dosage, Administration, Contraindications, and Precautions

Trivalent inactivated influenza vaccines (iiv3s).

Available Vaccines. As in recent seasons, various inactivated influenza vaccines (IIVs) are expected to be available for 2024–25 (Table 1); all are expected to be trivalent (IIV3s). Standard-dose, nonadjuvanted IIV3s are licensed for persons aged as young as 6 months. However, for certain IIV3s, the approved dose volume for children aged 6 through 35 months differs from that for older children and adults (Table 4). Care should be taken to administer the appropriate dose volume. Two IIV3s, the MF59-adjuvanted IIV3 Fluad (aIIV3) and the high-dose IIV3 Fluzone High-Dose (HD-IIV3), are approved only for persons aged ≥65 years, but are acceptable options for solid organ transplant recipients aged 18 through 64 years who are receiving immunosuppressive medication regimens, without a preference over other age-appropriate IIV3s or RIV3.

Standard-dose, nonadjuvanted IIV3s contain 15 μ g of HA per vaccine virus in a 0.5-mL dose (7.5 μ g of HA per vaccine virus in a 0.25-mL dose). For 2024–25, this category is expected to include five different vaccines (Table 1). Four of these are egg-based vaccines (Afluria, Fluarix, FluLaval, and Fluzone), and one is a cell culture–based vaccine (Flucelvax [ccIIV3]). All are approved for persons aged ≥6 months. Egg-based and cell culture–based vaccines differ in the substrate in which reference vaccine viruses supplied to the manufacturer are propagated in quantities sufficient to produce the needed number of doses of vaccine. For the IIV3s Afluria ( 51 ), Fluarix ( 52 ), FluLaval ( 54 ), and Fluzone ( 55 ), reference vaccine viruses are propagated in eggs. For Flucelvax (ccIIV3), reference vaccine viruses are propagated in Madin-Darby canine kidney cells instead of eggs ( 53 ).

Two additional IIV3s that will be available for the 2024–25 season are approved only for persons aged ≥65 years. These vaccines are egg based. Trivalent high-dose inactivated influenza vaccine (Fluzone High-Dose; HD-IIV3) contains 60 μ g of HA per vaccine virus (180 μ g total) in a 0.5-mL dose ( 58 ). Trivalent adjuvanted inactivated influenza vaccine (Fluad; aIIV3) contains 15 μ g of HA per vaccine virus (45 μ g total) and MF59 adjuvant ( 59 ).

Dosage and Administration. Standard-dose nonadjuvanted IIV3s are approved for children aged as young as 6 months. Certain of these IIV3s are approved at different dose volumes for very young children than for older children and adults. Care should be taken to administer the correct dose volume for each needed dose (see Children Aged 6 Through 35 Months: Influenza Vaccine Dose Volumes) (Tables 1 and 4):

Afluria: The approved dose volume for children aged 6 through 35 months is 0.25 mL per dose. Persons aged ≥36 months (≥3 years) should receive 0.5 mL per dose ( 51 ).
Fluarix: The approved dose volume is 0.5 mL per dose for all persons aged ≥6 months ( 52 ).
Flucelvax: The approved dose volume is 0.5 mL per dose for all persons aged ≥6 months ( 53 ).
FluLaval: The approved dose volume is 0.5 mL per dose for all persons aged ≥6 months ( 54 ).
Fluzone: The approved dose volume for children aged 6 through 35 months is either 0.25 mL or 0.5 mL per dose. Persons aged ≥36 months (≥3 years) should receive 0.5 mL per dose ( 55 ).

If prefilled syringes are not available, the appropriate volume can be administered from a multidose vial. Of note, dose volume is distinct from the number of doses. Children in this age group who require 2 doses for 2024–25 need 2 separate doses administered ≥4 weeks apart, regardless of the specific IIV3 used and volume given for each dose (see Children Aged 6 Months Through 8 Years: Number of Influenza Vaccine Doses) (Figure).

For children aged 36 months (3 years) through 17 years and adults aged ≥18 years, the dose volume for all IIV3s is 0.5 mL per dose. If a smaller vaccine dose (e.g., 0.25 mL) is inadvertently administered to a person aged ≥36 months, the remaining volume needed to make a full dose should be administered during the same vaccination visit or, if measuring the needed remaining volume is a challenge, administering a repeat dose at the full volume is acceptable. If the error is discovered later (after the recipient has left the vaccination setting), a full dose should be administered as soon as the recipient can return. Vaccination with a formulation approved for adult use should be counted as a single dose if inadvertently administered to a child.

IIV3s are administered intramuscularly (IM). For adults and older children, the deltoid muscle is the preferred site. Infants and younger children should be vaccinated in the anterolateral thigh. Additional specific guidance regarding site selection and needle length for IM injection is provided in the General Best Practice Guidelines for Immunization ( 110 ). One IIV3, Afluria, is licensed for IM injection via the PharmaJet Stratis jet injector for persons aged 18 through 64 years ( 51 ). Persons in this age group can receive Afluria via either needle and syringe or this specific jet injection device. Children aged 6 months through 17 years and adults aged ≥65 years should receive this vaccine by needle and syringe only. No other IIV3s are licensed for administration by jet injector.

Contraindications and Precautions for the Use of IIV3s. Manufacturer package inserts and updated CDC and ACIP guidance should be consulted for information on contraindications and precautions for individual influenza vaccines. Each IIV3, whether egg based or cell culture based, has a labeled contraindication for persons with a history of a severe allergic reaction to any component of that vaccine (Tables 2 and 3). However, although egg is a component of all IIV3s other than ccIIV3, ACIP makes specific recommendations for the use of influenza vaccine for persons with egg allergy (see Persons with a History of Egg Allergy). All egg-based IIV3s are contraindicated in persons who have had a severe allergic reaction (e.g., anaphylaxis) to a previous dose of any influenza vaccine (any egg-based IIV, ccIIV, RIV, or LAIV of any valency). Use of ccIIV3 is contraindicated in persons who have had a severe allergic reaction (e.g., anaphylaxis) to any ccIIV of any valency. A history of severe allergic reaction (e.g., anaphylaxis) to any other influenza vaccine (i.e., any egg-based IIV, RIV, or LAIV of any valency) is a precaution for the use of ccIIV3 (see Persons with Previous Allergic Reactions to Influenza Vaccines) (Tables 2 and 3). If ccIIV3 is administered in such an instance, vaccination should occur in an inpatient or outpatient medical setting and should be supervised by a health care provider who is able to recognize and manage severe allergic reactions. Providers can also consider consultation with an allergist to help identify the vaccine component responsible for the reaction. Information about vaccine components can be found in the package inserts for each vaccine. Prophylactic use of antiviral agents is an option that can be considered for preventing influenza among persons who cannot receive vaccine, particularly for those who are at higher risk for medical complications attributable to severe influenza ( 104 ).

Moderate or severe acute illness with or without fever is a general precaution for vaccination ( 110 ). A history of GBS within 6 weeks after receipt of a previous dose of influenza vaccine is considered a precaution for the use of all influenza vaccines (Table 2).

Trivalent Recombinant Influenza Vaccine (RIV3)

Available Vaccine. One recombinant influenza vaccine, Flublok (RIV3), is expected to be available during the 2024–25 influenza season. RIV3 is approved for persons aged ≥18 years. This vaccine contains recombinant HA produced in an insect cell line using genetic sequences from cell-derived influenza viruses and is manufactured without the use of influenza viruses or eggs ( 57 ).

Dosage and Administration . RIV3 is administered by IM injection via needle and syringe. A 0.5-mL dose contains 45 μ g of HA derived from each vaccine virus (135 μ g total).

Contraindications and Precautions for the Use of RIV3. Manufacturer package inserts and updated CDC and ACIP guidance should be consulted for information on contraindications and precautions for individual influenza vaccines. RIV3 is contraindicated in persons who have had a severe allergic reaction (e.g., anaphylaxis) to a previous dose of any RIV of any valency or to any component of RIV3. A history of a severe allergic reaction (e.g., anaphylaxis) to any other influenza vaccine (i.e., any egg-based IIV, ccIIV, or LAIV of any valency) is a precaution for the use of RIV3. If RIV3 is administered in such an instance, vaccination should occur in an inpatient or outpatient medical setting and should be supervised by a health care provider who is able to recognize and manage severe allergic reactions. Providers can also consider consulting with an allergist to help identify the vaccine component responsible for the reaction (Tables 2 and 3).

Trivalent Live Attenuated Influenza Vaccine (LAIV3)

Available Vaccine. One live attenuated influenza vaccine, FluMist (LAIV3), is expected to be available during the 2024–25 influenza season. LAIV3 is approved for persons aged 2 through 49 years. LAIV3 contains live attenuated influenza viruses that are propagated in eggs. These viruses are cold adapted (so that they replicate efficiently at 25°C [77°F]) and temperature sensitive (so that their replication is restricted at higher temperatures, 39°C [102.2°F] for influenza A viruses and 37°C [98.6°] for influenza B viruses). The live attenuated vaccine viruses replicate in the nasopharynx, which is necessary to promote an immune response ( 56 ). No preference is expressed for LAIV3 versus other influenza vaccines used within specified indications.

Dosage and Administration. LAIV3 is administered intranasally using the supplied prefilled, single-use sprayer containing 0.2 mL of vaccine. Approximately 0.1 mL (i.e., one half of the total sprayer contents) is sprayed into the first nostril while the recipient is in the upright position. An attached dose-divider clip is removed from the sprayer to permit administration of the second half of the dose into the other nostril. Sniffing of the dose is not necessary. If the recipient sneezes immediately after administration, the dose should not be repeated. However, if nasal congestion is present that might impede delivery of the vaccine to the nasopharyngeal mucosa, deferral of administration should be considered until resolution of the illness, or another appropriate vaccine should be administered instead. Each total dose of 0.2 mL contains 10 6.5–7.5 fluorescent focus units of each vaccine virus ( 56 ).

Contraindications and Precautions for the Use of LAIV3. Manufacturer package inserts and updated CDC and ACIP guidance should be consulted for information on contraindications and precautions for individual influenza vaccines. Conditions considered by ACIP to be contraindications and precautions for the use of LAIV3 are summarized (Table 2). These include two labeled contraindications that appear in the package insert ( 56 ) and other conditions for which there is either uncertain but biologically plausible potential risk associated with live viruses or limited data for use of LAIV. Contraindications to use of LAIV3 include the following (Tables 2 and 3):

Severe allergic reaction (e.g., anaphylaxis) to any component of the vaccine or to a previous dose of any influenza vaccine (i.e., any egg-based IIV, ccIIV, RIV, or LAIV of any valency; a labeled contraindication noted in the package insert). However, although egg is a component of LAIV3, ACIP makes specific recommendations for the use of influenza vaccine for persons with egg allergy (see Persons with a History of Egg Allergy).
Children and adolescents receiving concomitant aspirin- or salicylate-containing medications, because of the potential risk for Reye syndrome (a labeled contraindication noted in the package insert).
Children aged 2 through 4 years who have received a diagnosis of asthma or whose parents or caregivers report that a health care provider has told them during the preceding 12 months that their child had wheezing or asthma or whose medical record indicates a wheezing episode has occurred during the preceding 12 months.
Children and adults who are immunocompromised due to any cause, including but not limited to immunosuppression caused by medications, congenital or acquired immunodeficiency states, HIV infection, anatomic asplenia, or functional asplenia (such as that due to sickle cell anemia).
Close contacts and caregivers of severely immunosuppressed persons who require a protected environment.
Persons with active communication between the cerebrospinal fluid (CSF) and the oropharynx, nasopharynx, nose, or ear or any other cranial CSF leak.
Persons with cochlear implants, because of the potential for CSF leak that might exist for a period after implantation (providers might consider consultation with a specialist concerning the risk for persistent CSF leak if an inactivated or recombinant vaccine cannot be used).
Receipt of influenza antiviral medication within the previous 48 hours for oseltamivir and zanamivir, previous 5 days for peramivir, and previous 17 days for baloxavir. The interval between influenza antiviral receipt and LAIV3 during which interference might potentially occur might be further prolonged in the presence of medical conditions that delay medication clearance (e.g., renal insufficiency).

Precautions to the use of LAIV3 include the following (Tables 2 and 3):

Moderate or severe acute illness with or without fever.
History of GBS within 6 weeks after receipt of any influenza vaccine.
Asthma in persons aged ≥5 years.
Other underlying medical condition (other than those listed under contraindications) that might predispose to complications after wild-type influenza virus infection (e.g., chronic pulmonary, cardiovascular [except isolated hypertension], renal, hepatic, neurologic, hematologic, or metabolic disorders [including diabetes mellitus]).

In all instances, approved manufacturer packaging information should be consulted for authoritative guidance concerning storage and handling of specific influenza vaccines. Typically, influenza vaccines should be protected from light and stored at temperatures that are recommended in the package insert. Recommended storage temperatures are typically 36°F–46°F (2°C–8°C) and should be maintained at all times with adequate refrigeration and temperature monitoring. Vaccine that has frozen should be discarded. Specific recommendations for appropriate refrigerators and temperature monitoring equipment can be found in the Vaccine Storage and Handling Toolkit, available at https://www.cdc.gov/vaccines/hcp/storage-handling/?CDC_AAref_Val=https://www.cdc.gov/vaccines/hcp/admin/storage/toolkit/index.html .

Vaccines should not be used beyond the expiration date on the label. In addition to the expiration date, multidose vials also might have a beyond-use date (BUD), which specifies the number of days the vaccine can be kept once first accessed. After being accessed for the first dose, multidose vials should not be used after the BUD. If no BUD is provided, then the listed expiration date is to be used. Multidose vials should be returned to recommended storage conditions between uses. Package information might also specify a maximum number of doses contained in multidose vials (regardless of remaining volume). No more than the specified number of doses should be removed, and any remainder should be discarded. Providers should contact the manufacturer for information on permissible temperature excursions and other departures from recommended storage and handling conditions that are not discussed in the package labeling.

Influenza Surveillance, Prevention, and Control

Updated information regarding influenza surveillance, detection, prevention, and control is available at https://www.cdc.gov/flu . U.S. surveillance data are updated weekly throughout the year on FluView ( https://www.cdc.gov/flu/weekly ) and can be viewed in FluView Interactive ( https://www.cdc.gov/flu/weekly/fluviewinteractive.htm ). In addition, periodic updates regarding influenza are published in MMWR ( https://www.cdc.gov/mmwr/index.html ). Additional information regarding influenza and influenza vaccines can be obtained from CDCINFO by calling 1–800–232–4636. State and local health departments should be consulted about availability of influenza vaccines, access to vaccination programs, information related to state or local influenza activity, reporting of influenza outbreaks and influenza-related pediatric deaths, and advice concerning outbreak control.

Vaccine Adverse Event Reporting System (VAERS)

The National Childhood Vaccine Injury Act of 1986 requires health care providers to report any adverse event listed by the vaccine manufacturer as a contraindication to future doses of the vaccine or any adverse event listed in the VAERS Table of Reportable Events Following Vaccination ( https://vaers.hhs.gov/docs/VAERS_Table_of_Reportable_Events_Following_Vaccination.pdf ) that occurs within the specified period after vaccination. In addition to mandated reporting, health care providers are encouraged to report any clinically significant adverse event after vaccination to VAERS. Information on how to report a vaccine adverse event is available at https://vaers.hhs.gov/index.html .

National Vaccine Injury Compensation Program (VICP)

The National Vaccine Injury Compensation Program (VICP), established by the National Childhood Vaccine Injury Act of 1986, as amended, is a no-fault alternative to the traditional tort system. It provides compensation to persons found to be injured by certain vaccines. VICP covers most vaccines routinely given in the United States. The Vaccine Injury Table ( https://www.hrsa.gov/sites/default/files/hrsa/vicp/vaccine-injury-table-01-03-2022.pdf ) lists the vaccines covered by VICP and the associated injuries and conditions that might receive a legal presumption of causation. If the injury or condition is not in the table or does not meet the requirements in the table, persons must prove that the vaccine caused the injury or condition. Claims must be filed within specified time frames. Persons of all ages who receive a VICP-covered vaccine might be eligible to file a claim. Additional information is available at https://www.hrsa.gov/vaccine-compensation or by calling 1–800–338–2382.

Additional Resources

Acip statements.

Recommended Adult Immunization Schedule for Ages 19 Years or Older, United States: https://www.cdc.gov/vaccines/hcp/imz-schedules/adult-age.html?CDC_AAref_Val=https://www.cdc.gov/vaccines/schedules/hcp/imz/adult.html
Recommended Child and Adolescent Immunization Schedule for Ages 18 Years or Younger, United States: https://www.cdc.gov/vaccines/schedules/hcp/imz/child-adolescent.html
Immunization of Health Care Personnel: Recommendations of the Advisory Committee on Immunization Practices (ACIP), 2011. MMWR Recomm Rep 2011;60(No.RR-7):1–45: https://www.cdc.gov/mmwr/preview/mmwrhtml/rr6007a1.htm

General Best Practice Guidelines for Immunization:

General Best Practice Guidelines for Immunization: https://www.cdc.gov/vaccines/hcp/acip-recs/general-recs/index.html

COVID-19 Vaccine Recommendations and Guidance

ACIP recommendations for the use of COVID-19 vaccines: https://www.cdc.gov/acip-recs/hcp/vaccine-specific/covid-19.html?CDC_AAref_Val=https://www.cdc.gov/vaccines/hcp/acip-recs/vacc-specific/covid-19.html
Clinical Care Considerations for COVID-19 Vaccination: https://www.cdc.gov/vaccines/covid-19/clinical-considerations/index.html
Use of COVID-19 Vaccines in the United States—Interim Clinical Considerations: https://www.cdc.gov/vaccines/covid-19/clinical-considerations/covid-19-vaccines-us.html
FDA COVID-19 Vaccines page: https://www.fda.gov/emergency-preparedness-and-response/coronavirus-disease-2019-covid-19/covid-19-vaccines

Vaccine Information Sheets

IIV3 and RIV3: https://www.cdc.gov/vaccines/hcp/vis/vis-statements/flu.pdf
LAIV3: https://www.cdc.gov/vaccines/hcp/vis/vis-statements/flulive.pdf

Influenza Vaccine Package Inserts

https://www.fda.gov/vaccines-blood-biologics/vaccines/vaccines-licensed-use-united-states

CDC Influenza Antiviral Guidance

Influenza Antiviral Medications: Summary for Clinicians: https://www.cdc.gov/flu/professionals/antivirals/summary-clinicians.htm

Infectious Diseases Society of America Influenza Antiviral Guidance

Clinical Practice Guidelines by the Infectious Diseases Society of America: 2018 Update on Diagnosis, Treatment, Chemoprophylaxis, and Institutional Outbreak Management of Seasonal Influenza: https://academic.oup.com/cid/article/68/6/e1/5251935
American Academy of Pediatrics Guidance
American Academy of Pediatrics Recommendations for Prevention and Control of Influenza in Children (Red Book Online): https://publications.aap.org/redbook

Infectious Diseases Society of America Guidance for Vaccination of Immunocompromised Hosts

2013 IDSA Clinical Practice Guideline for Vaccination of the Immunocompromised Host: https://academic.oup.com/cid/article/58/3/e44/336537

American College of Obstetricians and Gynecologists

Influenza in Pregnancy: Prevention and Treatment: https://www.acog.org/clinical/clinical-guidance/committee-statement/articles/2024/02/influenza-in-pregnancy-prevention-and-treatment

Voting members of the Advisory Committee on Immunization Practices: Helen Keipp Talbot, MD, Vanderbilt University, Nashville, Tennessee (Chair); Oliver Brooks, MD, Watts HealthCare Corporation, Los Angeles, California; Wilbur H. Chen, MD, University of Maryland School of Medicine, Baltimore, Maryland; Sybil Cineas, MD, Warren Alpert Medical School of Brown University, Providence, Rhode Island; Matthew F. Daley, MD, Kaiser Permanente Colorado, Aurora, Colorado; Denise J. Jamieson, MD, Carver College of Medicine, University of Iowa, Iowa City, Iowa; Camille Nelson Kotton, MD, Harvard Medical School, Boston, Massachusetts; Jamie Loehr, MD, Cayuga Family Medicine, Ithaca, New York; Sarah S. Long, MD, Drexel University College of Medicine, Philadelphia, Pennsylvania; Yvonne Maldonado, MD, Stanford University School of Medicine, Palo Alto, California; Robert Schechter, MD, California Department of Public Health, Richmond, California; Albert Shaw, MD, Yale School of Medicine, New Haven, Connecticut.

Alicia Budd, MPH; Jessie Chung, MPH; Sascha Ellington, PhD; Brendan Flannery, PhD; Andrew Kroger, MD; Samantha Olson, MPH; David Shay, MD; Tom Shimabukuro, MD; and Tim Uyeki, MD; CDC.

Jamie Loehr, MD, Ithaca, New York (Chair); Robert Atmar, MD, Houston, Texas; Kevin Ault, MD, Kalamazoo, Michigan; Edward Belongia, MD, Marshfield, Wisconsin; Henry Bernstein, DO, Hempstead, New York; Thomas Boyce, MD, Marshfield, Wisconsin; Timothy Brennan, MD, Silver Spring, Maryland; Kristina Angel Bryant, MD, Louisville, Kentucky; Doug Campos-Outcalt, MD, Phoenix, Arizona; Uzo Chukwuma, PhD, Rockville, Maryland; Sarah Coles, MD, Phoenix, Arizona; Frances Ferguson, MD, Newton, Georgia; Alicia Fry, MD, Atlanta, Georgia; Sandra Adamson Fryhofer, MD, Atlanta, Georgia; Krissy Moehling Geffel, PhD, Pittsburgh, Pennsylvania; Michael Ison, MD, Rockville, Maryland; Wendy Keitel, MD, Houston, Texas; Camille Kotton, MD, Boston, Massachusetts; Marie-Michèle Léger, MPH, Alexandria, Virginia; Susan Lett, MD, Boston, Massachusetts; Zackary Moore, MD, Raleigh, North Carolina; Rebecca L. Morgan, PhD, Cleveland, Ohio; Cynthia Nolletti, MD, Silver Spring, Maryland; Jesse Papenburg, MD, Montreal, Quebec, Canada; Jo Resnick, PhD, Silver Spring, Maryland; Chris Roberts, PhD; Rockville, Maryland; William Schaffner, MD, Nashville, Tennessee; Robert Schechter, MD, Richmond, California; Kenneth Schmader, MD, Durham, North Carolina; Tamara Sheffield, MD, Salt Lake City, Utah; Angela Sinilaite, MPH, Ottawa, Ontario, Canada; Peter Szilagyi, MD, Los Angeles, California; Helen Keipp Talbot, MD, Nashville, Tennessee Matthew Zahn, MD, Santa Ana, California.

Corresponding author : Lisa A. Grohskopf, Influenza Division, National Center for Immunization and Respiratory Diseases, CDC. Telephone: 404-639-2552; Email: [email protected] .

1 Influenza Division, National Center for Immunization and Respiratory Diseases, CDC; 2 Immunization Safety Office, National Center for Emerging and Zoonotic Infectious Diseases, CDC; 3 Jamie Loehr, MD, Cayuga Family Medicine, Ithaca, New York

Disclosure of Relationship and Unlabeled Use

All authors have completed and submitted the International Committee of Medical Journal Editors form for the disclosure of potential conflicts of interest. No potential conflicts of interest were disclosed.

This report includes discussion of the unlabeled use of influenza vaccines in the recommendations for persons with a history of egg allergy and for solid organ transplant recipients aged 18 through 64 years. With regard to persons with a history of egg allergy, history of severe allergic reaction (e.g., anaphylaxis) to the vaccine or any of its components (which include egg for certain vaccines) is a labeled contraindication to receipt of most IIV3s and LAIV3. However, ACIP recommends that all persons aged ≥6 months with egg allergy should receive influenza vaccine. Any influenza vaccine (egg based or nonegg based) that is otherwise appropriate for the recipient’s age and health status can be used. With regard to solid organ transplant recipients aged 18 through 64 years, the high-dose inactivated influenza vaccine (HD-IIV3) and adjuvanted inactivated influenza vaccine (aIIV3) are approved for persons aged ≥65 years. However, ACIP recommends that solid organ transplant recipients aged 18 through 64 years who are receiving immunosuppressive medication regimens may receive either HD-IIV3 or aIIV3 as acceptable options, without a preference over other age-appropriate IIV3s or RIV3.

CDC Adoption of ACIP Recommendations for MMWR Recommendations and Reports, MMWR Policy Notes, and Immunization Schedules (Child/Adolescent, Adult)

Recommendations for routine use of vaccines in children, adolescents, and adults are developed by the Advisory Committee on Immunization Practices (ACIP). ACIP is chartered as a Federal Advisory Committee to provide expert external advice and guidance to the Director of CDC on use of vaccines and related agents for the control of vaccine preventable diseases in the civilian population of the United States. Recommendations for routine use of vaccines in children and adolescents are harmonized to the greatest extent possible with recommendations made by the American Academy of Pediatrics (AAP), the American Academy of Family Physicians (AAFP), the American College of Obstetricians and Gynecologists (ACOG), the American College of Nurse-Midwives (ACNM), the American Academy of Physician Associates (AAPA), and the National Association of Pediatric Nurse Practitioners (NAPNAP). Recommendations for routine use of vaccinations in adults are harmonized with recommendations of AAFP, ACOG, ACNM, AAPA, the American College of Physicians (ACP), the American Pharmacists Association (APhA), and the Society for Healthcare Epidemiology of America (SHEA). ACIP recommendations are forwarded to CDC’s Director and once adopted become official CDC policy. These recommendations are then published in CDC’s Morbidity and Mortality Weekly Report (MMWR). Additional information is available at https://www.cdc.gov/vaccines/acip .

Barker WH. Excess pneumonia and influenza associated hospitalization during influenza epidemics in the United States, 1970-78. Am J Public Health 1986;76:761–5. https://doi.org/10.2105/AJPH.76.7.761 PMID:3717461
Barker WH, Mullooly JP. Impact of epidemic type A influenza in a defined adult population. Am J Epidemiol 1980;112:798–811. https://doi.org/10.1093/oxfordjournals.aje.a113052 PMID:7457471
Coleman BL, Fadel SA, Fitzpatrick T, Thomas SM. Risk factors for serious outcomes associated with influenza illness in high- versus low- and middle-income countries: Systematic literature review and meta-analysis. Influenza Other Respir Viruses 2018;12:22–9. https://doi.org/10.1111/irv.12504 PMID:29197154
Mullooly JP, Bridges CB, Thompson WW, et al. Influenza- and RSV-associated hospitalizations among adults. Vaccine 2007;25:846–55. https://doi.org/10.1016/j.vaccine.2006.09.041 PMID:17074423
Poehling KA, Edwards KM, Griffin MR, et al. The burden of influenza in young children, 2004-2009. Pediatrics 2013;131:207–16. https://doi.org/10.1542/peds.2012-1255 PMID:23296444
Poehling KA, Edwards KM, Weinberg GA, et al. The underrecognized burden of influenza in young children. N Engl J Med 2006;355:31–40. https://doi.org/10.1056/NEJMoa054869 PMID:16822994
Siston AM, Rasmussen SA, Honein MA, et al. Pandemic 2009 influenza A(H1N1) virus illness among pregnant women in the United States. JAMA 2010;303:1517–25. https://doi.org/10.1001/jama.2010.479 PMID:20407061
Fragaszy EB, Warren-Gash C, White PJ, et al. Effects of seasonal and pandemic influenza on health-related quality of life, work and school absence in England: Results from the Flu Watch cohort study. Influenza Other Respir Viruses 2018;12:171–82. https://doi.org/10.1111/irv.12506 PMID:28991409
Van Wormer JJ, King JP, Gajewski A, McLean HQ, Belongia EA. Influenza and workplace productivity loss in working adults. J Occup Environ Med 2017;59:1135–9. https://doi.org/10.1097/JOM.0000000000001120 PMID:28759481
Willis GA, Preen DB, Richmond PC, et al. The impact of influenza infection on young children, their family and the health care system. Influenza Other Respir Viruses 2019;13:18–27. https://doi.org/10.1111/irv.12604 PMID:30137663
Fiore AE, Uyeki TM, Broder K, et al. Prevention and control of influenza with vaccines: recommendations of the Advisory Committee on Immunization Practices (ACIP), 2010. MMWR Recomm Rep 2010;59(No. RR-8):1–62. PMID:20689501
CDC. How flu vaccine effectiveness and efficacy are measured: questions and answers. Atlanta, GA: US Department of Health and Human Services, CDC; 2023. https://www.cdc.gov/flu/vaccines-work/effectivenessqa.htm
Rolfes MA, Foppa IM, Garg S, et al. Annual estimates of the burden of seasonal influenza in the United States: A tool for strengthening influenza surveillance and preparedness. Influenza Other Respir Viruses 2018;12:132–7. https://doi.org/10.1111/irv.12486 PMID:29446233
Rolfes MA, Flannery B, Chung JR, et al. Effects of influenza vaccination in the United States during the 2017–2018 influenza season. Clin Infect Dis 2019;69:1845–53. https://doi.org/10.1093/cid/ciz075 PMID:30715278
Grohskopf LA, Blanton LH, Ferdinands JM, et al. Prevention and control of seasonal influenza with vaccines: recommendations of the Advisory Committee on Immunization Practices—United States, 2023–24 Influenza Season. MMWR Recomm Rep 2022;72(No. RR-2):1–25.
Ahmed F. US Advisory Committee on Immunization Practices (ACIP) handbook for developing evidence-based recommendations, version 1.2. Atlanta, GA: US Department of Health and Human Services, CDC; 2013.
CDC. Advisory Committee on Immunization Practices (ACIP): evidence-based recommendations—GRADE. Atlanta, GA: US Department of Health and Human Services, CDC; 2024. https://www.cdc.gov/vaccines/acip/recs/grade/about-grade.html
World Health Organization. Recommended composition of influenza virus vaccines for use in the 2024 southern hemisphere influenza season. Genveva, Switzerland: World Health Organization; 2024. https://www.who.int/publications/m/item/recommended-composition-of-influenza-virus-vaccines-for-use-in-the-2024-southern-hemisphere-influenza-season
Paget J, Caini S, Del Riccio M, van Waarden W, Meijer A. Has influenza B/Yamagata become extinct and what implications might this have for quadrivalent influenza vaccines? Euro Surveill 2022;27:2200753 . https://doi.org/10.2807/1560-7917.ES.2022.27.39.2200753 PMID:36177871
CDC. Flu season. Atlanta, GA: US Department of Health and Human Services, CDC; 2023. https://www.cdc.gov/flu/about/season
Belongia EA, Sundaram ME, McClure DL, Meece JK, Ferdinands J, VanWormer JJ. Waning vaccine protection against influenza A (H3N2) illness in children and older adults during a single season. Vaccine 2015;33:246–51. https://doi.org/10.1016/j.vaccine.2014.06.052 PMID:24962752
Castilla J, Martínez-Baz I, Martínez-Artola V, et al. Decline in influenza vaccine effectiveness with time after vaccination, Navarre, Spain, season 2011/12. Euro Surveill 2013;18:20388. https://doi.org/10.2807/ese.18.05.20388-en PMID:23399423
Ferdinands JM, Fry AM, Reynolds S, et al. Intraseason waning of influenza vaccine protection: Evidence from the US Influenza Vaccine Effectiveness Network, 2011-12 through 2014-15. Clin Infect Dis 2017;64:544–50. PMID:28039340
Ferdinands JM, Gaglani M, Martin ET, et al. Waning vaccine effectiveness against influenza-associated hospitalizations among adults, 2015–2016 to 2018–2019, United States Hospitalized Adult Influenza Vaccine Effectiveness Network. Clin Infect Dis 2021;73:726–9. https://doi.org/10.1093/cid/ciab045 PMID:33462610
Kissling E, Nunes B, Robertson C, et al. I-MOVE multicentre case-control study 2010/11 to 2014/15: Is there within-season waning of influenza type/subtype vaccine effectiveness with increasing time since vaccination? Euro Surveill 2016;21:30201. https://doi.org/10.2807/1560-7917.ES.2016.21.16.30201 PMID:27124420
Kissling E, Valenciano M, Larrauri A, et al. Low and decreasing vaccine effectiveness against influenza A(H3) in 2011/12 among vaccination target groups in Europe: results from the I-MOVE multicentre case-control study. Euro Surveill 2013;18:20390. https://doi.org/10.2807/ese.18.05.20390-en PMID:23399425
Mira-Iglesias A, López-Labrador FX, García-Rubio J, et al. Influenza vaccine effectiveness and waning effect in hospitalized older adults. Valencia Region, Spain, 2018/2019 Season. Int J Environ Res Public Health 2021;18:1129. https://doi.org/10.3390/ijerph18031129 PMID:33514058
Ng Y, Nandar K, Chua LAV, et al. Evaluating the effectiveness of the influenza vaccine during respiratory outbreaks in Singapore’s long term care facilities, 2017. Vaccine 2019;37:3925–31. https://doi.org/10.1016/j.vaccine.2019.03.054 PMID:31160102
Pebody R, Andrews N, McMenamin J, et al. Vaccine effectiveness of 2011/12 trivalent seasonal influenza vaccine in preventing laboratory-confirmed influenza in primary care in the United Kingdom: evidence of waning intra-seasonal protection. Euro Surveill 2013;18:20389. https://doi.org/10.2807/ese.18.05.20389-en PMID:23399424
Powell LN, Bégué RE. Influenza Vaccine Effectiveness Among Children for the 2017-2018 Season. J Pediatric Infect Dis Soc 2020;9:468–73. https://doi.org/10.1093/jpids/piz077 PMID:31774120
Puig-Barberà J, Mira-Iglesias A, Tortajada-Girbés M, et al. Waning protection of influenza vaccination during four influenza seasons, 2011/2012 to 2014/2015. Vaccine 2017;35:5799–807. https://doi.org/10.1016/j.vaccine.2017.09.035 PMID:28941618
Radin JM, Hawksworth AW, Myers CA, Ricketts MN, Hansen EA, Brice GT. Influenza vaccine effectiveness: Maintained protection throughout the duration of influenza seasons 2010-2011 through 2013-2014. Vaccine 2016;34:3907–12. https://doi.org/10.1016/j.vaccine.2016.05.034 PMID:27265447
Ray GT, Lewis N, Klein NP, et al. Intraseason waning of influenza vaccine effectiveness. Clin Infect Dis 2019;68:1623–30. https://doi.org/10.1093/cid/ciy770 PMID:30204855
Young BE, Mak TM, Ang LW, et al. Influenza vaccine failure in the tropics: a retrospective cohort study of waning effectiveness. Epidemiol Infect 2020;148:e299. https://doi.org/10.1017/S0950268820002952 PMID:33261680
Hu W, Sjoberg PA, Fries AC, DeMarcus LS, Robbins AS. Waning vaccine protection against influenza among Department of Defense adult beneficiaries in the United States, 2016–2017 through 2019–2020 influenza seasons. Vaccines (Basel) 2022;10:888. https://doi.org/10.3390/vaccines10060888 PMID:35746496
Regan AK, Fielding JE, Chilver MB, et al. Intraseason decline in influenza vaccine effectiveness during the 2016 southern hemisphere influenza season: A test-negative design study and phylogenetic assessment. Vaccine 2019;37:2634–41 . https://doi.org/10.1016/j.vaccine.2019.02.027 PMID:30952499
Sahni LC, Naioti EA, Olson SM, et al. Sustained within-season vaccine effectiveness against influenza-associated hospitalization in children: evidence from the New Vaccine Surveillance Network, 2015–2016 through 2019–2020. Clin Infect Dis 2023;76:e1031–9. https://doi.org/10.1093/cid/ciac577 PMID:35867698
Uemura K, Ono S, Michihata N, Yamana H, Yasunaga H. Duration of influenza vaccine effectiveness in the elderly in Japan: A retrospective cohort study using large-scale population-based registry data. Vaccine 2023;41:3092–8. https://doi.org/10.1016/j.vaccine.2023.03.066 PMID:37045684
Chung H, Campitelli MA, Buchan SA, et al. Measuring waning protection from seasonal influenza vaccination during nine influenza seasons, Ontario, Canada, 2010/11 to 2018/19. Euro Surveill 2024;29:2300239. https://doi.org/10.2807/1560-7917.ES.2024.29.8.2300239 PMID:38390652
Domnich A, Orsi A, Signori A, et al. Waning intra-season vaccine effectiveness against influenza A(H3N2) underlines the need for more durable protection. Expert Rev Vaccines 2024;23:380–8. https://doi.org/10.1080/14760584.2024.2331073 PMID:38494919
Madhi SA, Cutland CL, Kuwanda L, et al. Influenza vaccination of pregnant women and protection of their infants. N Engl J Med 2014;371:918–31. https://doi.org/10.1056/NEJMoa1401480 PMID:25184864
Steinhoff MC, Katz J, Englund JA, et al. Year-round influenza immunisation during pregnancy in Nepal: a phase 4, randomised, placebo-controlled trial. Lancet Infect Dis 2017;17:981–9. https://doi.org/10.1016/S1473-3099(17)30252-9 PMID:28522338
Tapia MD, Sow SO, Tamboura B, et al. Maternal immunisation with trivalent inactivated influenza vaccine for prevention of influenza in infants in Mali: a prospective, active-controlled, observer-blind, randomised phase 4 trial. Lancet Infect Dis 2016;16:1026–35. https://doi.org/10.1016/S1473-3099(16)30054-8 PMID:27261067
Zaman K, Roy E, Arifeen SE, et al. Effectiveness of maternal influenza immunization in mothers and infants. N Engl J Med 2008;359:1555–64. https://doi.org/10.1056/NEJMoa0708630 PMID:18799552
Young B, Sadarangani S, Jiang L, Wilder-Smith A, Chen MI. Duration of influenza vaccine effectiveness: a systematic review, meta-analysis, and meta-regression of test-negative design case-control studies. J Infect Dis 2018;217:731–41. https://doi.org/10.1093/infdis/jix632 PMID:29220496
Ferdinands JM, Alyanak E, Reed C, Fry AM. Waning of influenza vaccine protection: exploring the trade-offs of changes in vaccination timing among older adults. Clin Infect Dis 2020;70:1550–9. https://doi.org/10.1093/cid/ciz452 PMID:31257422
Smith KJ, France G, Nowalk MP, et al. Compressed influenza vaccination in U.S. older adults: a decision analysis. Am J Prev Med 2019;56:e135–41. https://doi.org/10.1016/j.amepre.2018.11.015 PMID:30772149
Newall AT, Chen C, Wood JG, Stockwell MS. Within-season influenza vaccine waning suggests potential net benefits to delayed vaccination in older adults in the United States. Vaccine 2018;36:5910–5. https://doi.org/10.1016/j.vaccine.2018.08.007 PMID:30146403
CDC. Immunization of health-care personnel: recommendations of the Advisory Committee on Immunization Practices (ACIP). MMWR Recomm Rep 2011;60(No. RR-7):1–45. PMID:22108587
Pearson ML, Bridges CB, Harper SA. Influenza vaccination of health-care personnel: recommendations of the Healthcare Infection Control Practices Advisory Committee (HICPAC) and the Advisory Committee on Immunization Practices (ACIP). MMWR Recomm Rep 2006;55(No. RR-2):1–16. PMID:16498385
Afluria [Package Insert]. Parkville, Victoria, Australia: Seqirus; 2024.
Fluarix [Package Insert]. Dresden, Germany: GlaxoSmithKline; 2024.
Flucelvax [Package Insert]. Holly Springs, NC: Seqirus; 2024.
FluLaval [Package Insert]. Quebec City, QC, Canada: ID Biomedical Corporation of Quebec; 2024.
Fluzone [Package Insert]. Swiftwater, PA: Sanofi Pasteur; 2024.
FluMist [Package Insert]. Gaithersburg, MD: MedImmune; 2024.
Flublok [Package Insert]. Meriden, CT: Protein Sciences; 2024.
Fluzone High-Dose [Package Insert]. Swiftwater, PA: Sanofi Pasteur; 2024.
Fluad [Package Insert]. Holly Springs, NC: Seqirus; 2024.
Allison MA, Daley MF, Crane LA, et al. Influenza vaccine effectiveness in healthy 6- to 21-month-old children during the 2003-2004 season. J Pediatr 2006;149:755–62. https://doi.org/10.1016/j.jpeds.2006.06.036 PMID:17137887
Eisenberg KW, Szilagyi PG, Fairbrother G, et al. Vaccine effectiveness against laboratory-confirmed influenza in children 6 to 59 months of age during the 2003-2004 and 2004-2005 influenza seasons. Pediatrics 2008;122:911–9 . https://doi.org/10.1542/peds.2007-3304 PMID:18977968
Neuzil KM, Jackson LA, Nelson J, et al. Immunogenicity and reactogenicity of 1 versus 2 doses of trivalent inactivated influenza vaccine in vaccine-naive 5-8-year-old children. J Infect Dis 2006;194:1032–9. https://doi.org/10.1086/507309 PMID:16991077
Ritzwoller DP, Bridges CB, Shetterly S, Yamasaki K, Kolczak M, France EK. Effectiveness of the 2003-2004 influenza vaccine among children 6 months to 8 years of age, with 1 vs 2 doses. Pediatrics 2005;116:153–9. https://doi.org/10.1542/peds.2005-0049 PMID:15995046
Eick AA, Uyeki TM, Klimov A, et al. Maternal influenza vaccination and effect on influenza virus infection in young infants. Arch Pediatr Adolesc Med 2011;165:104–11. https://doi.org/10.1001/archpediatrics.2010.192 PMID:20921345
American College of Obstetricians and Gynecologists. Influenza in pregnancy: prevention and treatment. Obstet Gynecol 2024;143:e24–30 https://www.acog.org/clinical/clinical-guidance/committee-statement/articles/2024/02/influenza-in-pregnancy-prevention-and-treatment . https://doi.org/10.1097/AOG.0000000000005479 PMID:38016152
Bratton KN, Wardle MT, Orenstein WA, Omer SB. Maternal influenza immunization and birth outcomes of stillbirth and spontaneous abortion: a systematic review and meta-analysis. Clin Infect Dis 2015;60:e11–9. https://doi.org/10.1093/cid/ciu915 PMID:25409473
Chambers CD, Johnson D, Xu R, et al. Risks and safety of pandemic H1N1 influenza vaccine in pregnancy: birth defects, spontaneous abortion, preterm delivery, and small for gestational age infants. Vaccine 2013;31:5026–32. https://doi.org/10.1016/j.vaccine.2013.08.097 PMID:24016809
Chambers CD, Johnson DL, Xu R, et al. Safety of the 2010-11, 2011-12, 2012-13, and 2013-14 seasonal influenza vaccines in pregnancy: Birth defects, spontaneous abortion, preterm delivery, and small for gestational age infants, a study from the cohort arm of VAMPSS. Vaccine 2016;34:4443–9. https://doi.org/10.1016/j.vaccine.2016.06.054 PMID:27449682
Heikkinen T, Young J, van Beek E, et al. Safety of MF59-adjuvanted A/H1N1 influenza vaccine in pregnancy: a comparative cohort study. Am J Obstet Gynecol 2012;207:177.e1–8. https://doi.org/10.1016/j.ajog.2012.07.007 PMID:22939717
Huang WT, Tang FW, Yang SE, Chih YC, Chuang JH. Safety of inactivated monovalent pandemic (H1N1) 2009 vaccination during pregnancy: a population-based study in Taiwan. Vaccine 2014;32:6463–8. https://doi.org/10.1016/j.vaccine.2014.09.054 PMID:25285884
Irving SA, Kieke BA, Donahue JG, et al. Trivalent inactivated influenza vaccine and spontaneous abortion. Obstet Gynecol 2013;121:159–65. https://doi.org/10.1097/AOG.0b013e318279f56f PMID:23262941
Ma F, Zhang L, Jiang R, et al. Prospective cohort study of the safety of an influenza A(H1N1) vaccine in pregnant Chinese women. Clin Vaccine Immunol 2014;21:1282–7. https://doi.org/10.1128/CVI.00375-14 PMID:24990911
McMillan M, Porritt K, Kralik D, Costi L, Marshall H. Influenza vaccination during pregnancy: a systematic review of fetal death, spontaneous abortion, and congenital malformation safety outcomes. Vaccine 2015;33:2108–17. https://doi.org/10.1016/j.vaccine.2015.02.068 PMID:25758932
Pasternak B, Svanström H, Mølgaard-Nielsen D, et al. Vaccination against pandemic A/H1N1 2009 influenza in pregnancy and risk of fetal death: cohort study in Denmark. BMJ 2012;344:e2794. https://doi.org/10.1136/bmj.e2794 PMID:22551713
Sammon CJ, Snowball J, McGrogan A, de Vries CS. Evaluating the hazard of foetal death following H1N1 influenza vaccination; a population based cohort study in the UK GPRD. PLoS One 2012;7:e51734. https://doi.org/10.1371/journal.pone.0051734 PMID:23341865
Oppermann M, Fritzsche J, Weber-Schoendorfer C, et al. A(H1N1)v2009: a controlled observational prospective cohort study on vaccine safety in pregnancy. Vaccine 2012;30:4445–52. https://doi.org/10.1016/j.vaccine.2012.04.081 PMID:22564554
Donahue JG, Kieke BA, King JP, et al. Association of spontaneous abortion with receipt of inactivated influenza vaccine containing H1N1pdm09 in 2010-11 and 2011-12. Vaccine 2017;35:5314–22. https://doi.org/10.1016/j.vaccine.2017.06.069 PMID:28917295
Donahue JG, Kieke BA, King JP, et al. Inactivated influenza vaccine and spontaneous abortion in the Vaccine Safety Datalink in 2012-13, 2013-14, and 2014-15. Vaccine 2019;37:6673–81 . https://doi.org/10.1016/j.vaccine.2019.09.035 PMID:31540812
Moro PL, Marquez P. Reports of cell-based influenza vaccine administered during pregnancy in the Vaccine Adverse Event Reporting System (VAERS), 2013-2020. Vaccine 2021;39:678–81. https://doi.org/10.1016/j.vaccine.2020.12.045 PMID:33358703
Robinson C, Van Boxmeer J, Tilson H, et al. Outcomes in pregnant persons immunized with a cell-based quadrivalent inactivated influenza vaccine: a prospective observational cohort study. Vaccines (Basel) 2022;10:1600 https://doi.org/10.3390/vaccines10101600. PMID:36298465
Swamy GK. Clinical Trial to compare safety of recombinant influenza vaccine (RIV4) versus quadrivalent inactivated influenza vaccine (IIV4) in pregnancy. Presented at the Advisory Committee on Immunization Practices meeting, Atlanta, GA; October 20, 2022. https://stacks.cdc.gov/view/cdc/122379
CDC. Advisory Committee on Immunization Practices: GRADE: higher dose and adjuvanted influenza vaccines for persons aged ≥65 Years. Atlanta, GA: US Department of Health and Human Services, CDC; 2022. https://www.cdc.gov/vaccines/acip/recs/grade/influenza-older-adults.html
CDC. Advisory Committee on Immunization Practices: evidence to recommendations (EtR) framework: higher dose and adjuvanted influenza vaccines for persons aged ≥65 years. Atlanta, GA: US Department of Health and Human Services, CDC; 2022. https://www.cdc.gov/vaccines/acip/recs/grade/influenza-older-adults-etr.html
CDC. Fluview. Weekly influenza surveillance report. Atlanta, GA: US Department of Health and Human Services, CDC; 2024. https://www.cdc.gov/flu/weekly/index.htm
Thompson WW, Shay DK, Weintraub E, et al. Mortality associated with influenza and respiratory syncytial virus in the United States. JAMA 2003;289:179–86 https://doi.org/10.1001/jama.289.2.179. PMID:12517228
DiazGranados CA. Dunning AJ, Kimmel M, et al. Efficacy of high-dose versus standard-dose influenza vaccine in older adults. N Engl J Med 2014;371:635–45. https://doi.org/10.1056/NEJMoa1315727 PMID:25119609
Dunkle LM, Izikson R, Patriarca P, et al. Efficacy of recombinant influenza vaccine in adults 50 years of age or older. N Engl J Med 2017;376:2427–36. https://doi.org/10.1056/NEJMoa1608862 PMID:28636855
Keitel WA, Treanor JJ, El Sahly HM, et al. Comparative immunogenicity of recombinant influenza hemagglutinin (rHA) and trivalent inactivated vaccine (TIV) among persons > or =65 years old. Vaccine 2009;28:379–85. https://doi.org/10.1016/j.vaccine.2009.10.037 PMID:19879222
Izurieta HS, Chillarige Y, Kelman J, et al. Relative effectiveness of cell-cultured and egg-based influenza vaccines among elderly persons in the United States, 2017–2018. J Infect Dis 2019;220:1255–64. https://doi.org/10.1093/infdis/jiy716 PMID:30561688
Izurieta HS, Chillarige Y, Kelman J, et al. Relative effectiveness of influenza vaccines among the United States elderly, 2018–2019. J Infect Dis 2020;222:278–87. https://doi.org/10.1093/infdis/jiaa080 PMID:32100009
Izurieta HS, Lu M, Kelman J, et al. Comparative effectiveness of influenza vaccines among US Medicare beneficiaries ages 65 years and older during the 2019–2020 season. Clin Infect Dis 2021;73:e4251–9. https://doi.org/10.1093/cid/ciaa1727 PMID:33211809
Izurieta HS, Thadani N, Shay DK, et al. Comparative effectiveness of high-dose versus standard-dose influenza vaccines in US residents aged 65 years and older from 2012 to 2013 using Medicare data: a retrospective cohort analysis. Lancet Infect Dis 2015;15:293–300. https://doi.org/10.1016/S1473-3099(14)71087-4 PMID:25672568
Lu Y, Chillarige Y, Izurieta HS, et al. Effect of age on relative effectiveness of high-dose versus standard-dose influenza vaccines among US Medicare beneficiaries aged >/=65 years. J Infect Dis 2019;220:1511–20. https://doi.org/10.1093/infdis/jiz360 PMID:31290553
Pelton SI, Divino V, Shah D, et al. Evaluating the relative vaccine effectiveness of adjuvanted trivalent influenza vaccine compared to high-dose trivalent and other egg-based influenza vaccines among older adults in the US during the 2017–2018 influenza season. Vaccines (Basel) 2020;8:1–17. https://doi.org/10.3390/vaccines8030446 PMID:32784684
Richardson DM, Medvedeva EL, Roberts CB, Linkin DR. Comparative effectiveness of high-dose versus standard-dose influenza vaccination in community-dwelling veterans. Clin Infect Dis 2015;61:171–6. https://doi.org/10.1093/cid/civ261 PMID:25829001
Shay DK, Chillarige Y, Kelman J, et al. Comparative effectiveness of high-dose versus standard-dose influenza vaccines among US Medicare beneficiaries in preventing postinfluenza deaths during 2012–2013 and 2013–2014. J Infect Dis 2017;215:510–7. https://doi.org/10.1093/infdis/jiw641 PMID:28329311
Young-Xu Y, Snider JT, van Aalst R, et al. Analysis of relative effectiveness of high-dose versus standard-dose influenza vaccines using an instrumental variable method. Vaccine 2019;37:1484–90. https://doi.org/10.1016/j.vaccine.2019.01.063 PMID:30745146
Young-Xu Y, Van Aalst R, Mahmud SM, et al. Relative vaccine effectiveness of high-dose versus standard-dose influenza vaccines among Veterans Health Administration patients. J Infect Dis 2018;217:1718–27 . https://doi.org/10.1093/infdis/jiy088 PMID:29452380
Young-Xu Y, Thornton Snider J, Mahmud SM, et al. High-dose influenza vaccination and mortality among predominantly male, white, senior veterans, United States, 2012/13 to 2014/15. Euro Surveill 2020;25:1900401 https://doi.org/10.2807/1560-7917.ES.2020.25.19.1900401. PMID:32431290
Pelton SI, Divino V, Postma MJ, et al. A retrospective cohort study assessing relative effectiveness of adjuvanted versus high-dose trivalent influenza vaccines among older adults in the United States during the 2018-19 influenza season. Vaccine 2021;39:2396–407. https://doi.org/10.1016/j.vaccine.2021.03.054 PMID:33810903
van Aalst R, Gravenstein S, Mor V, et al. Comparative effectiveness of high dose versus adjuvanted influenza vaccine: A retrospective cohort study. Vaccine 2020;38:372–9. https://doi.org/10.1016/j.vaccine.2019.09.105 PMID:31606249
Bosaeed M, Kumar D. Seasonal influenza vaccine in immunocompromised persons. Hum Vaccin Immunother 2018;14:1311–22. https://doi.org/10.1080/21645515.2018.1445446 PMID:29485353
Rubin LG, Levin MJ, Ljungman P, et al. 2013 IDSA clinical practice guideline for vaccination of the immunocompromised host. Clin Infect Dis 2014;58:e44–100. https://doi.org/10.1093/cid/cit684 PMID:24311479
CDC. Influenza antiviral medications: summary for clinicians. Atlanta, GA: US Department of Health and Human Services, CDC; 2023. https://www.cdc.gov/flu/professionals/antivirals/summary-clinicians.htm
Des Roches A, Paradis L, Gagnon R, et al. Egg-allergic patients can be safely vaccinated against influenza. J Allergy Clin Immunol 2012;130:1213–1216.e1. https://doi.org/10.1016/j.jaci.2012.07.046 PMID:23021881
CDC. Grading of Recommendations, Assessment, Development, and Evaluation (GRADE): safety of influenza vaccines for persons with egg allergy. Atlanta, GA: US Department of Health and Human Services, CDC; 2023. https://www.cdc.gov/vaccines/acip/recs/grade/influenza-egg-allergy.html
Woo EJ, Moro PL, Cano M, Jankosky C. Postmarketing safety surveillance of trivalent recombinant influenza vaccine: Reports to the Vaccine Adverse Event Reporting System. Vaccine 2017;35:5618–21. https://doi.org/10.1016/j.vaccine.2017.08.047 PMID:28886946
Woo EJ, Moro PL. Postmarketing safety surveillance of quadrivalent recombinant influenza vaccine: Reports to the vaccine adverse event reporting system. Vaccine 2021;39:1812–7. https://doi.org/10.1016/j.vaccine.2021.02.052 PMID:33678452
Woo EJ. Allergic reactions after egg-free recombinant influenza vaccine: reports to the US Vaccine Adverse Event Reporting System. Clin Infect Dis 2015;60:777–80. https://doi.org/10.1093/cid/ciu948 PMID:25428412
Kroger A, Bahta L, Long S, Sanchez P. General best practice guidelines for immunization. Atlanta, GA: US Department of Health and Human Services, CDC; 2023. https://www.cdc.gov/vaccines/hcp/acip-recs/general-recs/index.html
CDC. Flu prevention: information for travelers. Atlanta, GA: US Department of Health and Human Services, CDC; 2024. https://www.cdc.gov/flu/school-business/travelersfacts.htm
World Health Organization. Global influenza programme: FluNet. Geneva, Switzerland: World Health Organization; 2024. https://www.who.int/tools/flunet
Millman AJ, Kornylo Duong K, Lafond K, Green NM, Lippold SA, Jhung MA. Influenza outbreaks among passengers and crew on two cruise ships: a recent account of preparedness and response to an ever-present challenge. J Travel Med 2015;22:306–11 . https://doi.org/10.1111/jtm.12215 PMID:26031322
Mutsch M, Tavernini M, Marx A, et al. Influenza virus infection in travelers to tropical and subtropical countries. Clin Infect Dis 2005;40:1282–7. https://doi.org/10.1086/429243 PMID:15825030
Ratnam I, Black J, Leder K, et al. Incidence and risk factors for acute respiratory illnesses and influenza virus infections in Australian travellers to Asia. J Clin Virol 2013;57:54–8. https://doi.org/10.1016/j.jcv.2013.01.008 PMID:23380660
Uyeki TM, Zane SB, Bodnar UR, et al. Large summertime influenza A outbreak among tourists in Alaska and the Yukon Territory. Clin Infect Dis 2003;36:1095–102 . https://doi.org/10.1086/374053 PMID:12715302
Rapivab (peramivir injection) [Package Insert]. Durham, NC: BioCryst; 2024.
Xofluza (baloxavir marboxil) [Package Insert]. South San Francisco, CA: Genentech; 2022.
Food and Drug Administration. Guidance for industry: bioavailability and bioequivalence studies for orally administered drug products: general considerations. Silver Spring, MD: US Department of Health and Human Services, Food and Drug Administration; 2003. https://ipqpubs.com/wp-content/uploads/2020/12/BioStudies_OralDosageProducts_March.2003.GUIDANCE.pdf.pdf
Shingrix [Package Insert]. Durham, NC: GlaxoSmithKline; 2023.
Arexvy [Package insert]. Durham, NC: GlaxoSmithKline; 2024.
Heplisav-B [Package Insert]. Emeryville, CA: Dynavax; 2023.
Levin MJ, Buchwald UK, Gardner J, et al. Immunogenicity and safety of zoster vaccine live administered with quadrivalent influenza virus vaccine. Vaccine 2018;36:179–85. https://doi.org/10.1016/j.vaccine.2017.08.029 PMID:28830693
Schmader K. Safety of Simultaneous Vaccination with Zoster Vaccine Recombinant (RZV) and Quadrivalent Adjuvanted Inactivated Influenza Vaccine (allV4). Presented at the Advisory Committee on Immunization Practices meeting, Atlanta, GA; October 25, 2023. https://stacks.cdc.gov/view/cdc/134683
Food and Drug Administration. Vaccines and Related Biological Products Advisory Committee March 5, 2024 Meeting announcement. https://www.fda.gov/advisory-committees/advisory-committee-calendar/vaccines-and-related-biological-products-advisory-committee-march-5-2024-meeting-announcement


Trade name (manufacturer)	Presentations	Age indication	g HA (IIV3s and RIV3) or virus count (LAIV3) for each vaccine virus (per dose)	Route	Mercury (from thimerosal, if present), g/0.5 mL
)
Afluria (Seqirus)	0.5-mL PFS	≥3 yrs	15 g/0.5 mL	IM	—**
Afluria (Seqirus)	5.0-mL MDV	≥6 mos (needle and syringe) 18 through 64 yrs (jet injector)	7.5 g/0.25 mL 15 g/0.5 mL	IM	24.5
Fluarix (GlaxoSmithKline)	0.5-mL PFS	≥6 mos	15 g/0.5 mL	IM	—
FluLaval (GlaxoSmithKline)	0.5-mL PFS	≥6 mos	15 g/0.5 mL	IM	—
Fluzone (Sanofi Pasteur)	0.5-mL PFS	≥6 mos	15 g/0.5 mL	IM	—
Fluzone (Sanofi Pasteur)	5.0-mL MDV	≥6 mos	7.5 g/0.25 mL 15 g/0.5 mL	IM	25

Flucelvax (Seqirus)	0.5-mL PFS	≥6 mos	15 g/0.5 mL	IM	—
Flucelvax (Seqirus)	5.0-mL MDV	≥6 mos	15 g/0.5 mL	IM	25
)
Fluzone High-Dose (Sanofi Pasteur)	0.5-mL PFS	≥65 yrs	60 g/0.5 mL	IM	—
with MF59 adjuvant)
Fluad (Seqirus)	0.5-mL PFS	≥65 yrs	15 g/0.5 mL	IM	—

Flublok (Sanofi Pasteur)	0.5-mL PFS	≥18 yrs	45 g/0.5 mL	IM	—
)
FluMist (AstraZeneca)	0.2-mL prefilled single-use intranasal sprayer	2 through 49 yrs	10 fluorescent focus units/0.2 mL	NAS	—

Abbreviations: ACIP = Advisory Committee on Immunization Practices; aIIV3 = adjuvanted inactivated influenza vaccine, trivalent; ccIIV3 = cell culture-based inactivated influenza vaccine, trivalent; HA = hemagglutinin; HD-IIV3 = high-dose inactivated influenza vaccine, trivalent; IIV3 = inactivated influenza vaccine, trivalent; IM = intramuscular; LAIV3 = live attenuated influenza vaccine, trivalent; MDV = multidose vial; NAS = intranasal; PFS = prefilled syringe; RIV3 = recombinant influenza vaccine, trivalent. * Manufacturer package inserts and updated CDC and ACIP guidance should be consulted for additional information concerning, but not limited to, indications, contraindications, warnings, and precautions. Package inserts for U.S.-licensed vaccines are available at https://www.fda.gov/vaccines-blood-biologics/vaccines/vaccines-licensed-use-united-states . Availability and characteristics of specific products and presentations might change or differ from what is described in this table and in the text of this report. † Although a history of severe allergic reaction (e.g., anaphylaxis) to egg is a labeled contraindication to the use of egg-based IIV3s and LAIV3, ACIP recommends that all persons aged ≥6 months with egg allergy should receive influenza vaccine and that any influenza vaccine (egg based or nonegg based) that is otherwise appropriate for the recipient’s age and health status can be used (see Persons with a History of Egg Allergy). § The approved dose volume for Afluria is 0.25 mL for children aged 6 through 35 months and 0.5 mL for persons aged ≥3 years. However, 0.25-mL prefilled syringes are no longer available. For children aged 6 through 35 months, a 0.25-mL dose must be obtained from a multidose vial. ¶ IM-administered influenza vaccines should be administered by needle and syringe only, except for the MDV presentation of Afluria, which can alternatively be given by the PharmaJet Stratis jet injector for persons aged 18 through 64 years only. For older children and adults, the recommended site for IM influenza vaccination is the deltoid muscle. The preferred site for infants and young children is the anterolateral aspect of the thigh. Additional specific guidance regarding site selection and needle length for IM administration is available in the General Best Practice Guidelines for Immunization available at https://www.cdc.gov/vaccines/hcp/acip-recs/general-recs/index.html . ** Not applicable. †† Fluzone is approved for children aged 6 through 35 months at either 0.25 mL or 0.5 mL per dose; however, 0.25-mL prefilled syringes are no longer available. If a prefilled syringe of Fluzone is used for a child in this age group, the dose volume will be 0.5 mL per dose.


Vaccine type	Contraindications	Precautions
Egg-based IIV3s	or to a previous dose of any influenza vaccine (i.e., any egg-based IIV, ccIIV, RIV, or LAIV)
ccIIV3
RIV3
LAIV3	or to a previous dose of any influenza vaccine (i.e., any egg-based IIV, ccIIV, RIV, or LAIV)

Abbreviations: ACIP = Advisory Committee on Immunization Practices; ccIIV = cell culture–based inactivated influenza vaccine (any valency); ccIIV3 = cell culture–based inactivated influenza vaccine, trivalent; CSF = cerebrospinal fluid; IIV = inactivated influenza vaccine (any valency); IIV3 = inactivated influenza vaccine, trivalent; LAIV = live attenuated influenza vaccine (any valency); LAIV3 = live attenuated influenza vaccine, trivalent; RIV = recombinant influenza vaccine (any valency); RIV3 = recombinant influenza vaccine, trivalent. * Manufacturer package inserts and updated CDC and ACIP guidance should be consulted for additional information concerning, but not limited to, indications, contraindications, warnings, and precautions. When a contraindication is present, a vaccine should not be administered. When a precaution is present, vaccination should generally be deferred but might be indicated if the benefit of protection from the vaccine outweighs the risk for an adverse reaction (see the General Best Practice Guidelines for Immunization, available at https://www.cdc.gov/vaccines/hcp/acip-recs/general-recs/index.html ). Package inserts for U.S.-licensed vaccines are available at https://www.fda.gov/vaccines-blood-biologics/vaccines/vaccines-licensed-use-united-states . † Although a history of severe allergic reaction (e.g., anaphylaxis) to egg is a labeled contraindication to the use of egg-based IIV3s and LAIV3, ACIP recommends that all persons aged ≥6 months with egg allergy should receive influenza vaccine, and that any influenza vaccine (egg based or nonegg based) that is otherwise appropriate for the recipient’s age and health status can be used (see Persons with a History of Egg Allergy). § Labeled contraindication noted in package insert. ¶ If administered, vaccination should occur in a medical setting and should be supervised by a health care provider who can recognize and manage severe allergic reactions. Providers can consider consultation with an allergist in such cases to assist in identification of the component responsible for the allergic reaction. ** Injectable vaccines are recommended for persons with cochlear implant because of the potential for CSF leak, which might exist for a period after implantation. Providers might consider consultation with a specialist concerning risk for persistent CSF leak if an inactivated or recombinant vaccine cannot be used. †† Use of LAIV3 in context of influenza antivirals has not been studied; however, interference with activity of LAIV3 is biologically plausible, and this possibility is noted in the package insert for LAIV3. In the absence of data supporting an adequate minimum interval between influenza antiviral use and LAIV3 administration, the intervals provided are based on the half-life of each antiviral. The interval between influenza antiviral receipt and LAIV3 for which interference might potentially occur might be further prolonged in the presence of medical conditions that delay medication clearance (e.g., renal insufficiency). Influenza antivirals might also interfere with LAIV3 if initiated within 2 weeks after vaccination. Persons who receive antivirals during the period starting with the specified time before receipt of LAIV3 through 2 weeks after receipt of LAIV3 should be revaccinated with an age-appropriate IIV3 or RIV3.


Vaccine (of any valency) associated with previous severe allergic reaction (e.g., anaphylaxis)	Available 2024–25 influenza vaccines
	Egg based IIV3s and LAIV3	ccIIV3	RIV3
Any egg based IIV or LAIV	Contraindication	Precaution	Precaution
Any ccIIV	Contraindication	Contraindication	Precaution
Any RIV	Contraindication	Precaution	Contraindication
Unknown influenza vaccine	Allergist consultation recommended

Abbreviations: ACIP = Advisory Committee on Immunization Practices; ccIIV = cell culture–based inactivated influenza vaccine (any valency); ccIIV3 = cell culture–based inactivated influenza vaccine, trivalent; IIV = inactivated influenza vaccine (any valency); IIV3 = inactivated influenza vaccine, trivalent; LAIV = live attenuated influenza vaccine (any valency); LAIV3 = live attenuated influenza vaccine, trivalent; RIV = recombinant influenza vaccine (any valency); RIV3 = recombinant influenza vaccine, trivalent. * Manufacturer package inserts and updated CDC and ACIP guidance should be consulted for additional information, including, but not limited to indications, contraindications, warnings, and precautions. Package inserts for U.S.-licensed vaccines are available at https://www.fda.gov/vaccines-blood-biologics/vaccines/vaccines-licensed-use-united-states . † When a contraindication is present, a vaccine should not be administered, consistent with the General Best Practice Guidelines for Immunization (Source: Kroger A, Bahta L, Long S, Sanchez P. General best practice guidelines for immunization; https://www.cdc.gov/vaccines/hcp/acip-recs/general-recs/index.html ). In addition to the contraindications based on history of severe allergic reaction to influenza vaccines that are noted in the table, each individual influenza vaccine is contraindicated for persons who have had a severe allergic reaction (e.g., anaphylaxis) to any component of that vaccine. Vaccine components can be found in package inserts. Although a history of severe allergic reaction (e.g., anaphylaxis) to egg is a labeled contraindication to the use of egg-based IIV3s and LAIV3, ACIP recommends that all persons aged ≥6 months with egg allergy should receive influenza vaccine, and that any influenza vaccine (egg based or nonegg based) that is otherwise appropriate for the recipient’s age and health status can be used (see Persons with a History of Egg Allergy). § When a precaution is present, vaccination should generally be deferred but might be indicated if the benefit of protection from the vaccine outweighs the risk for an adverse reaction, consistent with the General Best Practice Guidelines for Immunization (Source: Kroger A, Bahta L, Long S, Sanchez P. General best practice guidelines for immunization; https://www.cdc.gov/vaccines/hcp/acip-recs/general-recs/index.html ). Providers can consider using the following vaccines in these instances; however, vaccination should occur in an inpatient or outpatient medical setting with supervision by a health care provider who is able to recognize and manage severe allergic reactions: 1) for persons with a history of severe allergic reaction (e.g., anaphylaxis) to any egg-based IIV or LAIV of any valency, the provider can consider administering ccIIV3 or RIV3; 2) for persons with a history of severe allergic reaction (e.g., anaphylaxis) to any ccIIV of any valency, the provider can consider administering RIV3; and 3) for persons with a history of severe allergic reaction (e.g., anaphylaxis) to any RIV of any valency, the provider can consider administering ccIIV3. Providers can also consider consulting with an allergist to help determine which vaccine component is responsible for the allergic reaction.

BOX. Abbreviation conventions for influenza vaccines discussed in this report.

IIV = inactivated influenza vaccine
RIV = recombinant influenza vaccine
LAIV = live attenuated influenza vaccine
3 for trivalent vaccines: one A(H1N1), one A(H3N2), and one B virus (from one lineage)
4 for quadrivalent vaccines: one A(H1N1), one A(H3N2), and two B viruses (one from each lineage)
All influenza vaccines expected to be available in the United States for the 2024–25 season are trivalent vaccines. However, abbreviations for quadrivalent vaccines (e.g., IIV4) might be used in this report when discussing information specific to quadrivalent vaccines
Abbreviations for general vaccine categories (e.g., IIV) might be used when discussing information that is not specific to valency or to a specific vaccine in that category.
a for MF59-adjuvanted inactivated influenza vaccine (e.g., aIIV3)
cc for cell culture–based inactivated influenza vaccine (e.g., ccIIV3)
HD for high-dose inactivated influenza vaccine (e.g., HD-IIV3)
SD for standard-dose inactivated influenza vaccine (e.g., SD-IIV3)

FIGURE. Influenza vaccine dosing algorithm for children aged 6 months through 8 years* — Advisory Committee on Immunization Practices, United States, 2024–25 influenza season.

* Children aged 6 months through 8 years who require 2 doses of influenza vaccine should receive their first dose as soon as possible (including during July and August, if vaccine is available) to allow the second dose (which must be administered ≥4 weeks later) to be received, ideally, by the end of October. For children aged 8 years who require 2 doses of vaccine, both doses should be administered even if the child turns age 9 years between receipt of dose 1 and dose 2.


Trade name (Manufacturer)	Dose volume for children aged 6 through 35 mos ( g HA per vaccine virus)
Afluria (Seqirus)	0.25 mL (7.5 g)
Fluarix (GlaxoSmithKline)	0.5 mL (15 g)
Flucelvax (Seqirus)	0.5 mL (15 g)
FluLaval (GlaxoSmithKline)	0.5 mL (15 g)
Fluzone (Sanofi Pasteur)	0.5 mL (15 g)

Abbreviation: HA = hemagglutinin. * For persons aged ≥36 months (≥3 years), the dose volume is 0.5 mL per dose for all inactivated influenza vaccines. † The approved dose volume for Afluria is 0.25 mL for children aged 6 through 35 months and 0.5 mL for persons aged ≥3 years. However, 0.25-mL prefilled syringes are no longer available. For children aged 6 through 35 months, a 0.25-mL dose must be obtained from a multidose vial. § Per the package insert, Fluzone is approved for children aged 6 through 35 months at either 0.25 mL or 0.5 mL per dose; however, 0.25-mL prefilled syringes are no longer available. If a prefilled syringe of Fluzone is used for a child in this age group, the dose volume will be 0.5 mL per dose. The 5.0 mL multidose vials should be accessed for only 10 doses, regardless of the volume of the doses obtained or any remaining volume in the vial. Any vaccine remaining in a vial after the maximum number of doses has been removed should be discarded.

Suggested citation for this article: Grohskopf LA, Ferdinands JM, Blanton LH, Broder KR, Loehr J. Prevention and Control of Seasonal Influenza with Vaccines: Recommendations of the Advisory Committee on Immunization Practices — United States, 2024–25 Influenza Season. MMWR Recomm Rep 2024;73(No. RR-5):1–25. DOI: http://dx.doi.org/10.15585/mmwr.rr7305a1 .

MMWR and Morbidity and Mortality Weekly Report are service marks of the U.S. Department of Health and Human Services. Use of trade names and commercial sources is for identification only and does not imply endorsement by the U.S. Department of Health and Human Services. References to non-CDC sites on the Internet are provided as a service to MMWR readers and do not constitute or imply endorsement of these organizations or their programs by CDC or the U.S. Department of Health and Human Services. CDC is not responsible for the content of pages found at these sites. URL addresses listed in MMWR were current as of the date of publication.

All HTML versions of MMWR articles are generated from final proofs through an automated process. This conversion might result in character translation or format errors in the HTML version. Users are referred to the electronic PDF version ( https://www.cdc.gov/mmwr ) and/or the original MMWR paper copy for printable versions of official text, figures, and tables.

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Digital gender gaps in Students’ knowledge, attitudes and skills: an integrative data analysis across 32 Countries

Online learning problems, academic worries, social interaction, and psychological well-being among secondary school students in Hong Kong during the COVID-19 pandemic: the socioeconomic and gender differences

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ORIGINAL RESEARCH article

1. Introduction

2. Literature review

3. Materials and methods

5. Conclusion

Data availability statement

Author contributions

Acknowledgments

Conflict of interest

Publisher’s note

COVID-19’s impacts on the scope, effectiveness, and interaction characteristics of online learning: A social network analysis

1. Introduction

2. Literature review and research questions

3. Methodology

3.2 Data collection and preprocessing

3.3 Instrumentation

3.4 Ethics statement

4.1 COVID-19’s role in promoting the scale of online courses was not as important as expected

4.2 Characteristics of online learning interaction amid COVID-19

4.2.2 After the COVID-19 pandemic, the scale decreased rapidly, but most course interaction was more effective.

4.3 Analyses of natural science courses and social science courses

4.3.2 Analysis of the closeness centrality of Economics and Electrodynamics.

5. Discussion

5.1 During the COVID-19 pandemic, the scale of interaction increased in only a few courses

5.2 Characteristics of online learning interaction during and after the COVID-19 pandemic

5.2.2 Online learning interaction declined after the COVID-19 pandemic.

5.3 What are the characteristics of interaction in social science courses and natural science courses?

6. Conclusion and future work

Academic performance under COVID-19: The role of online learning readiness and emotional competence

Wenjing Guo

Associated Data

COVID-19 and online learning readiness

Online learning readiness and academic performance

COVID-19 and emotional competence

Emotional competence and academic performance

The current study

Participants and procedure

College student sample

Measurement