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Students’ online learning challenges during the pandemic and how they cope with them: The case of the Philippines

• Published: 28 May 2021
• Volume 26 , pages 7321–7338, ( 2021 )

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• Jessie S. Barrot   ORCID: orcid.org/0000-0001-8517-4058 1 ,
• Ian I. Llenares 1 &
• Leo S. del Rosario 1  

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Recently, the education system has faced an unprecedented health crisis that has shaken up its foundation. Given today’s uncertainties, it is vital to gain a nuanced understanding of students’ online learning experience in times of the COVID-19 pandemic. Although many studies have investigated this area, limited information is available regarding the challenges and the specific strategies that students employ to overcome them. Thus, this study attempts to fill in the void. Using a mixed-methods approach, the findings revealed that the online learning challenges of college students varied in terms of type and extent. Their greatest challenge was linked to their learning environment at home, while their least challenge was technological literacy and competency. The findings further revealed that the COVID-19 pandemic had the greatest impact on the quality of the learning experience and students’ mental health. In terms of strategies employed by students, the most frequently used were resource management and utilization, help-seeking, technical aptitude enhancement, time management, and learning environment control. Implications for classroom practice, policy-making, and future research are discussed.

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1 Introduction

Since the 1990s, the world has seen significant changes in the landscape of education as a result of the ever-expanding influence of technology. One such development is the adoption of online learning across different learning contexts, whether formal or informal, academic and non-academic, and residential or remotely. We began to witness schools, teachers, and students increasingly adopt e-learning technologies that allow teachers to deliver instruction interactively, share resources seamlessly, and facilitate student collaboration and interaction (Elaish et al., 2019 ; Garcia et al., 2018 ). Although the efficacy of online learning has long been acknowledged by the education community (Barrot, 2020 , 2021 ; Cavanaugh et al., 2009 ; Kebritchi et al., 2017 ; Tallent-Runnels et al., 2006 ; Wallace, 2003 ), evidence on the challenges in its implementation continues to build up (e.g., Boelens et al., 2017 ; Rasheed et al., 2020 ).

Recently, the education system has faced an unprecedented health crisis (i.e., COVID-19 pandemic) that has shaken up its foundation. Thus, various governments across the globe have launched a crisis response to mitigate the adverse impact of the pandemic on education. This response includes, but is not limited to, curriculum revisions, provision for technological resources and infrastructure, shifts in the academic calendar, and policies on instructional delivery and assessment. Inevitably, these developments compelled educational institutions to migrate to full online learning until face-to-face instruction is allowed. The current circumstance is unique as it could aggravate the challenges experienced during online learning due to restrictions in movement and health protocols (Gonzales et al., 2020 ; Kapasia et al., 2020 ). Given today’s uncertainties, it is vital to gain a nuanced understanding of students’ online learning experience in times of the COVID-19 pandemic. To date, many studies have investigated this area with a focus on students’ mental health (Copeland et al., 2021 ; Fawaz et al., 2021 ), home learning (Suryaman et al., 2020 ), self-regulation (Carter et al., 2020 ), virtual learning environment (Almaiah et al., 2020 ; Hew et al., 2020 ; Tang et al., 2020 ), and students’ overall learning experience (e.g., Adarkwah, 2021 ; Day et al., 2021 ; Khalil et al., 2020 ; Singh et al., 2020 ). There are two key differences that set the current study apart from the previous studies. First, it sheds light on the direct impact of the pandemic on the challenges that students experience in an online learning space. Second, the current study explores students’ coping strategies in this new learning setup. Addressing these areas would shed light on the extent of challenges that students experience in a full online learning space, particularly within the context of the pandemic. Meanwhile, our nuanced understanding of the strategies that students use to overcome their challenges would provide relevant information to school administrators and teachers to better support the online learning needs of students. This information would also be critical in revisiting the typology of strategies in an online learning environment.

2 Literature review

2.1 education and the covid-19 pandemic.

In December 2019, an outbreak of a novel coronavirus, known as COVID-19, occurred in China and has spread rapidly across the globe within a few months. COVID-19 is an infectious disease caused by a new strain of coronavirus that attacks the respiratory system (World Health Organization, 2020 ). As of January 2021, COVID-19 has infected 94 million people and has caused 2 million deaths in 191 countries and territories (John Hopkins University, 2021 ). This pandemic has created a massive disruption of the educational systems, affecting over 1.5 billion students. It has forced the government to cancel national examinations and the schools to temporarily close, cease face-to-face instruction, and strictly observe physical distancing. These events have sparked the digital transformation of higher education and challenged its ability to respond promptly and effectively. Schools adopted relevant technologies, prepared learning and staff resources, set systems and infrastructure, established new teaching protocols, and adjusted their curricula. However, the transition was smooth for some schools but rough for others, particularly those from developing countries with limited infrastructure (Pham & Nguyen, 2020 ; Simbulan, 2020 ).

Inevitably, schools and other learning spaces were forced to migrate to full online learning as the world continues the battle to control the vicious spread of the virus. Online learning refers to a learning environment that uses the Internet and other technological devices and tools for synchronous and asynchronous instructional delivery and management of academic programs (Usher & Barak, 2020 ; Huang, 2019 ). Synchronous online learning involves real-time interactions between the teacher and the students, while asynchronous online learning occurs without a strict schedule for different students (Singh & Thurman, 2019 ). Within the context of the COVID-19 pandemic, online learning has taken the status of interim remote teaching that serves as a response to an exigency. However, the migration to a new learning space has faced several major concerns relating to policy, pedagogy, logistics, socioeconomic factors, technology, and psychosocial factors (Donitsa-Schmidt & Ramot, 2020 ; Khalil et al., 2020 ; Varea & González-Calvo, 2020 ). With reference to policies, government education agencies and schools scrambled to create fool-proof policies on governance structure, teacher management, and student management. Teachers, who were used to conventional teaching delivery, were also obliged to embrace technology despite their lack of technological literacy. To address this problem, online learning webinars and peer support systems were launched. On the part of the students, dropout rates increased due to economic, psychological, and academic reasons. Academically, although it is virtually possible for students to learn anything online, learning may perhaps be less than optimal, especially in courses that require face-to-face contact and direct interactions (Franchi, 2020 ).

2.2 Related studies

Recently, there has been an explosion of studies relating to the new normal in education. While many focused on national policies, professional development, and curriculum, others zeroed in on the specific learning experience of students during the pandemic. Among these are Copeland et al. ( 2021 ) and Fawaz et al. ( 2021 ) who examined the impact of COVID-19 on college students’ mental health and their coping mechanisms. Copeland et al. ( 2021 ) reported that the pandemic adversely affected students’ behavioral and emotional functioning, particularly attention and externalizing problems (i.e., mood and wellness behavior), which were caused by isolation, economic/health effects, and uncertainties. In Fawaz et al.’s ( 2021 ) study, students raised their concerns on learning and evaluation methods, overwhelming task load, technical difficulties, and confinement. To cope with these problems, students actively dealt with the situation by seeking help from their teachers and relatives and engaging in recreational activities. These active-oriented coping mechanisms of students were aligned with Carter et al.’s ( 2020 ), who explored students’ self-regulation strategies.

In another study, Tang et al. ( 2020 ) examined the efficacy of different online teaching modes among engineering students. Using a questionnaire, the results revealed that students were dissatisfied with online learning in general, particularly in the aspect of communication and question-and-answer modes. Nonetheless, the combined model of online teaching with flipped classrooms improved students’ attention, academic performance, and course evaluation. A parallel study was undertaken by Hew et al. ( 2020 ), who transformed conventional flipped classrooms into fully online flipped classes through a cloud-based video conferencing app. Their findings suggested that these two types of learning environments were equally effective. They also offered ways on how to effectively adopt videoconferencing-assisted online flipped classrooms. Unlike the two studies, Suryaman et al. ( 2020 ) looked into how learning occurred at home during the pandemic. Their findings showed that students faced many obstacles in a home learning environment, such as lack of mastery of technology, high Internet cost, and limited interaction/socialization between and among students. In a related study, Kapasia et al. ( 2020 ) investigated how lockdown impacts students’ learning performance. Their findings revealed that the lockdown made significant disruptions in students’ learning experience. The students also reported some challenges that they faced during their online classes. These include anxiety, depression, poor Internet service, and unfavorable home learning environment, which were aggravated when students are marginalized and from remote areas. Contrary to Kapasia et al.’s ( 2020 ) findings, Gonzales et al. ( 2020 ) found that confinement of students during the pandemic had significant positive effects on their performance. They attributed these results to students’ continuous use of learning strategies which, in turn, improved their learning efficiency.

Finally, there are those that focused on students’ overall online learning experience during the COVID-19 pandemic. One such study was that of Singh et al. ( 2020 ), who examined students’ experience during the COVID-19 pandemic using a quantitative descriptive approach. Their findings indicated that students appreciated the use of online learning during the pandemic. However, half of them believed that the traditional classroom setting was more effective than the online learning platform. Methodologically, the researchers acknowledge that the quantitative nature of their study restricts a deeper interpretation of the findings. Unlike the above study, Khalil et al. ( 2020 ) qualitatively explored the efficacy of synchronized online learning in a medical school in Saudi Arabia. The results indicated that students generally perceive synchronous online learning positively, particularly in terms of time management and efficacy. However, they also reported technical (internet connectivity and poor utility of tools), methodological (content delivery), and behavioral (individual personality) challenges. Their findings also highlighted the failure of the online learning environment to address the needs of courses that require hands-on practice despite efforts to adopt virtual laboratories. In a parallel study, Adarkwah ( 2021 ) examined students’ online learning experience during the pandemic using a narrative inquiry approach. The findings indicated that Ghanaian students considered online learning as ineffective due to several challenges that they encountered. Among these were lack of social interaction among students, poor communication, lack of ICT resources, and poor learning outcomes. More recently, Day et al. ( 2021 ) examined the immediate impact of COVID-19 on students’ learning experience. Evidence from six institutions across three countries revealed some positive experiences and pre-existing inequities. Among the reported challenges are lack of appropriate devices, poor learning space at home, stress among students, and lack of fieldwork and access to laboratories.

Although there are few studies that report the online learning challenges that higher education students experience during the pandemic, limited information is available regarding the specific strategies that they use to overcome them. It is in this context that the current study was undertaken. This mixed-methods study investigates students’ online learning experience in higher education. Specifically, the following research questions are addressed: (1) What is the extent of challenges that students experience in an online learning environment? (2) How did the COVID-19 pandemic impact the online learning challenges that students experience? (3) What strategies did students use to overcome the challenges?

2.3 Conceptual framework

The typology of challenges examined in this study is largely based on Rasheed et al.’s ( 2020 ) review of students’ experience in an online learning environment. These challenges are grouped into five general clusters, namely self-regulation (SRC), technological literacy and competency (TLCC), student isolation (SIC), technological sufficiency (TSC), and technological complexity (TCC) challenges (Rasheed et al., 2020 , p. 5). SRC refers to a set of behavior by which students exercise control over their emotions, actions, and thoughts to achieve learning objectives. TLCC relates to a set of challenges about students’ ability to effectively use technology for learning purposes. SIC relates to the emotional discomfort that students experience as a result of being lonely and secluded from their peers. TSC refers to a set of challenges that students experience when accessing available online technologies for learning. Finally, there is TCC which involves challenges that students experience when exposed to complex and over-sufficient technologies for online learning.

To extend Rasheed et al. ( 2020 ) categories and to cover other potential challenges during online classes, two more clusters were added, namely learning resource challenges (LRC) and learning environment challenges (LEC) (Buehler, 2004 ; Recker et al., 2004 ; Seplaki et al., 2014 ; Xue et al., 2020 ). LRC refers to a set of challenges that students face relating to their use of library resources and instructional materials, whereas LEC is a set of challenges that students experience related to the condition of their learning space that shapes their learning experiences, beliefs, and attitudes. Since learning environment at home and learning resources available to students has been reported to significantly impact the quality of learning and their achievement of learning outcomes (Drane et al., 2020 ; Suryaman et al., 2020 ), the inclusion of LRC and LEC would allow us to capture other important challenges that students experience during the pandemic, particularly those from developing regions. This comprehensive list would provide us a clearer and detailed picture of students’ experiences when engaged in online learning in an emergency. Given the restrictions in mobility at macro and micro levels during the pandemic, it is also expected that such conditions would aggravate these challenges. Therefore, this paper intends to understand these challenges from students’ perspectives since they are the ones that are ultimately impacted when the issue is about the learning experience. We also seek to explore areas that provide inconclusive findings, thereby setting the path for future research.

3 Material and methods

The present study adopted a descriptive, mixed-methods approach to address the research questions. This approach allowed the researchers to collect complex data about students’ experience in an online learning environment and to clearly understand the phenomena from their perspective.

3.1 Participants

This study involved 200 (66 male and 134 female) students from a private higher education institution in the Philippines. These participants were Psychology, Physical Education, and Sports Management majors whose ages ranged from 17 to 25 ( x̅  = 19.81; SD  = 1.80). The students have been engaged in online learning for at least two terms in both synchronous and asynchronous modes. The students belonged to low- and middle-income groups but were equipped with the basic online learning equipment (e.g., computer, headset, speakers) and computer skills necessary for their participation in online classes. Table 1 shows the primary and secondary platforms that students used during their online classes. The primary platforms are those that are formally adopted by teachers and students in a structured academic context, whereas the secondary platforms are those that are informally and spontaneously used by students and teachers for informal learning and to supplement instructional delivery. Note that almost all students identified MS Teams as their primary platform because it is the official learning management system of the university.

Informed consent was sought from the participants prior to their involvement. Before students signed the informed consent form, they were oriented about the objectives of the study and the extent of their involvement. They were also briefed about the confidentiality of information, their anonymity, and their right to refuse to participate in the investigation. Finally, the participants were informed that they would incur no additional cost from their participation.

3.2 Instrument and data collection

The data were collected using a retrospective self-report questionnaire and a focused group discussion (FGD). A self-report questionnaire was considered appropriate because the indicators relate to affective responses and attitude (Araujo et al., 2017 ; Barrot, 2016 ; Spector, 1994 ). Although the participants may tell more than what they know or do in a self-report survey (Matsumoto, 1994 ), this challenge was addressed by explaining to them in detail each of the indicators and using methodological triangulation through FGD. The questionnaire was divided into four sections: (1) participant’s personal information section, (2) the background information on the online learning environment, (3) the rating scale section for the online learning challenges, (4) the open-ended section. The personal information section asked about the students’ personal information (name, school, course, age, and sex), while the background information section explored the online learning mode and platforms (primary and secondary) used in class, and students’ length of engagement in online classes. The rating scale section contained 37 items that relate to SRC (6 items), TLCC (10 items), SIC (4 items), TSC (6 items), TCC (3 items), LRC (4 items), and LEC (4 items). The Likert scale uses six scores (i.e., 5– to a very great extent , 4– to a great extent , 3– to a moderate extent , 2– to some extent , 1– to a small extent , and 0 –not at all/negligible ) assigned to each of the 37 items. Finally, the open-ended questions asked about other challenges that students experienced, the impact of the pandemic on the intensity or extent of the challenges they experienced, and the strategies that the participants employed to overcome the eight different types of challenges during online learning. Two experienced educators and researchers reviewed the questionnaire for clarity, accuracy, and content and face validity. The piloting of the instrument revealed that the tool had good internal consistency (Cronbach’s α = 0.96).

The FGD protocol contains two major sections: the participants’ background information and the main questions. The background information section asked about the students’ names, age, courses being taken, online learning mode used in class. The items in the main questions section covered questions relating to the students’ overall attitude toward online learning during the pandemic, the reasons for the scores they assigned to each of the challenges they experienced, the impact of the pandemic on students’ challenges, and the strategies they employed to address the challenges. The same experts identified above validated the FGD protocol.

Both the questionnaire and the FGD were conducted online via Google survey and MS Teams, respectively. It took approximately 20 min to complete the questionnaire, while the FGD lasted for about 90 min. Students were allowed to ask for clarification and additional explanations relating to the questionnaire content, FGD, and procedure. Online surveys and interview were used because of the ongoing lockdown in the city. For the purpose of triangulation, 20 (10 from Psychology and 10 from Physical Education and Sports Management) randomly selected students were invited to participate in the FGD. Two separate FGDs were scheduled for each group and were facilitated by researcher 2 and researcher 3, respectively. The interviewers ensured that the participants were comfortable and open to talk freely during the FGD to avoid social desirability biases (Bergen & Labonté, 2020 ). These were done by informing the participants that there are no wrong responses and that their identity and responses would be handled with the utmost confidentiality. With the permission of the participants, the FGD was recorded to ensure that all relevant information was accurately captured for transcription and analysis.

3.3 Data analysis

To address the research questions, we used both quantitative and qualitative analyses. For the quantitative analysis, we entered all the data into an excel spreadsheet. Then, we computed the mean scores ( M ) and standard deviations ( SD ) to determine the level of challenges experienced by students during online learning. The mean score for each descriptor was interpreted using the following scheme: 4.18 to 5.00 ( to a very great extent ), 3.34 to 4.17 ( to a great extent ), 2.51 to 3.33 ( to a moderate extent ), 1.68 to 2.50 ( to some extent ), 0.84 to 1.67 ( to a small extent ), and 0 to 0.83 ( not at all/negligible ). The equal interval was adopted because it produces more reliable and valid information than other types of scales (Cicchetti et al., 2006 ).

For the qualitative data, we analyzed the students’ responses in the open-ended questions and the transcribed FGD using the predetermined categories in the conceptual framework. Specifically, we used multilevel coding in classifying the codes from the transcripts (Birks & Mills, 2011 ). To do this, we identified the relevant codes from the responses of the participants and categorized these codes based on the similarities or relatedness of their properties and dimensions. Then, we performed a constant comparative and progressive analysis of cases to allow the initially identified subcategories to emerge and take shape. To ensure the reliability of the analysis, two coders independently analyzed the qualitative data. Both coders familiarize themselves with the purpose, research questions, research method, and codes and coding scheme of the study. They also had a calibration session and discussed ways on how they could consistently analyze the qualitative data. Percent of agreement between the two coders was 86 percent. Any disagreements in the analysis were discussed by the coders until an agreement was achieved.

This study investigated students’ online learning experience in higher education within the context of the pandemic. Specifically, we identified the extent of challenges that students experienced, how the COVID-19 pandemic impacted their online learning experience, and the strategies that they used to confront these challenges.

4.1 The extent of students’ online learning challenges

Table 2 presents the mean scores and SD for the extent of challenges that students’ experienced during online learning. Overall, the students experienced the identified challenges to a moderate extent ( x̅  = 2.62, SD  = 1.03) with scores ranging from x̅  = 1.72 ( to some extent ) to x̅  = 3.58 ( to a great extent ). More specifically, the greatest challenge that students experienced was related to the learning environment ( x̅  = 3.49, SD  = 1.27), particularly on distractions at home, limitations in completing the requirements for certain subjects, and difficulties in selecting the learning areas and study schedule. It is, however, found that the least challenge was on technological literacy and competency ( x̅  = 2.10, SD  = 1.13), particularly on knowledge and training in the use of technology, technological intimidation, and resistance to learning technologies. Other areas that students experienced the least challenge are Internet access under TSC and procrastination under SRC. Nonetheless, nearly half of the students’ responses per indicator rated the challenges they experienced as moderate (14 of the 37 indicators), particularly in TCC ( x̅  = 2.51, SD  = 1.31), SIC ( x̅  = 2.77, SD  = 1.34), and LRC ( x̅  = 2.93, SD  = 1.31).

Out of 200 students, 181 responded to the question about other challenges that they experienced. Most of their responses were already covered by the seven predetermined categories, except for 18 responses related to physical discomfort ( N  = 5) and financial challenges ( N  = 13). For instance, S108 commented that “when it comes to eyes and head, my eyes and head get ache if the session of class was 3 h straight in front of my gadget.” In the same vein, S194 reported that “the long exposure to gadgets especially laptop, resulting in body pain & headaches.” With reference to physical financial challenges, S66 noted that “not all the time I have money to load”, while S121 claimed that “I don't know until when are we going to afford budgeting our money instead of buying essentials.”

4.2 Impact of the pandemic on students’ online learning challenges

Another objective of this study was to identify how COVID-19 influenced the online learning challenges that students experienced. As shown in Table 3 , most of the students’ responses were related to teaching and learning quality ( N  = 86) and anxiety and other mental health issues ( N  = 52). Regarding the adverse impact on teaching and learning quality, most of the comments relate to the lack of preparation for the transition to online platforms (e.g., S23, S64), limited infrastructure (e.g., S13, S65, S99, S117), and poor Internet service (e.g., S3, S9, S17, S41, S65, S99). For the anxiety and mental health issues, most students reported that the anxiety, boredom, sadness, and isolation they experienced had adversely impacted the way they learn (e.g., S11, S130), completing their tasks/activities (e.g., S56, S156), and their motivation to continue studying (e.g., S122, S192). The data also reveal that COVID-19 aggravated the financial difficulties experienced by some students ( N  = 16), consequently affecting their online learning experience. This financial impact mainly revolved around the lack of funding for their online classes as a result of their parents’ unemployment and the high cost of Internet data (e.g., S18, S113, S167). Meanwhile, few concerns were raised in relation to COVID-19’s impact on mobility ( N  = 7) and face-to-face interactions ( N  = 7). For instance, some commented that the lack of face-to-face interaction with her classmates had a detrimental effect on her learning (S46) and socialization skills (S36), while others reported that restrictions in mobility limited their learning experience (S78, S110). Very few comments were related to no effect ( N  = 4) and positive effect ( N  = 2). The above findings suggest the pandemic had additive adverse effects on students’ online learning experience.

4.3 Students’ strategies to overcome challenges in an online learning environment

The third objective of this study is to identify the strategies that students employed to overcome the different online learning challenges they experienced. Table 4 presents that the most commonly used strategies used by students were resource management and utilization ( N  = 181), help-seeking ( N  = 155), technical aptitude enhancement ( N  = 122), time management ( N  = 98), and learning environment control ( N  = 73). Not surprisingly, the top two strategies were also the most consistently used across different challenges. However, looking closely at each of the seven challenges, the frequency of using a particular strategy varies. For TSC and LRC, the most frequently used strategy was resource management and utilization ( N  = 52, N  = 89, respectively), whereas technical aptitude enhancement was the students’ most preferred strategy to address TLCC ( N  = 77) and TCC ( N  = 38). In the case of SRC, SIC, and LEC, the most frequently employed strategies were time management ( N  = 71), psychological support ( N  = 53), and learning environment control ( N  = 60). In terms of consistency, help-seeking appears to be the most consistent across the different challenges in an online learning environment. Table 4 further reveals that strategies used by students within a specific type of challenge vary.

5 Discussion and conclusions

The current study explores the challenges that students experienced in an online learning environment and how the pandemic impacted their online learning experience. The findings revealed that the online learning challenges of students varied in terms of type and extent. Their greatest challenge was linked to their learning environment at home, while their least challenge was technological literacy and competency. Based on the students’ responses, their challenges were also found to be aggravated by the pandemic, especially in terms of quality of learning experience, mental health, finances, interaction, and mobility. With reference to previous studies (i.e., Adarkwah, 2021 ; Copeland et al., 2021 ; Day et al., 2021 ; Fawaz et al., 2021 ; Kapasia et al., 2020 ; Khalil et al., 2020 ; Singh et al., 2020 ), the current study has complemented their findings on the pedagogical, logistical, socioeconomic, technological, and psychosocial online learning challenges that students experience within the context of the COVID-19 pandemic. Further, this study extended previous studies and our understanding of students’ online learning experience by identifying both the presence and extent of online learning challenges and by shedding light on the specific strategies they employed to overcome them.

Overall findings indicate that the extent of challenges and strategies varied from one student to another. Hence, they should be viewed as a consequence of interaction several many factors. Students’ responses suggest that their online learning challenges and strategies were mediated by the resources available to them, their interaction with their teachers and peers, and the school’s existing policies and guidelines for online learning. In the context of the pandemic, the imposed lockdowns and students’ socioeconomic condition aggravated the challenges that students experience.

While most studies revealed that technology use and competency were the most common challenges that students face during the online classes (see Rasheed et al., 2020 ), the case is a bit different in developing countries in times of pandemic. As the findings have shown, the learning environment is the greatest challenge that students needed to hurdle, particularly distractions at home (e.g., noise) and limitations in learning space and facilities. This data suggests that online learning challenges during the pandemic somehow vary from the typical challenges that students experience in a pre-pandemic online learning environment. One possible explanation for this result is that restriction in mobility may have aggravated this challenge since they could not go to the school or other learning spaces beyond the vicinity of their respective houses. As shown in the data, the imposition of lockdown restricted students’ learning experience (e.g., internship and laboratory experiments), limited their interaction with peers and teachers, caused depression, stress, and anxiety among students, and depleted the financial resources of those who belong to lower-income group. All of these adversely impacted students’ learning experience. This finding complemented earlier reports on the adverse impact of lockdown on students’ learning experience and the challenges posed by the home learning environment (e.g., Day et al., 2021 ; Kapasia et al., 2020 ). Nonetheless, further studies are required to validate the impact of restrictions on mobility on students’ online learning experience. The second reason that may explain the findings relates to students’ socioeconomic profile. Consistent with the findings of Adarkwah ( 2021 ) and Day et al. ( 2021 ), the current study reveals that the pandemic somehow exposed the many inequities in the educational systems within and across countries. In the case of a developing country, families from lower socioeconomic strata (as in the case of the students in this study) have limited learning space at home, access to quality Internet service, and online learning resources. This is the reason the learning environment and learning resources recorded the highest level of challenges. The socioeconomic profile of the students (i.e., low and middle-income group) is the same reason financial problems frequently surfaced from their responses. These students frequently linked the lack of financial resources to their access to the Internet, educational materials, and equipment necessary for online learning. Therefore, caution should be made when interpreting and extending the findings of this study to other contexts, particularly those from higher socioeconomic strata.

Among all the different online learning challenges, the students experienced the least challenge on technological literacy and competency. This is not surprising considering a plethora of research confirming Gen Z students’ (born since 1996) high technological and digital literacy (Barrot, 2018 ; Ng, 2012 ; Roblek et al., 2019 ). Regarding the impact of COVID-19 on students’ online learning experience, the findings reveal that teaching and learning quality and students’ mental health were the most affected. The anxiety that students experienced does not only come from the threats of COVID-19 itself but also from social and physical restrictions, unfamiliarity with new learning platforms, technical issues, and concerns about financial resources. These findings are consistent with that of Copeland et al. ( 2021 ) and Fawaz et al. ( 2021 ), who reported the adverse effects of the pandemic on students’ mental and emotional well-being. This data highlights the need to provide serious attention to the mediating effects of mental health, restrictions in mobility, and preparedness in delivering online learning.

Nonetheless, students employed a variety of strategies to overcome the challenges they faced during online learning. For instance, to address the home learning environment problems, students talked to their family (e.g., S12, S24), transferred to a quieter place (e.g., S7, S 26), studied at late night where all family members are sleeping already (e.g., S51), and consulted with their classmates and teachers (e.g., S3, S9, S156, S193). To overcome the challenges in learning resources, students used the Internet (e.g., S20, S27, S54, S91), joined Facebook groups that share free resources (e.g., S5), asked help from family members (e.g., S16), used resources available at home (e.g., S32), and consulted with the teachers (e.g., S124). The varying strategies of students confirmed earlier reports on the active orientation that students take when faced with academic- and non-academic-related issues in an online learning space (see Fawaz et al., 2021 ). The specific strategies that each student adopted may have been shaped by different factors surrounding him/her, such as available resources, student personality, family structure, relationship with peers and teacher, and aptitude. To expand this study, researchers may further investigate this area and explore how and why different factors shape their use of certain strategies.

Several implications can be drawn from the findings of this study. First, this study highlighted the importance of emergency response capability and readiness of higher education institutions in case another crisis strikes again. Critical areas that need utmost attention include (but not limited to) national and institutional policies, protocol and guidelines, technological infrastructure and resources, instructional delivery, staff development, potential inequalities, and collaboration among key stakeholders (i.e., parents, students, teachers, school leaders, industry, government education agencies, and community). Second, the findings have expanded our understanding of the different challenges that students might confront when we abruptly shift to full online learning, particularly those from countries with limited resources, poor Internet infrastructure, and poor home learning environment. Schools with a similar learning context could use the findings of this study in developing and enhancing their respective learning continuity plans to mitigate the adverse impact of the pandemic. This study would also provide students relevant information needed to reflect on the possible strategies that they may employ to overcome the challenges. These are critical information necessary for effective policymaking, decision-making, and future implementation of online learning. Third, teachers may find the results useful in providing proper interventions to address the reported challenges, particularly in the most critical areas. Finally, the findings provided us a nuanced understanding of the interdependence of learning tools, learners, and learning outcomes within an online learning environment; thus, giving us a multiperspective of hows and whys of a successful migration to full online learning.

Some limitations in this study need to be acknowledged and addressed in future studies. One limitation of this study is that it exclusively focused on students’ perspectives. Future studies may widen the sample by including all other actors taking part in the teaching–learning process. Researchers may go deeper by investigating teachers’ views and experience to have a complete view of the situation and how different elements interact between them or affect the others. Future studies may also identify some teacher-related factors that could influence students’ online learning experience. In the case of students, their age, sex, and degree programs may be examined in relation to the specific challenges and strategies they experience. Although the study involved a relatively large sample size, the participants were limited to college students from a Philippine university. To increase the robustness of the findings, future studies may expand the learning context to K-12 and several higher education institutions from different geographical regions. As a final note, this pandemic has undoubtedly reshaped and pushed the education system to its limits. However, this unprecedented event is the same thing that will make the education system stronger and survive future threats.

Availability of data and materials

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

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Barrot, J.S., Llenares, I.I. & del Rosario, L.S. Students’ online learning challenges during the pandemic and how they cope with them: The case of the Philippines. Educ Inf Technol 26 , 7321–7338 (2021). https://doi.org/10.1007/s10639-021-10589-x

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A structural equation model predicting adults’ online learning self-efficacy

How important is the tuition fee during the covid-19 pandemic in a developing country evaluation of filipinos’ preferences on public university attributes using conjoint analysis, student perceptions of online biology learning during the covid-19 pandemic, journal pre-proof is tuition fee during the covid-19 pandemic in a developing country evaluation of filipinos’ preferences on public university attributes using conjoint analysis, ruralsync: providing digital content to remote communities in the philippines through opportunistic spectrum access, the chairman's statement, related papers (5), online learning during lockdown period for covid-19 in india, online learning as a catalyst for self-directed learning in universities during the covid-19 pandemic, integration of knowledge through online classes in the learning enhancement of students, right here, right now, online: using synchronous tools to teach business subjects to students learning in a second language, online education: a change or an alternative, trending questions (3).

The shift to online teaching during the pandemic in the Philippines caused disruptions and challenges for students and teachers, impacting class dynamics and learning outcomes.

- Mental health issues due to lack of social interaction. - Increased stress and anxiety from disrupted learning routines.

Learning conditions in the Philippines during the Covid-19 pandemic faced disruptions with a shift to online classes, causing issues like lack of technology and mental health concerns among students and teachers.

Online classes and learning in the Philippines during the Covid-19 Pandemic

• Aris E. Ignacio College of Information Technology Southville International School and Colleges Las Piñas City 1740, Philippines

The COVID-19 pandemic brought great disruption to all aspects of life specifically on how classes were conducted both in an offline and online modes. The sudden shift to purely online method of teaching and learning was a result of the lockdowns that were imposed by the Philippine government. While some institutions have dealt with the situation by shutting down operations, others continued to deliver instructions and lessons using the Internet and different applications that support online learning. The continuation of classes online had caused several issues from students and teachers ranging from lack of technology to mental health matters. Finally, recommendations were asserted to mitigate the presented concerns and improve the delivery of the necessary quality education to the intended learners.

Austria, I., Ignacio, A., Mirandilla-Santos, M.G. & Yu, W. (2020). Learning from Home: How Philippine Schools Can Respond to the COVID-19 Outbreak. Retrieved from https://www.facebook.com/notes/381262523025716/

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Online classes and learning in the Philippines during the Covid-19 Pandemic

• Aris E. Ignacio
• Published 4 March 2021
• Education, Computer Science

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Study Protocol

Assessing the effect of the COVID-19 pandemic, shift to online learning, and social media use on the mental health of college students in the Philippines: A mixed-method study protocol

Roles Funding acquisition, Writing – original draft

Affiliation College of Medicine, University of the Philippines, Manila, Philippines

Roles Methodology, Supervision, Visualization, Writing – original draft, Writing – review & editing

Affiliations Department of Clinical Epidemiology, College of Medicine, University of the Philippines, Manila, Philippines, Institute of Clinical Epidemiology, National Institutes of Health, University of the Philippines, Manila, Philippines

Roles Methodology

Affiliation Department of Psychiatry, College of Medicine, University of the Philippines, Manila, Philippines

Roles Conceptualization, Funding acquisition, Project administration, Supervision, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

• Leonard Thomas S. Lim, 
• Zypher Jude G. Regencia, 
• J. Rem C. Dela Cruz, 
• Frances Dominique V. Ho, 
• Marcela S. Rodolfo, 
• Josefina Ly-Uson, 
• Emmanuel S. Baja

• Published: May 3, 2022
• https://doi.org/10.1371/journal.pone.0267555
• Peer Review
• Reader Comments

Introduction

The COVID-19 pandemic declared by the WHO has affected many countries rendering everyday lives halted. In the Philippines, the lockdown quarantine protocols have shifted the traditional college classes to online. The abrupt transition to online classes may bring psychological effects to college students due to continuous isolation and lack of interaction with fellow students and teachers. Our study aims to assess Filipino college students’ mental health status and to estimate the effect of the COVID-19 pandemic, the shift to online learning, and social media use on mental health. In addition, facilitators or stressors that modified the mental health status of the college students during the COVID-19 pandemic, quarantine, and subsequent shift to online learning will be investigated.

Methods and analysis

Mixed-method study design will be used, which will involve: (1) an online survey to 2,100 college students across the Philippines; and (2) randomly selected 20–40 key informant interviews (KIIs). Online self-administered questionnaire (SAQ) including Depression, Anxiety, and Stress Scale (DASS-21) and Brief-COPE will be used. Moreover, socio-demographic factors, social media usage, shift to online learning factors, family history of mental health and COVID-19, and other factors that could affect mental health will also be included in the SAQ. KIIs will explore factors affecting the student’s mental health, behaviors, coping mechanism, current stressors, and other emotional reactions to these stressors. Associations between mental health outcomes and possible risk factors will be estimated using generalized linear models, while a thematic approach will be made for the findings from the KIIs. Results of the study will then be triangulated and summarized.

Ethics and dissemination

Our study has been approved by the University of the Philippines Manila Research Ethics Board (UPMREB 2021-099-01). The results will be actively disseminated through conference presentations, peer-reviewed journals, social media, print and broadcast media, and various stakeholder activities.

Citation: Lim LTS, Regencia ZJG, Dela Cruz JRC, Ho FDV, Rodolfo MS, Ly-Uson J, et al. (2022) Assessing the effect of the COVID-19 pandemic, shift to online learning, and social media use on the mental health of college students in the Philippines: A mixed-method study protocol. PLoS ONE 17(5): e0267555. https://doi.org/10.1371/journal.pone.0267555

Editor: Elisa Panada, UNITED KINGDOM

Received: June 9, 2021; Accepted: April 11, 2022; Published: May 3, 2022

Copyright: © 2022 Lim 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.

Funding: This project is being supported by the American Red Cross through the Philippine Red Cross and Red Cross Youth. The funder will not have a role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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

The World Health Organization (WHO) declared the Coronavirus 2019 (COVID-19) outbreak as a global pandemic, and the Philippines is one of the 213 countries affected by the disease [ 1 ]. To reduce the virus’s transmission, the President imposed an enhanced community quarantine in Luzon, the country’s northern and most populous island, on March 16, 2020. This lockdown manifested as curfews, checkpoints, travel restrictions, and suspension of business and school activities [ 2 ]. However, as the virus is yet to be curbed, varying quarantine restrictions are implemented across the country. In addition, schools have shifted to online learning, despite financial and psychological concerns [ 3 ].

Previous outbreaks such as the swine flu crisis adversely influenced the well-being of affected populations, causing them to develop emotional problems and raising the importance of integrating mental health into medical preparedness for similar disasters [ 4 ]. In one study conducted on university students during the swine flu pandemic in 2009, 45% were worried about personally or a family member contracting swine flu, while 10.7% were panicking, feeling depressed, or emotionally disturbed. This study suggests that preventive measures to alleviate distress through health education and promotion are warranted [ 5 ].

During the COVID-19 pandemic, researchers worldwide have been churning out studies on its psychological effects on different populations [ 6 – 9 ]. The indirect effects of COVID-19, such as quarantine measures, the infection of family and friends, and the death of loved ones, could worsen the overall mental wellbeing of individuals [ 6 ]. Studies from 2020 to 2021 link the pandemic to emotional disturbances among those in quarantine, even going as far as giving vulnerable populations the inclination to commit suicide [ 7 , 8 ], persistent effect on mood and wellness [ 9 ], and depression and anxiety [ 10 ].

In the Philippines, a survey of 1,879 respondents measuring the psychological effects of COVID-19 during its early phase in 2020 was released. Results showed that one-fourth of respondents reported moderate-to-severe anxiety, while one-sixth reported moderate-to-severe depression [ 11 ]. In addition, other local studies in 2020 examined the mental health of frontline workers such as nurses and physicians—placing emphasis on the importance of psychological support in minimizing anxiety [ 12 , 13 ].

Since the first wave of the pandemic in 2020, risk factors that could affect specific populations’ psychological well-being have been studied [ 14 , 15 ]. A cohort study on 1,773 COVID-19 hospitalized patients in 2021 found that survivors were mainly troubled with fatigue, muscle weakness, sleep difficulties, and depression or anxiety [ 16 ]. Their results usually associate the crisis with fear, anxiety, depression, reduced sleep quality, and distress among the general population.

Moreover, the pandemic also exacerbated the condition of people with pre-existing psychiatric disorders, especially patients that live in high COVID-19 prevalence areas [ 17 ]. People suffering from mood and substance use disorders that have been infected with COVID-19 showed higher suicide risks [ 7 , 18 ]. Furthermore, a study in 2020 cited the following factors contributing to increased suicide risk: social isolation, fear of contagion, anxiety, uncertainty, chronic stress, and economic difficulties [ 19 ].

Globally, multiple studies have shown that mental health disorders among university student populations are prevalent [ 13 , 20 – 22 ]. In a 2007 survey of 2,843 undergraduate and graduate students at a large midwestern public university in the United States, the estimated prevalence of any depressive or anxiety disorder was 15.6% and 13.0% for undergraduate and graduate students, respectively [ 20 ]. Meanwhile, in a 2013 study of 506 students from 4 public universities in Malaysia, 27.5% and 9.7% had moderate and severe or extremely severe depression, respectively; 34% and 29% had moderate and severe or extremely severe anxiety, respectively [ 21 ]. In China, a 2016 meta-analysis aiming to establish the national prevalence of depression among university students analyzed 39 studies from 1995 to 2015; the meta-analysis found that the overall prevalence of depression was 23.8% across all studies that included 32,694 Chinese university students [ 23 ].

A college student’s mental status may be significantly affected by the successful fulfillment of a student’s role. A 2013 study found that acceptable teaching methods can enhance students’ satisfaction and academic performance, both linked to their mental health [ 24 ]. However, online learning poses multiple challenges to these methods [ 3 ]. Furthermore, a 2020 study found that students’ mental status is affected by their social support systems, which, in turn, may be jeopardized by the COVID-19 pandemic and the physical limitations it has imposed. Support accessible to a student through social ties to other individuals, groups, and the greater community is a form of social support; university students may draw social support from family, friends, classmates, teachers, and a significant other [ 25 , 26 ]. Among individuals undergoing social isolation and distancing during the COVID-19 pandemic in 2020, social support has been found to be inversely related to depression, anxiety, irritability, sleep quality, and loneliness, with higher levels of social support reducing the risk of depression and improving sleep quality [ 27 ]. Lastly, it has been shown in a 2020 study that social support builds resilience, a protective factor against depression, anxiety, and stress [ 28 ]. Therefore, given the protective effects of social support on psychological health, a supportive environment should be maintained in the classroom. Online learning must be perceived as an inclusive community and a safe space for peer-to-peer interactions [ 29 ]. This is echoed in another study in 2019 on depressed students who narrated their need to see themselves reflected on others [ 30 ]. Whether or not online learning currently implemented has successfully transitioned remains to be seen.

The effect of social media on students’ mental health has been a topic of interest even before the pandemic [ 31 , 32 ]. A systematic review published in 2020 found that social media use is responsible for aggravating mental health problems and that prominent risk factors for depression and anxiety include time spent, activity, and addiction to social media [ 31 ]. Another systematic review published in 2016 argues that the nature of online social networking use may be more important in influencing the symptoms of depression than the duration or frequency of the engagement—suggesting that social rumination and comparison are likely to be candidate mediators in the relationship between depression and social media [ 33 ]. However, their findings also suggest that the relationship between depression and online social networking is complex and necessitates further research to determine the impact of moderators and mediators that underly the positive and negative impact of online social networking on wellbeing [ 33 ].

Despite existing studies already painting a picture of the psychological effects of COVID-19 in the Philippines, to our knowledge, there are still no local studies contextualized to college students living in different regions of the country. Therefore, it is crucial to elicit the reasons and risk factors for depression, stress, and anxiety and determine the potential impact that online learning and social media use may have on the mental health of the said population. In turn, the findings would allow the creation of more context-specific and regionalized interventions that can promote mental wellness during the COVID-19 pandemic.

Materials and methods

The study’s general objective is to assess the mental health status of college students and determine the different factors that influenced them during the COVID-19 pandemic. Specifically, it aims:

• To describe the study population’s characteristics, categorized by their mental health status, which includes depression, anxiety, and stress.
• To determine the prevalence and risk factors of depression, anxiety, and stress among college students during the COVID-19 pandemic, quarantine, and subsequent shift to online learning.
• To estimate the effect of social media use on depression, anxiety, stress, and coping strategies towards stress among college students and examine whether participant characteristics modified these associations.
• To estimate the effect of online learning shift on depression, anxiety, stress, and coping strategies towards stress among college students and examine whether participant characteristics modified these associations.
• To determine the facilitators or stressors among college students that modified their mental health status during the COVID-19 pandemic, quarantine, and subsequent shift to online learning.

Study design

A mixed-method study design will be used to address the study’s objectives, which will include Key Informant Interviews (KIIs) and an online survey. During the quarantine period of the COVID-19 pandemic in the Philippines from April to November 2021, the study shall occur with the population amid community quarantine and an abrupt transition to online classes. Since this is the Philippines’ first study that will look at the prevalence of depression, anxiety, and stress among college students during the COVID-19 pandemic, quarantine, and subsequent shift to online learning, the online survey will be utilized for the quantitative part of the study design. For the qualitative component of the study design, KIIs will determine facilitators or stressors among college students that modified their mental health status during the quarantine period.

Study population

The Red Cross Youth (RCY), one of the Philippine Red Cross’s significant services, is a network of youth volunteers that spans the entire country, having active members in Luzon, Visayas, and Mindanao. The group is clustered into different age ranges, with the College Red Cross Youth (18–25 years old) being the study’s population of interest. The RCY has over 26,060 students spread across 20 chapters located all over the country’s three major island groups. The RCY is heterogeneously composed, with some members classified as college students and some as out-of-school youth. Given their nationwide scope, disseminating information from the national to the local level is already in place; this is done primarily through email, social media platforms, and text blasts. The research team will leverage these platforms to distribute the online survey questionnaire.

In addition, the online survey will also be open to non-members of the RCY. It will be disseminated through social media and engagements with different university administrators in the country. Stratified random sampling will be done for the KIIs. The KII participants will be equally coming from the country’s four (4) primary areas: 5–10 each from the national capital region (NCR), Luzon, Visayas, and Mindanao, including members and non-members of the RCY.

Inclusion and exclusion criteria

The inclusion criteria for the online survey will include those who are 18–25 years old, currently enrolled in a university, can provide consent for the study, and are proficient in English or Filipino. The exclusion criteria will consist of those enrolled in graduate-level programs (e.g., MD, JD, Master’s, Doctorate), out-of-school youth, and those whose current curricula involve going on duty (e.g., MDs, nursing students, allied medical professions, etc.). The inclusion criteria for the KIIs will include online survey participants who are 18–25 years old, can provide consent for the study, are proficient in English or Filipino, and have access to the internet.

Sample size

A continuity correction method developed by Fleiss et al. (2013) was used to calculate the sample size needed [ 34 ]. For a two-sided confidence level of 95%, with 80% power and the least extreme odds ratio to be detected at 1.4, the computed sample size was 1890. With an adjustment for an estimated response rate of 90%, the total sample size needed for the study was 2,100. To achieve saturation for the qualitative part of the study, 20 to 40 participants will be randomly sampled for the KIIs using the respondents who participated in the online survey [ 35 ].

Study procedure

Self-administered questionnaire..

The study will involve creating, testing, and distributing a self-administered questionnaire (SAQ). All eligible study participants will answer the SAQ on socio-demographic factors such as age, sex, gender, sexual orientation, residence, household income, socioeconomic status, smoking status, family history of mental health, and COVID-19 sickness of immediate family members or friends. The two validated survey tools, Depression, Anxiety, and Stress Scale (DASS-21) and Brief-COPE, will be used for the mental health outcome assessment [ 36 – 39 ]. The DASS-21 will measure the negative emotional states of depression, anxiety, and stress [ 40 ], while the Brief-COPE will measure the students’ coping strategies [ 41 ].

For the exposure assessment of the students to social media and shift to online learning, the total time spent on social media (TSSM) per day will be ascertained by querying the participants to provide an estimated time spent daily on social media during and after their online classes. In addition, students will be asked to report their use of the eight commonly used social media sites identified at the start of the study. These sites include Facebook, Twitter, Instagram, LinkedIn, Pinterest, TikTok, YouTube, and social messaging sites Viber/WhatsApp and Facebook Messenger with response choices coded as "(1) never," "(2) less often," "(3) every few weeks," "(4) a few times a week," and “(5) daily” [ 42 – 44 ]. Furthermore, a global frequency score will be calculated by adding the response scores from the eight social media sites. The global frequency score will be used as an additional exposure marker of students to social media [ 45 ]. The shift to online learning will be assessed using questions that will determine the participants’ satisfaction with online learning. This assessment is comprised of 8 items in which participants will be asked to respond on a 5-point Likert scale ranging from ‘strongly disagree’ to ‘strongly agree.’

The online survey will be virtually distributed in English using the Qualtrics XM™ platform. Informed consent detailing the purpose, risks, benefits, methods, psychological referrals, and other ethical considerations will be included before the participants are allowed to answer the survey. Before administering the online survey, the SAQ shall undergo pilot testing among twenty (20) college students not involved with the study. It aims to measure total test-taking time, respondent satisfaction, and understandability of questions. The survey shall be edited according to the pilot test participant’s responses. Moreover, according to the Philippines’ Data Privacy Act, all the answers will be accessible and used only for research purposes.

Key informant interviews.

The research team shall develop the KII concept note, focusing on the extraneous factors affecting the student’s mental health, behaviors, and coping mechanism. Some salient topics will include current stressors (e.g., personal, academic, social), emotional reactions to these stressors, and how they wish to receive support in response to these stressors. The KII will be facilitated by a certified psychologist/psychiatrist/social scientist and research assistants using various online video conferencing software such as Google Meet, Skype, or Zoom. All the KIIs will be recorded and transcribed for analysis. Furthermore, there will be a debriefing session post-KII to address the psychological needs of the participants. Fig 1 presents the diagrammatic flowchart of the study.

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https://doi.org/10.1371/journal.pone.0267555.g001

Data analyses

Quantitative data..

Descriptive statistics will be calculated, including the prevalence of mental health outcomes such as depression, anxiety, stress, and coping strategies. In addition, correlation coefficients will be estimated to assess the relations among the different mental health outcomes, covariates, and possible risk factors.

Several study characteristics as effect modifiers will also be assessed, including sex, gender, sexual orientation, family income, smoking status, family history of mental health, and Covid-19. We will include interaction terms between the dichotomized modifier variable and markers of social media use (total TSSM and global frequency score) and shift to online learning in the models. The significance of the interaction terms will be evaluated using the likelihood ratio test. All the regression analyses will be done in R ( http://www.r-project.org ). P values ≤ 0.05 will be considered statistically significant.

Qualitative data.

After transcribing the interviews, the data transcripts will be analyzed using NVivo 1.4.1 software [ 50 ] by three research team members independently using the inductive logic approach in thematic analysis: familiarizing with the data, generating initial codes, searching for themes, reviewing the themes, defining and naming the themes, and producing the report [ 51 ]. Data familiarization will consist of reading and re-reading the data while noting initial ideas. Additionally, coding interesting features of the data will follow systematically across the entire dataset while collating data relevant to each code. Moreover, the open coding of the data will be performed to describe the data into concepts and themes, which will be further categorized to identify distinct concepts and themes [ 52 ].

The three researchers will discuss the results of their thematic analyses. They will compare and contrast the three analyses in order to come up with a thematic map. The final thematic map of the analysis will be generated after checking if the identified themes work in relation to the extracts and the entire dataset. In addition, the selection of clear, persuasive extract examples that will connect the analysis to the research question and literature will be reviewed before producing a scholarly report of the analysis. Additionally, the themes and sub-themes generated will be assessed and discussed in relevance to the study’s objectives. Furthermore, the gathering and analyzing of the data will continue until saturation is reached. Finally, pseudonyms will be used to present quotes from qualitative data.

Data triangulation.

Data triangulation using the two different data sources will be conducted to examine the various aspects of the research and will be compared for convergence. This part of the analysis will require listing all the relevant topics or findings from each component of the study and considering where each method’s results converge, offer complementary information on the same issue, or appear to contradict each other. It is crucial to explicitly look for disagreements between findings from different data collection methods because exploration of any apparent inter-method discrepancy may lead to a better understanding of the research question [ 53 , 54 ].

Data management plan.

The Project Leader will be responsible for overall quality assurance, with research associates and assistants undertaking specific activities to ensure quality control. Quality will be assured through routine monitoring by the Project Leader and periodic cross-checks against the protocols by the research assistants. Transcribed KIIs and the online survey questionnaire will be used for recording data for each participant in the study. The project leader will be responsible for ensuring the accuracy, completeness, legibility, and timeliness of the data captured in all the forms. Data captured from the online survey or KIIs should be consistent, clarified, and corrected. Each participant will have complete source documentation of records. Study staff will prepare appropriate source documents and make them available to the Project Leader upon request for review. In addition, study staff will extract all data collected in the KII notes or survey forms. These data will be secured and kept in a place accessible to the Project Leader. Data entry and cleaning will be conducted, and final data cleaning, data freezing, and data analysis will be performed. Key informant interviews will always involve two researchers. Where appropriate, quality control for the qualitative data collection will be assured through refresher KII training during research design workshops. The Project Leader will check through each transcript for consistency with agreed standards. Where translations are undertaken, the quality will be assured by one other researcher fluent in that language checking against the original recording or notes.

Ethics approval.

The study shall abide by the Principles of the Declaration of Helsinki (2013). It will be conducted along with the Guidelines of the International Conference on Harmonization-Good Clinical Practice (ICH-GCP), E6 (R2), and other ICH-GCP 6 (as amended); National Ethical Guidelines for Health and Health-Related Research (NEGHHRR) of 2017. This protocol has been approved by the University of the Philippines Manila Research Ethics Board (UPMREB 2021-099-01 dated March 25, 2021).

The main concerns for ethics were consent, data privacy, and subject confidentiality. The risks, benefits, and conflicts of interest are discussed in this section from an ethical standpoint.

Recruitment.

The participants will be recruited to answer the online SAQ voluntarily. The recruitment of participants for the KIIs will be chosen through stratified random sampling using a list of those who answered the online SAQ; this will minimize the risk of sampling bias. In addition, none of the participants in the study will have prior contact or association with the researchers. Moreover, power dynamics will not be contacted to recruit respondents. The research objectives, methods, risks, benefits, voluntary participation, withdrawal, and respondents’ rights will be discussed with the respondents in the consent form before KII.

Informed consent will be signified by the potential respondent ticking a box in the online informed consent form and the voluntary participation of the potential respondent to the study after a thorough discussion of the research details. The participant’s consent is voluntary and may be recanted by the participant any time s/he chooses.

Data privacy.

All digital data will be stored in a cloud drive accessible only to the researchers. Subject confidentiality will be upheld through the assignment of control numbers and not requiring participants to divulge the name, address, and other identifying factors not necessary for analysis.

Compensation.

No monetary compensation will be given to the participants, but several tokens will be raffled to all the participants who answered the online survey and did the KIIs.

This research will pose risks to data privacy, as discussed and addressed above. In addition, there will be a risk of social exclusion should data leaks arise due to the stigma against mental health. This risk will be mitigated by properly executing the data collection and analysis plan, excluding personal details and tight data privacy measures. Moreover, there is a risk of psychological distress among the participants due to the sensitive information. This risk will be addressed by subjecting the SAQ and the KII guidelines to the project team’s psychiatrist’s approval, ensuring proper communication with the participants. The KII will also be facilitated by registered clinical psychologists/psychiatrists/social scientists to ensure the participants’ appropriate handling; there will be a briefing and debriefing of the participants before and after the KII proper.

Participation in this study will entail health education and a voluntary referral to a study-affiliated psychiatrist, discussed in previous sections. Moreover, this would contribute to modifications in targeted mental-health campaigns for the 18–25 age group. Summarized findings and recommendations will be channeled to stakeholders for their perusal.

Dissemination.

The results will be actively disseminated through conference presentations, peer-reviewed journals, social media, print and broadcast media, and various stakeholder activities.

This study protocol rationalizes the examination of the mental health of the college students in the Philippines during the COVID-19 pandemic as the traditional face-to-face classes transitioned to online and modular classes. The pandemic that started in March 2020 is now stretching for more than a year in which prolonged lockdown brings people to experience social isolation and disruption of everyday lifestyle. There is an urgent need to study the psychosocial aspects, particularly those populations that are vulnerable to mental health instability. In the Philippines, where community quarantine is still being imposed across the country, college students face several challenges amidst this pandemic. The pandemic continues to escalate, which may lead to fear and a spectrum of psychological consequences. Universities and colleges play an essential role in supporting college students in their academic, safety, and social needs. The courses of activities implemented by the different universities and colleges may significantly affect their mental well-being status. Our study is particularly interested in the effect of online classes on college students nationwide during the pandemic. The study will estimate this effect on their mental wellbeing since this abrupt transition can lead to depression, stress, or anxiety for some students due to insufficient time to adjust to the new learning environment. The role of social media is also an important exposure to some college students [ 55 , 56 ]. Social media exposure to COVID-19 may be considered a contributing factor to college students’ mental well-being, particularly their stress, depression, and anxiety [ 57 , 58 ]. Despite these known facts, little is known about the effect of transitioning to online learning and social media exposure on the mental health of college students during the COVID-19 pandemic in the Philippines. To our knowledge, this is the first study in the Philippines that will use a mixed-method study design to examine the mental health of college students in the entire country. The online survey is a powerful platform to employ our methods.

Additionally, our study will also utilize a qualitative assessment of the college students, which may give significant insights or findings of the experiences of the college students during these trying times that cannot be captured on our online survey. The thematic findings or narratives from the qualitative part of our study will be triangulated with the quantitative analysis for a more robust synthesis. The results will be used to draw conclusions about the mental health status among college students during the pandemic in the country, which will eventually be used to implement key interventions if deemed necessary. A cross-sectional study design for the online survey is one of our study’s limitations in which contrasts will be mainly between participants at a given point of time. In addition, bias arising from residual or unmeasured confounding factors cannot be ruled out.

The COVID-19 pandemic and its accompanying effects will persistently affect the mental wellbeing of college students. Mental health services must be delivered to combat mental instability. In addition, universities and colleges should create an environment that will foster mental health awareness among Filipino college students. The results of our study will tailor the possible coping strategies to meet the specific needs of college students nationwide, thereby promoting psychological resilience.

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The COVID-19 pandemic brought great disruption to all aspects of life specifically on how classes were conducted both in an offline and online modes. The sudden shift to purely online method of teaching and learning was a result of the lockdowns that were imposed by the Philippine government. While some institutions have dealt with the situation by shutting down operations, others continued to deliver instructions and lessons using the Internet and different applications that support online learning. The continuation of classes online had caused several issues from students and teachers ranging from lack of technology to mental health matters. Finally, recommendations were asserted to mitigate the presented concerns and improve the delivery of the necessary quality education to the intended learners.

Table of contents

Exploring the Online Learning Experience of Filipino College Students During Covid-19 Pandemic

• Louie Giray College of Education, Polytechnic University of the Philipines, Taguig City, Philippines
• Daxjhed Gumalin College of Education, Polytechnic University of the Philipines, Taguig City, Philippines
• Jomarie Jacob College of Education, Polytechnic University of the Philipines, Taguig City, Philippines
• Karl Villacorta College of Education, Polytechnic University of the Philipines, Taguig City, Philippines

This study was endeavored to understand the online learning experience of Filipino college students enrolled in the academic year 2020-2021 during the COVID-19 pandemic. The data were obtained through an open-ended qualitative survey. The responses were analyzed and interpreted using thematic analysis. A total of 71 Filipino college students from state and local universities in the Philippines participated in this study. Four themes were classified from the collected data: (1) negative views toward online schooling, (2) positive views toward online schooling, (3) difficulties encountered in online schooling, and (4) motivation to continue studying. The results showed that although many Filipino college students find online learning amid the COVID-19 pandemic to be a positive experience such as it provides various conveniences, eliminates the necessity of public transportation amid the COVID-19 pandemic, among others, a more significant number of respondents believe otherwise. The majority of the respondents shared a general difficulty adjusting toward the new online learning setup because of problems related to technology and Internet connectivity, mental health, finances, and time and space management. A large portion of students also got their motivation to continue studying despite the pandemic from fear of being left behind, parental persuasion, and aspiration to help the family.

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• Open access
• Published: 14 July 2022

The impact of extreme weather on student online learning participation

• Ezekiel Adriel D. Lagmay 1 &
• Maria Mercedes T. Rodrigo   ORCID: orcid.org/0000-0001-7881-7756 1  

Research and Practice in Technology Enhanced Learning volume  17 , Article number:  26 ( 2022 ) Cite this article

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In March 2020, the COVID-19 pandemic forced over 1 billion learners to shift from face-to-face instruction to online learning. Seven months after it began, this transition became even more challenging for Filipino online learners. Eight typhoons struck the Philippines from October to November 2020. Two of these typhoons caused widespread flooding, utilities interruptions, property destruction, and loss of life. We examine how these severe weather conditions affected online learning participation of Filipino students pursuing their undergraduate and graduate studies. We used CausalImpact analysis to explore September 2020 to January 2021 data collected from the Moodle Learning Management System data of one university in the Philippines. We found that overall student online participation was significantly negatively affected by typhoons. However, the effect on participation in Assignments and Quizzes was not significant. These findings suggested that students continued to participate in activities that have a direct bearing on their final grades, rather than activities that had no impact on their course outcomes.

Context of the study

The shift to online learning because of COVID-19 offered us a unique opportunity to quantify the impact of extreme weather on the online learning participation of Filipino students. In prior years, the majority of education in the Philippines, as in most countries, took place in person. While some institutions made use of Learning Management Systems (LMSs), most instruction was face to face. LMSs were repositories for materials, submission sites, or test platforms, but were typically not used to replace class time. The onset of the pandemic forced 1 billion students (UNESCO, 2021 ), including Filipinos, to shift to an online mode. The struggle to teach and learn online worsened when eight typhoons entered the Philippine Area of Responsibility (PAR) from October 11 to November 12, 2020 (Lalu, 2020 ). Two of them, Typhoons Goni and Vamco, were particularly destructive, causing widespread destruction, utilities disruptions, and loss of life. The migration of all instruction to digital platforms thus enabled us to capture a greater variety of instructional activities, data that were previously unavailable, and to use this data to study the effects of these typhoons on student learning behaviors.

Effects of extreme weather on academic achievement

The immediate effects of extreme weather events such as severe typhoons and heat waves include property destruction, crop failure, and human casualties. On November 8, 2013, for example, Typhoon Haiyan made landfall in the Philippines. A Category 5 storm, it was one of the most powerful typhoons of all time. It displaced 4.1 million people, killed 6,000, damaged 1.1 million homes, and destroyed 33 million coconut trees, a major cash crop (World Vision, 2021 ). In total, Typhoon Haiyan caused damages estimated at US$5.8 billion. Typhoon Goni made landfall in the Philippines on October 27, 2020, seven months into the COVID-19 pandemic. Like Haiyan, Goni was a Category 5 storm, the strongest of 2020, with maximum sustained winds of 255 km per hour. It left 25 dead and damaged over 280,000 houses. Damage to crops, livestock, fisheries, and agriculture was estimated at P5 billion, while damage to infrastructure such as roads and bridges was estimated at P12.8 billion (International Federation of Red Cross & Red Crescent Societies, 2020a ). Typhoon Vamco made landfall in the Philippines on November 11, 2020. Vamco was weaker than Goni, with maximum sustained winds at 155 km per hour (International Federation of Red Cross & Red Crescent Societies, 2020b ). However, Vamco brought historically high levels of flooding in parts of the country—the worst in 45 years. The storm killed 101 people and left over P20 billion in damages to livelihoods and infrastructure.

The longer-term consequences of these events are far-reaching and complex. In the developing world specifically, limited savings among less wealthy households and the lack of social supports such as access to credit and insurance make it difficult for poorer families to recover from shocks caused by extreme weather (Groppo & Kraehnert, 2017 ; Marchetta et al., 2018 ). Parents are forced to shift their investments from their children’s schooling, e.g., uniforms, books, transportation, tuition (Joshi, 2019 ), instead directing their resources to recovery from the economic consequences of the typhoon’s damage (Deuchert & Felfe, 2015 ). Post-typhoon enrollment decreases. Parents spend less time on their children’s learning and care (Joshi, 2019 ). Children spend less time in school and more time helping at home. Teens and young adults who are transitioning from school to work are particularly vulnerable to these shocks. They are likely to drop out of school and join the workforce in order to mitigate the impact of extreme weather. Poor young women in particular are susceptible to being pushed into the labor market (Marchetta et al., 2018 ).

These necessary choices cause an immediate gap in learning that grows over time. When Typhoon Mike hit Cebu in 1990, the children whose houses suffered typhoon damage lagged 0.13 years behind in school. The lag grew to 0.27 years in 1998, 0.52 years in 2002, and 0.67 years in 2005. By the time children are 22 years old, the gap in educational attainment is approximated at one year (Deuchert & Felfe, 2015 ).

The work of Bernabe et al. ( 2021 ) agrees. They found that storms have a disruptive impact on education. In areas severely affected by winds, children are 9% more likely to accumulate an educational delay and 6.5% less likely to complete secondary education. Individuals severely affected by storms between the ages of 23 and 33 are less likely to complete higher education, reducing their ability to obtain regular salaried jobs.

One might ask: Is it not possible for these children and young adults to return to school to make up for these gaps? Cunha and Heckman ( 2007 ) argue that different stages of childhood are more receptive to certain types of inputs than others. Secondary language learning, for example, is best before 12. They also find that public training programs for adults that try to bridge learning gaps from childhood do not produce substantial gains for most of their participants and tend to be more costly than remediation provided at earlier ages.

In summary, the physical and economic damage wrought by extreme weather events has an adverse impact on educational achievement. The education of young children who come from economically disadvantaged homes receives less financial support and parental attention, resulting in an achievement gap that increases with time. Adolescents and young adults, on the other hand, are sometimes forced to discontinue their studies and to enter the workforce to help mitigate the effects of the event. Resuming studies after an interruption is challenging because oftentimes an optimal window for learning has passed and attempts at remediation are costly and generally produce fewer gains.

Research questions

For this study, we ask two main research questions:

RQ1: To what extent was student participation affected by Typhoons Goni and Vamco?

RQ2: Was student participation able to return to pre-typhoon levels, or did the typhoons dampen participation for the rest of the post-typhoon period? If participation did return to pre-typhoon levels, how long did it take for participation to recover?

Time series analysis in education

We use CausalImpact analysis (Brodersen et al., 2015 ) to analyze the ways in which student participation in an online learning environment was affected by Typhoons Goni and Vamco. CausalImpact is a type of causal inference analysis method for time series data.

Time series analysis methods

Causal inference refers to a family of analysis methods that enable researchers to draw conclusions about the effect of a causal variable or treatment on some outcome or phenomenon of interest (Hill & Stuart, 2015 ). These methods have the same general approach: They take time series data prior to an interruption or intervention, create a model from this data, use the model to predict counterfactual post-intervention trends, and then compare the counterfactual against the actual data to check for differences. They differ in terms of their underlying modeling approach. Examples of these methods are as follows (Kuromiya et al., 2020 ; Moraffah et al., 2021 ):

CausalImpact—It is developed to evaluate the impact of a market intervention using difference-in-difference to infer the causality from observational data. Under the hood, “…it builds a Bayesian structural time series model based on multiple comparable control groups (or markets) and uses the model to project (or forecast) a series of baseline values for the time period after the event.” (Brodersen et al., 2015 ; Nishida, 2017 )

Interrupted Time Series (ITS) Model—Uses segmented regression model with dummy variables representing the period of the intervention for evaluating the effectiveness of population-level interventions. It is simple in terms of interpreting the results. (Bernal et al., 2017 )

Prophet—A type of generalized additive model consisting of trend, seasonality, and holidays. There is no need to interpolate missing values since the model handles time series analysis as a curve fitting problem and can predict future values at a very high accuracy. (Taylor & Letham, 2018 )

CausalTransfer—An improvement to CausalImpact which estimates treatment effects from experiments spanning multiple time points by using a state-space model. The main issue with CausalImpact is that it “treats every time point as a separate experiment and does not pool information over time”; hence, one is “only able to observe the outcomes under the treatment for one time series and under the control for the treatment for another one, but not the potential outcome under control for the former and under treatment for the latter.” CausalTransfer “combines regression to adjust for confounding with time series modelling to learn the effect of the treatment and how it evolves over time” and does not assume that data is stationary. (Li & Bühlmann, 2020 )

Several methods based on neural networks and deep learning have been introduced in recent years (Moraffah et al., 2021 ):

Recurrent Marginal Structural Network (R-MSN)—A sequence-to-sequence recurrent neural network (RNN)-based architecture for forecasting responses to a series of planned treatments. In contrast to other marginal structural models (MSMs) which model “the potential outcomes associated with each possible treatment trajectory with the Inverse Probability of Treatment Weighted (IPTW),” which in turn is “dependent on a correct specification of the conditional probability of treatment assignment,” R-MSN directly learns “time-dependent treatment responses from observational data, based on the marginal structural modeling framework.” (Lim et al., 2018 )

Time Series Deconfounder—This method “uses a novel recurrent neural network architecture with multitask output to build a factor model over time and infer latent variables that render the assigned treatments conditionally independent” prior to performing causal inference with the aforementioned latent variables being used in place of the multi-cause unobserved confounders. To further ensure that the factor model is able to estimate the distribution of the assigned causes, “a validation set of subjects were considered in order to compare the similarity of the two test statistics.” This overcomes the problem of having to ensure that all the confounders are observed, which may lead to biased results otherwise. (Bica et al., 2020 )

Deep Sequential Weighting—It is used for estimating individual treatment effects with time-varying confounders by using a deep recurrent weighting neural network for inferring the hidden confounders using a combination of the current treatment assignments and historical information. The learned representations of hidden confounders combined with current observed data are then utilized for obtaining potential outcome and treatment predictions. For re-weighting the population, the time-varying inverse probabilities of treatment are computed. (Liu et al., 2020 )

For their own study, Kuromiya et al. ( 2020 ) first considered ITS and Prophet as possible approaches. They found that ITS had weak predictive power and limited flexibility. Prophet was better than ITS at predicting future values. In determining the impact of an event, though, Prophet was more difficult to interpret. They therefore decided to use a method called CausalImpact instead. As this was the study that we emulated, we used CausalImpact as well. We were not able to consider using CausalTransfer nor any of the neural network/deep learning methods.

Prior Studies using CausalImpact analysis

CausalImpact is a specific type of causal inference that enables researchers to estimate the impact of an intervention such as an ad campaign on an outcome variable such as additional clicks (Brodersen, 2014 ; Brodersen, et al., 2015 ). Given time series data, we first identify predictor variables, the outcome variable, and the pre- and post-intervention time segments. CausalImpact uses the pre-intervention data to model the relationship between the predictor variables and the outcome variable. It then uses the model to estimate the post-intervention counterfactual. The impact of the intervention is the difference between the counterfactual and the observed post-intervention data. While many algorithms may be used to model the counterfactual, CausalImpact made use of Bayesian structural time series models, explained in detail in (Brodersen, et al., 2015 ). The CausalImpact R package (Brodersen, 2014 ; Brodersen, et al., 2015 ) is publicly available at http://google.github.io/CausalImpact/CausalImpact.html .

CausalImpact was created within a commercial context and was intended for use on marketing data and clickstream traffic (Brodersen, 2014 ). Since its release in 2014, the method has also been used to model the effects of product modularity on bus manufacturing (Piran et al., 2017 ), US cyber policies on cyberattacks (Kumar et al., 2016 ), Arab uprisings and tourism (Perles-Ribes et al., 2018 ), and the performance of app store releases (Martin, 2016 ).

In 2020, Kuromiya and colleagues applied CausalImpact to estimate the effects of school closures on student use of the LMS Moodle and the electronic book reader BookRoll (Kuromiya et al., 2020 ). They performed this analysis for all courses in aggregate and for one specific English course. In their analysis, they found that student traffic in Moodle and BookRoll increased significantly during the COVID-19 pandemic. For all courses in aggregate, Moodle traffic increased by 163%, while BookRoll traffic increased by 77%. With the English course, Moodle traffic increased by 2227%, while BookRoll traffic increased by 875%. Note that Kuromiya and colleagues use the term “intervention” to refer to school closures rather than a new teaching strategy. They therefore expanded the definition of “intervention” to include external events that may affect a system, rather than deliberate actions from researchers, educators, or other persons that are intended to influence how the system behaves. In this study, we use this expanded definition of “intervention” to refer to the typhoons that affected online learning.

In 2021, Lagmay and Rodrigo began the analysis of Typhoons Goni and Vamco’s effects on student participation in online classes and published initial results at an international conference (Lagmay & Rodrigo, 2021 ). While this paper drew inspiration from Kuromiya et al. ( 2020 ), it differed in its choice of predictor variables. Lagmay and Rodrigo ( 2021 ) made use of teacher and non-editing teacher activity to predict student activity. In contrast, Kuromiya et al. ( 2020 ; personal communications, 26 January 2021) used number of logs per day as both the input variable and the outcome variable.

Lagmay and Rodrigo ( 2021 ) analyzed Moodle activity from September 9, 2020, to January 9, 2021. The pre-intervention period was defined as the pre-typhoon period from September 9, 2020, to October 28, 2020. The intervention period were the days disrupted by the typhoon, October 29 to November 13. Finally, the post-intervention period was November 14 to December 23, the period after the typhoon to just before the Christmas break. The paper found a statistically significant decrease in all LMS activity but a non-statistically significant difference in activities related to assessment. The paper we present here expands the Lagmay and Rodrigo ( 2021 ) paper by experimenting with the time periods.

While much educational research makes use of causal inference in general, as of the time of this writing, the works of Kuromiya et al. ( 2020 ) and Lagmay and Rodrigo ( 2021 ) were the only applications of CausalImpact on educational data that our survey of the literature could find.

The dataset was composed of a time series of log data from the Moodle of a privately owned university in Metro Manila, Philippines. Prior to the study, the researchers conferred with the University Data Protection Office and the University Counsel to determine whether we needed to seek informed consent from faculty and students to access their Moodle data. Since the data that we received were anonymized and because we did not have the ability to re-identify the same, there was no need to seek informed consent from the Moodle users (J. Jacob, personal communication, 25 September 2020; P. Sison-Arroyo, personal communication, 25 September 2020). Furthermore, the University Research Ethics Office determined that our research protocol was considered exempt from institutional ethics review because it was research conducted in educational settings involving normal educational practices, and that the information was processed such that participants could not be identified (L. Alampay, personal communication, 11 October 2020).

We collected data from 11,736 students, 925 teachers, and 38 non-editing teachers beginning September 9, 2020, and ending on January 9, 2021. The students were undergraduate and graduate students. Undergraduate students were from 18 to 22 years old, while graduate students were 23 and older. Students generally came from middle- to upper-class families. Teachers had at least a bachelor’s degree in the subject area that they were teaching. Most had master’s degrees or higher. Both students and teachers were a mix of males and females, though the exact distribution was not included in the Moodle data.

This time period of data collection represented two distinct academic terms: the first quarter (September 9 to October 24) and second quarter (October 28 to January 9). The dataset contained a total of 2,641,461 logs from 12,699 users. Each transaction was composed of the complete set of the following columns available from Moodle:

Time—timestamp of the of the action, up to the minute.

User ID—numerical identifier (ID) of the user performing the action.

Affected user—numerical identifier of the user affected by the action; When Teacher T sends a notification to Student S, the User ID would be that of Teacher T whereas the Affected user would be Student S.

Event context—teacher-given name of the module or activity within which the action took place, e.g., “Classroom Exercise 1 Module 1.”

Component—one of 43 Moodle-defined categories under which various events take place, e.g., Quiz.

Event name—one of 244 Moodle-defined names for actions that can be performed by the user, e.g., Quiz attempt viewed.

Description—narrative description of the action performed by the user, e.g., The user with id '1603' has viewed the attempt with id '20202' belonging to the user with id “1603” for the quiz with course module id “18804.”

Origin—The method used to access Moodle (examples: web, cli (Client), etc.).

IP address—If Moodle is accessed via the web, this gives the originating IP address (this was anonymized or deleted to ensure data privacy concerns).

The users of Moodle fell into three categories: teachers , non-editing teachers (e.g., a teaching assistant; non-editing teachers may view and grade work but may not edit or delete course content), and students . Because the logs did not include the user category, the university’s systems administrators provided the researchers with each user’s type.

We used transaction log volume, i.e., counts, as the indicator of participation. A transaction is defined as any interaction with Moodle. Each time a student performs an action such as accessing course materials or answering a quiz within Moodle, that action is logged as a transaction. The more the student works within Moodle, the more transaction Moodle logs for that student. While we were interested in broad types of transactions such as quizzes, we did not examine the actual content of course activities and resources. We did not read lectures, discussion postings, exams, quizzes, etc. To answer our research questions, an examination of transaction categories and volumes was sufficient.

Data preprocessing

The raw data consisted of 3 files of User IDs and User Types (each file representing a user type), and one transaction log file for each of the 123 days of the academic term under study. To preprocess the data, we first merged the list of User IDs and User Types with the transaction logs. We eliminated identifying features such as IP addresses, user full names, and ID numbers. We also had to parse and separate the Time column into separate Date and Time features. The log file was then aggregated according to the Date, User Type, and Component, and the rows that fall under each category were counted. All preprocessed files were then appended to a single file of transactions.

The second phase of the data preprocessing procedure, just prior to the CausalImpact analysis, was to normalize the data (See Table 1 ). We first aggregated the data frame according to User Type and Component columns (305 for 2020-10-10 and 1133 for 2020-12-23). We took the maximum possible Total for each group across all dates (6146). Then, the items in the Total column were divided by their respective maximum possible value according to the User Type and Component, normalizing the data between 0 and 1 for each User Type and Component (0.05 and 0.18).

We then decided to model three of the top ten most frequently occurring components overall: System, Quiz, and Assignment which, together, represented over 88% of all transactions (See Table 2 ). System refers to all actions related to communication and course management. Quizzes in Moodle are activities that are completed online and are often automatically graded. Assignment in Moodle is usually file uploads of work completed outside of the LMS.

CausalImpact analysis

We performed a CausalImpact analysis for four outcome variables: overall student LMS activity, the System component, the Assignment component, and the Quiz component. In this section, we discuss the analysis in three sections: predictor variable selection, time period definition, and CausalImpact results.

Predictor variable selection

We opted to use teacher and non-editing teacher transactions as predictor variables. Our theoretical grounding for this choice is the teacher expectancy effect (TEE), also known as the Pygmalion Effect. The Pygmalion Effect stems from research on how interpersonal expectations shape reality (Szumski & Karwowski, 2019 ). It is a form of self-fulfilling prophecy, asserting that teacher expectations have an impact on students’ academic progress. Through verbal and non-verbal behaviors, teachers signal their expectations to students about how the students will (not should) behave or how they will succeed or fail academically (Niari et al., 2016 ). Students then enact the behaviors or achievement levels that meet teachers’ expectations. Pygmalion effects have been observed at the individual and class level for both achievement outcomes and self-concept (see Friedrich et al., 2015 ; Szumski & Karwowski, 2019 ). These effects have been shown to persist over time (see Szumski & Karwowski, 2019 ). On this basis, we speculate that what teachers signal as their expectations for the online classes will serve as cues to the student about what they will deliver in order to pass the course.

Since teacher and non-editing teacher transactions were categorized under various components, it was necessary to determine which of these components were most predictive. We used Dynamic Time Warping (DTW) to arrive at a parsimonious set of predictor variables. As explained in (Larsen, 2021 ), the usual approach to finding the relationship between a predictor and a response variable in time series data is to use the Euclidean distance. However, this penalizes instances where the relationships between data have shifted. DTW finds the distance along the warping curve, as opposed to the raw data, to arrive at the best alignment between two time series. We used the MarketMatching R implementation of the DTW algorithm (Larsen, 2021 ). It should be noted, however, that MarketMatching will only work on predictor variables with a complete set of values and with a variance or standard deviation not equal to 0. To guarantee this, we trimmed the dataset to the top 10 most frequently used components across users. The result of this algorithm was a set of predictor variables with the closest relationship with the response variable (See Table 3 ).

Time period definition

The definitions of the pre- and post-intervention periods required some consideration. As mentioned in Sect.  2 , we collected data from the first quarter (September 9 to October 24) and second quarter (October 28 to January 9) of the academic year. The start of the second quarter was immediately disrupted by Typhoons Goni and Vamco. This led the university to suspend second quarter classes from November 16–21. The university mandated asynchronous-only classes from November 23–28 and resumed synchronous classes, if teachers chose to hold them, from November 29 onward (Vilches, 2020 ). Furthermore, the second quarter included a Christmas break from December 24 to January 3. To factor in the possible impacts of the class suspension and the Christmas break, we decided to run CausalImpact on four different time periods (See Fig.  1 ). The pre-intervention period was from September 9 to October 28, the days before the typhoons entered the Philippine Area of Responsibility (PAR). We included October 25 to 27, the period in-between the quarters, because it was during this time that teachers began contacting students to send them links to the online classroom where they would meet on the first meeting day. The intervention period was the period in which the two typhoons struck. We considered two possible endings to this period: November 13, the day Typhoon Vamco left the PAR, and November 21, the last day of the post-typhoon break. The post-intervention period followed and, like the intervention period, had two possible end dates: December 23, before the Christmas break, and January 9. Hence, we created four time periods:

Intervention period that does not include the post-typhoon break; post-intervention period that includes the Christmas break (NB-WC)

Intervention period that does not include the post-typhoon break; post-intervention period that does not include the Christmas break (NB-NC)

Intervention period that includes the post-typhoon break; post-intervention period that includes the Christmas break (WB-WC)

Intervention period that includes the post-typhoon break; post-intervention period that does not include the Christmas break (WB-NC)

Time period definitions

Note that we were working with the same dataset reported in Lagmay and Rodrigo ( 2021 ). In this current paper, though, the end date of the intervention period and the start date of the post-intervention periods in time periods WB-WC and WB-NC are different. The dates in Fig.  1 are consistent with the university memo regarding the post-typhoon period (Vilches, 2020 ). These same time period definitions in Lagmay and Rodrigo ( 2021 ) were off by 2 days.

CausalImpact results

Tables 4 and 5 show the results of the analysis for each of the time periods.

All LMS Activity

During time period NB-WC, all LMS activity decreased significantly ( p  = 0.02). The response variable had an average value of 0.17. The counterfactual prediction was 0.21. The typhoons therefore had an estimated effect of − 0.041 with a 95% confidence interval of [− 0.077, − 0.0063]. When the data points during the intervention period are summed, the response variable had an overall value of 9.61. The counterfactual prediction was 11.98 with a 95% confidence interval of [9.97, 14.10]. This means that overall student participation decreased by − 20% with a 95% confidence interval of [− 37%, − 3%].

Figure  2 a shows the CausalImpact graph of all LMS activity for time period NB-WC. Each unit on the x -axis represents one day in the time period. The topmost graph labeled “original” shows a solid line representing the actual observed data, i.e., the number of transactions per day. The broken line represents the prediction. The light blue band represents the confidence interval of the prediction. The middle graph labeled “pointwise” shows the difference between the predicted number of transactions and the actual number of transactions per day. If the predicted number of transactions for day 1 was 100 and the actual number of transactions was 80, the pointwise difference was 20. Finally, the cumulative graph at the bottom shows the accumulated difference between the predicted number of transactions and the actual number of transactions. If the pointwise difference on day 2 was 10, the accumulated difference of days 1 and 2 is 30. If the pointwise difference on day 3 was 12, the accumulated difference of days 1, 2, and 3 is 42. The gap in the pointwise and cumulative graphs is the intervention period. There is no accumulated difference during the pre-intervention period. The differences are accumulated post-intervention. Note that the cumulative graph shows a downward trend during the post-intervention period and that there was indeed a slump in the week or so following the typhoons.

CausalImpact graphs for all LMS activity

During time period NB-NC, all LMS activity also decreased significantly ( p  = 0.01). Student participation had an average value of 0.18. The counterfactual prediction was 0.23. The typhoons therefore had an estimated effect of − 0.045 with a 95% interval of [− 0.082, − 0.010]. When the data points during the intervention period are summed, the response variable had an overall value of 7.41. The counterfactual prediction was 9.25 with a 95% confidence interval of [7.83, 10.77]. Like time period NB-WC, student participation decreased by − 20% with a 95% confidence interval of [− 36%, − 5%]. Figure  2 b shows the CausalImpact graph for time period NB-NC.

The results for all LMS activity during time periods WB-WC and WB-NC were insignificant. Time period WB-WC yielded a p value of 0.044, while time period WB-NC yielded a p value of 0.033. However, in both cases, the signs of the 95% CI fluctuated, which means that even if the p value implies significance, the results cannot be meaningfully interpreted. Since time periods WB-WC and WB-NC included the class suspension, it is possible that the definition of the intervention period was too long and the effect of the typhoons had already worn off. Figure  2 c, d shows a visualization of this scenario. We trim off the slump that follows immediately after the typhoons. Although the cumulative graph still follows a decreasing trajectory, the difference between the predicted and actual data is no longer significant. Note that the graph shape does not change, regardless of time period. What changes is the size of the intervention period from the end of October to around the middle of November and the length of the graph’s tail.

During time periods NB-WC (Fig.  3 a) and WB-WC (Fig.  3 c), System activity decreased, but not significantly. Although the p value of time period NB-WC was 0.03 and student participation showed a decrease of − 26%, the 95% interval of this percentage was [− 52%, + 1%]. The p value of time period WB-WC was 0.04 and the response variable showed a decrease of − 25% with a 95% interval of [− 52%, + 5%]. These fluctuations of the sign during the post-periods of the two time periods meant that the effect is not significant and cannot be meaningfully interpreted (Coqueret & Guida, 2020 ).

CausalImpact graphs for System component

System activity during period NB-NC (Fig.  3 b) significantly decreased ( p  = 0.01). Student participation averaged 0.11 as opposed to a counterfactual prediction of 0.15 with a 95% interval of [0.12, 0.19]. The effect of the typhoons is estimated at − 0.046 with a 95% interval of [− 0.080, − 0.013]. The sum of student participation data points during the post-intervention period was 4.43 in contrast to a predicted 6.30 with a 95% interval of [4.96, 7.71].

The results of time period WB-NC (Fig.  3 d) were also statistically significant ( p  = 0.01). Student participation averaged 0.12 as opposed to the predicted 0.17 with a 95% interval of [0.13, 0.20]. The effect of the typhoons was therefore estimated at − 0.046 with a 95% interval of [− 0.081, − 0.0072]. The sum of the student participation data points was 4.04 in contrast to a predicted 5.57 with a 95% interval of [4.28, 6.70].

Assignments

The effects of the typhoons on student behavior on Assignments were not significant across any of the time periods. p values were 0.14, 0.348, and 0.195 for time periods NB-WC, WB-WC, and WB-NC, respectively. Although time period NB-NC had a p value of 0.04, student participation’s sign fluctuated. It showed a decrease of − 18% with a 95% interval of [− 37%, + 2%]. This meant that the result could not be meaningfully interpreted.

The effects of the typhoons on student behavior on Quizzes were not statistically significant for any of the four time periods. p values were 0.29, 0.39, 0.45, and 0.14 for time periods NB-WC, NB-NC, WB-WC, and WB-NC, respectively.

The purpose of this paper was to determine (1) the extent to which extreme weather affected student participation during online classes and (2) whether and at what point they were able to return to pre-typhoon levels of participation. It extends the earlier work by Lagmay and Rodrigo ( 2021 ) in several ways: The earlier work only included one time period definition, which we labelled in this paper as NB-NC, while this paper experiments with four different time period definitions. Furthermore, Lagmay and Rodrigo ( 2021 ) limited the discussion of the findings to the significance of the decrease, the standard deviation, and the confidence interval. This paper also discusses the absolute and relative effects which were not discussed in the prior paper. Despite these differences in scope, the findings were consistent: Student participation decreased as a whole but those certain components of participation remained at pre-typhoon levels. These findings need to be unpacked for greater nuance.

While student participation as a whole decreased, we found that the significance of the decrease varied, first depending on the definition of the intervention period and second depending on component. When the post-intervention time period excluded the Christmas break (time periods NB-NC and WB-NC), post-typhoon participation as measured in System component significantly decreased, while when the intervention time period excluded the additional week of post-Vamco class suspensions (time periods NB-WC and NB-NC), all LMS Logs significantly decreased.

What was most interesting was that participation in the Assignments and Quizzes components was not significantly different from their predicted behavior, regardless of time period. Because we did not examine the details of actual learning design, course activities, or relative weights of assessments, these findings suggest that students continued to comply with academic assessments as assignments and quizzes make measurable contributions to their grades. System behavior, on the other hand, refers to actions such as checking the course for announcements. These activities are generally not graded. This implies that students were able to continue complying with academic requirements despite the setbacks brought on by the typhoons.

The findings from this study are consistent with findings from prior work on the negative effects of interruptions on academic outcomes. Short-term, small-scale interruptions from social media use, family and friends, sleepiness, and computer malfunctions can derail concentration and throw learning off-course (Zhang et al., 2022 ; Zureick et al., 2018 ). Hence, students who experience these interruptions tend to have lower assessment scores than peers who do not. Larger-scale interruptions such as extreme weather and other natural disasters have adverse long-term effects on educational outcomes, especially among marginalized groups (Bernabe et al., 2021 ; Groppo & Kraehnert, 2017 ; Marchetta et al., 2018 ). It is therefore unsurprising that overall student participation dropped following Typhoons Goni and Vamco.

That students were able to continue engaging with Assignments and Quizzes calls for further reflection. How did students still have the capacity to work on assessments when it seemed most logical, under the circumstances, for them to deprioritize their studies in general? The work of Lai et al. ( 2019 ) offers some insight in this regard. They found two trajectories of school recovery after a disaster: low-interrupted and high-stable. The low-interrupted trajectory referred to school performance levels that dropped following a disaster, while the high-stable trajectory referred to relatively unchanged performance levels. Schools that had higher levels of attendance in general were more likely to have high-stable trajectories, while schools with a high percentage of economically disadvantaged students were more likely to have low-interrupted trajectories. Sustained engagement with assessments despite the typhoons implies that the university examined in this study had a high-stable trajectory and that its students, by and large, were not economically disadvantaged.

There are solutions available to mitigate the effects of inclement weather. Herrera-Almanza and Cas ( 2017 ) studied the long-term academic outcomes of Filipino public school students whose schools were built as part of the Typhoon-Resistant School Building Program of the Philippine government and the Government of Japan. The project made use of Japanese pre-fabrication construction methods and materials to build more structures that were less prone to storm damage, increasing post-typhoon access to schools. The researchers found that students from these beneficiary schools accumulated more years of schooling and were more likely to complete secondary school. Programs such as this illustrate ways in which policy makers can increase the resilience of economically disadvantaged communities.

Limitations

The generalizability of these findings is subject to at least five limitations. Firstly, CausalImpact analysis requires that the predictor variables should not be affected by the same intervention as the response variable (Brodersen & Hauser, 2014 –2017). In this case, it was the likely case that the teachers and non-editing teachers were affected by the typhoons, just as their students were. To this point, we offer two counterarguments: First, we used DTW to find the teacher and non-editing teacher features that were most predictive of student behaviors. The algorithm eliminated the features with no predictive power, leaving only those that could give us a reasonable estimate of student behavior. Multicollinearity was not an issue of concern because CausalImpact’s underlying model “uses spike and slab priors to combat collinearity” (K. Larsen, personal communications, June 29, 2021). The methodology is provided in (Larsen, 2016 ).

Second, we return to our theoretical framework regarding the Pygmalion Effect (Szumski & Karwowski, 2019 ). Teacher expectations have been shown to affect student behavior, achievement, and self-concept. Since teachers continued to provide learning materials and assessments after the typhoons and throughout the second quarter, this may have signaled to the students that they were still expected to fulfill their academic obligations.

Our second limitation has to do with the population from which the data were taken. Prior research cited in the “ Effects of extreme weather on academic achievement ” section showed that extreme weather has detrimental, long-term effects on student achievement, and yet these students seemed to have flourished despite these two typhoons. One possible explanation for this is that the students in this sample were among the best in the country. They generally came from well-to-do socioeconomic backgrounds, and their families had the economic stability to withstand the typhoon’s shocks. Their resilience may not be indicative of the resilience of the Philippines or any developing country as a whole. It may, at best, serve as validation of prior findings that the impact of extreme weather varies along socioeconomic lines. Those who are more financially able will survive, possibly flourish. While it would have been revelatory to perform this analysis on data from an LMS used by less economically fortunate people, such data were not available.

Third, the university had two LMSs working in parallel, Moodle and Canvas. We were only able to capture Moodle data for this study, and the classes using the Moodle server were generally the Computer Science and Management Information Systems classes. The students were therefore technology-savvy and adept at online modes of communication. Students from other courses might have encountered greater challenges.

Fourth, the data captured here represent LMS participation but not other important outcomes such as assessment results, the quality of the educational experience, or the mental health consequences of online learning coupled with severe weather. While students and faculty evidently powered through their requirements, it would be best to triangulate these results with findings and observations from other constituency checks, for a more complete reading of our community.

Finally, as mentioned in the “ Time series analysis methods ” section, we were not able to consider using CausalTransfer, a more updated version of CausalImpact, nor any of the neural network or deep learning approaches. Future studies may consider experimenting with these other approaches to determine if they yield better results.

Despite these limitations, this paper contributes to technology-enhanced education research and practice. For education researchers, this paper adds to the literature by applying CausalImpact analysis on LMS data to determine the effects of severe weather on students. It contributes to what is quantitatively known about how Philippine students cope with online learning. In the context of severe weather, quantitative research on this subject is still scarce.

This paper serves also as a possible model for researchers who wish to determine the effects of an intervention on a system. They can consider the use of CausalImpact as a possible approach if they have sufficient pre-intervention data for CausalImpact to draw an accurate model, a clearly defined intervention period, and sufficient post-intervention data to serve as a comparison. Future researchers should also be careful with their choice of predictor variables as the behavior of predictor variables should not be affected by the intervention.

For education practitioners, this paper provides evidence that schools and their students can be resilient, and that academic continuity is possible even in the face of difficult circumstances. However, evidence of resilience for some students should not be interpreted as resilience for all. Markers of resilience such as hope and confidence must be grounded in reality (Mahdiani & Ungar, 2021 ). Resilience should not be used as an excuse for social inequalities and should not shift the responsibility to survive and thrive on people who may lack the power or resources to do so. As extreme weather events that are characteristic to the Philippines, policy makers have to invest in typhoon-resistant infrastructure (Herrera-Almanza & Cas, 2017 ) and practitioners will have to provide marginalized students with more support in order to achieve desired educational outcomes.

Availability of data and materials

The dataset(s) supporting the conclusions of this article are available in the RPTEL_CausalImpact_Lagmay_Rodrigo repository, https://github.com/KielLagmay/RPTEL_CausalImpact_Lagmay_Rodrigo .

Project name: RPTEL_CausalImpact_Lagmay_Rodrigo.

Project home page: https://github.com/KielLagmay/RPTEL_CausalImpact_Lagmay_Rodrigo .

Archived version: N/A.

Operating system(s): Windows 10 or later (with PowerShell), Windows 8.1 or later (with CMD), macOS 10.13 High Sierra or later (with BASH), macOS 10.14 Mojave or later (with ZSH), or Ubuntu 20.04 or later (with BASH) .

Programming language: R, Python, Jupyter Notebook, Shell, PowerShell, and Batch.

Other requirements: Anaconda with Python 3.6 or higher + Pyro5, pandas, import_ipynb, netifaces, and dateutil; R with CausalImpact, MarketMatching, dplyr, ggplot2, zoo, tidyr, reshape2, mctest, ppcor, and fsMTS libraries.

License: GPL-3.0.

Any restrictions to use by non-academics: For data privacy reasons, please send a request to [email protected] for the actual log files, user type files, and aggregated files.

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Acknowledgements

We thank Hiroyuki Kuromiya and Hiroaki Ogata of Kyoto University for their advice. Finally, we thank the Ateneo Laboratory for the Learning Sciences for its constant support.

Funding for this project was provided by Ateneo Research Institute for Science and Engineering (ARISE) of Ateneo de Manila University through the grant entitled: Analysis of Student and Faculty Behavior within Canvas and Moodle.

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EADL was primarily responsible for the quantitative analysis of the data and contributed to the writing of the paper. MMTR conceptualized the study, scaffolded the analysis of the data, and contributed to the writing of the paper. All authors read and approved the final manuscript.

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Lagmay, E.A.D., Rodrigo, M.M.T. The impact of extreme weather on student online learning participation. RPTEL 17 , 26 (2022). https://doi.org/10.1186/s41039-022-00201-2

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Perceptions and Lived Experiences of Selected Students in a Private University in Manila on Online Classes During this Time of Pandemic THE PROBLEM AND ITS BACKGROUND

Knowing that education has been inaccessible and ineffective to some students in the Philippines, it is crucial to know the efficacy and success of the new learning setup during this time of the pandemic to assess if these new modalities are beneficial to the students or not. This paper reviewed and studied the various perceptions and lived experiences of selected students in a private university in Manila regarding online classes amidst the pandemic. Using a phenomenological approach, the researchers gathered data by interviewing 15 students who are attending online classes. Findings and results were drawn on the themes created by the researchers. It can be seen that majority of the students find online classes ineffective and only cater the privileged. Furthermore, lessons and discussions are difficult to digest using the current mode of learning. These findings signify a need to improve the mode of learning in order to provide a quality and effective education to students despite the current crisis faced by the country. Keywords: distance learning, online learning, online classes

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Assessing the effect of the COVID-19 pandemic, shift to online learning, and social media use on the mental health of college students in the Philippines: A mixed-method study protocol

Leonard thomas s. lim.

1 College of Medicine, University of the Philippines, Manila, Philippines

Zypher Jude G. Regencia

2 Department of Clinical Epidemiology, College of Medicine, University of the Philippines, Manila, Philippines

3 Institute of Clinical Epidemiology, National Institutes of Health, University of the Philippines, Manila, Philippines

J. Rem C. Dela Cruz

Frances dominique v. ho, marcela s. rodolfo, josefina ly-uson.

4 Department of Psychiatry, College of Medicine, University of the Philippines, Manila, Philippines

Emmanuel S. Baja

Associated data.

Introduction

The COVID-19 pandemic declared by the WHO has affected many countries rendering everyday lives halted. In the Philippines, the lockdown quarantine protocols have shifted the traditional college classes to online. The abrupt transition to online classes may bring psychological effects to college students due to continuous isolation and lack of interaction with fellow students and teachers. Our study aims to assess Filipino college students’ mental health status and to estimate the effect of the COVID-19 pandemic, the shift to online learning, and social media use on mental health. In addition, facilitators or stressors that modified the mental health status of the college students during the COVID-19 pandemic, quarantine, and subsequent shift to online learning will be investigated.

Methods and analysis

Mixed-method study design will be used, which will involve: (1) an online survey to 2,100 college students across the Philippines; and (2) randomly selected 20–40 key informant interviews (KIIs). Online self-administered questionnaire (SAQ) including Depression, Anxiety, and Stress Scale (DASS-21) and Brief-COPE will be used. Moreover, socio-demographic factors, social media usage, shift to online learning factors, family history of mental health and COVID-19, and other factors that could affect mental health will also be included in the SAQ. KIIs will explore factors affecting the student’s mental health, behaviors, coping mechanism, current stressors, and other emotional reactions to these stressors. Associations between mental health outcomes and possible risk factors will be estimated using generalized linear models, while a thematic approach will be made for the findings from the KIIs. Results of the study will then be triangulated and summarized.

Ethics and dissemination

Our study has been approved by the University of the Philippines Manila Research Ethics Board (UPMREB 2021-099-01). The results will be actively disseminated through conference presentations, peer-reviewed journals, social media, print and broadcast media, and various stakeholder activities.

The World Health Organization (WHO) declared the Coronavirus 2019 (COVID-19) outbreak as a global pandemic, and the Philippines is one of the 213 countries affected by the disease [ 1 ]. To reduce the virus’s transmission, the President imposed an enhanced community quarantine in Luzon, the country’s northern and most populous island, on March 16, 2020. This lockdown manifested as curfews, checkpoints, travel restrictions, and suspension of business and school activities [ 2 ]. However, as the virus is yet to be curbed, varying quarantine restrictions are implemented across the country. In addition, schools have shifted to online learning, despite financial and psychological concerns [ 3 ].

Previous outbreaks such as the swine flu crisis adversely influenced the well-being of affected populations, causing them to develop emotional problems and raising the importance of integrating mental health into medical preparedness for similar disasters [ 4 ]. In one study conducted on university students during the swine flu pandemic in 2009, 45% were worried about personally or a family member contracting swine flu, while 10.7% were panicking, feeling depressed, or emotionally disturbed. This study suggests that preventive measures to alleviate distress through health education and promotion are warranted [ 5 ].

During the COVID-19 pandemic, researchers worldwide have been churning out studies on its psychological effects on different populations [ 6 – 9 ]. The indirect effects of COVID-19, such as quarantine measures, the infection of family and friends, and the death of loved ones, could worsen the overall mental wellbeing of individuals [ 6 ]. Studies from 2020 to 2021 link the pandemic to emotional disturbances among those in quarantine, even going as far as giving vulnerable populations the inclination to commit suicide [ 7 , 8 ], persistent effect on mood and wellness [ 9 ], and depression and anxiety [ 10 ].

In the Philippines, a survey of 1,879 respondents measuring the psychological effects of COVID-19 during its early phase in 2020 was released. Results showed that one-fourth of respondents reported moderate-to-severe anxiety, while one-sixth reported moderate-to-severe depression [ 11 ]. In addition, other local studies in 2020 examined the mental health of frontline workers such as nurses and physicians—placing emphasis on the importance of psychological support in minimizing anxiety [ 12 , 13 ].

Since the first wave of the pandemic in 2020, risk factors that could affect specific populations’ psychological well-being have been studied [ 14 , 15 ]. A cohort study on 1,773 COVID-19 hospitalized patients in 2021 found that survivors were mainly troubled with fatigue, muscle weakness, sleep difficulties, and depression or anxiety [ 16 ]. Their results usually associate the crisis with fear, anxiety, depression, reduced sleep quality, and distress among the general population.

Moreover, the pandemic also exacerbated the condition of people with pre-existing psychiatric disorders, especially patients that live in high COVID-19 prevalence areas [ 17 ]. People suffering from mood and substance use disorders that have been infected with COVID-19 showed higher suicide risks [ 7 , 18 ]. Furthermore, a study in 2020 cited the following factors contributing to increased suicide risk: social isolation, fear of contagion, anxiety, uncertainty, chronic stress, and economic difficulties [ 19 ].

Globally, multiple studies have shown that mental health disorders among university student populations are prevalent [ 13 , 20 – 22 ]. In a 2007 survey of 2,843 undergraduate and graduate students at a large midwestern public university in the United States, the estimated prevalence of any depressive or anxiety disorder was 15.6% and 13.0% for undergraduate and graduate students, respectively [ 20 ]. Meanwhile, in a 2013 study of 506 students from 4 public universities in Malaysia, 27.5% and 9.7% had moderate and severe or extremely severe depression, respectively; 34% and 29% had moderate and severe or extremely severe anxiety, respectively [ 21 ]. In China, a 2016 meta-analysis aiming to establish the national prevalence of depression among university students analyzed 39 studies from 1995 to 2015; the meta-analysis found that the overall prevalence of depression was 23.8% across all studies that included 32,694 Chinese university students [ 23 ].

A college student’s mental status may be significantly affected by the successful fulfillment of a student’s role. A 2013 study found that acceptable teaching methods can enhance students’ satisfaction and academic performance, both linked to their mental health [ 24 ]. However, online learning poses multiple challenges to these methods [ 3 ]. Furthermore, a 2020 study found that students’ mental status is affected by their social support systems, which, in turn, may be jeopardized by the COVID-19 pandemic and the physical limitations it has imposed. Support accessible to a student through social ties to other individuals, groups, and the greater community is a form of social support; university students may draw social support from family, friends, classmates, teachers, and a significant other [ 25 , 26 ]. Among individuals undergoing social isolation and distancing during the COVID-19 pandemic in 2020, social support has been found to be inversely related to depression, anxiety, irritability, sleep quality, and loneliness, with higher levels of social support reducing the risk of depression and improving sleep quality [ 27 ]. Lastly, it has been shown in a 2020 study that social support builds resilience, a protective factor against depression, anxiety, and stress [ 28 ]. Therefore, given the protective effects of social support on psychological health, a supportive environment should be maintained in the classroom. Online learning must be perceived as an inclusive community and a safe space for peer-to-peer interactions [ 29 ]. This is echoed in another study in 2019 on depressed students who narrated their need to see themselves reflected on others [ 30 ]. Whether or not online learning currently implemented has successfully transitioned remains to be seen.

The effect of social media on students’ mental health has been a topic of interest even before the pandemic [ 31 , 32 ]. A systematic review published in 2020 found that social media use is responsible for aggravating mental health problems and that prominent risk factors for depression and anxiety include time spent, activity, and addiction to social media [ 31 ]. Another systematic review published in 2016 argues that the nature of online social networking use may be more important in influencing the symptoms of depression than the duration or frequency of the engagement—suggesting that social rumination and comparison are likely to be candidate mediators in the relationship between depression and social media [ 33 ]. However, their findings also suggest that the relationship between depression and online social networking is complex and necessitates further research to determine the impact of moderators and mediators that underly the positive and negative impact of online social networking on wellbeing [ 33 ].

Despite existing studies already painting a picture of the psychological effects of COVID-19 in the Philippines, to our knowledge, there are still no local studies contextualized to college students living in different regions of the country. Therefore, it is crucial to elicit the reasons and risk factors for depression, stress, and anxiety and determine the potential impact that online learning and social media use may have on the mental health of the said population. In turn, the findings would allow the creation of more context-specific and regionalized interventions that can promote mental wellness during the COVID-19 pandemic.

Materials and methods

The study’s general objective is to assess the mental health status of college students and determine the different factors that influenced them during the COVID-19 pandemic. Specifically, it aims:

• To describe the study population’s characteristics, categorized by their mental health status, which includes depression, anxiety, and stress.
• To determine the prevalence and risk factors of depression, anxiety, and stress among college students during the COVID-19 pandemic, quarantine, and subsequent shift to online learning.
• To estimate the effect of social media use on depression, anxiety, stress, and coping strategies towards stress among college students and examine whether participant characteristics modified these associations.
• To estimate the effect of online learning shift on depression, anxiety, stress, and coping strategies towards stress among college students and examine whether participant characteristics modified these associations.
• To determine the facilitators or stressors among college students that modified their mental health status during the COVID-19 pandemic, quarantine, and subsequent shift to online learning.

Study design

A mixed-method study design will be used to address the study’s objectives, which will include Key Informant Interviews (KIIs) and an online survey. During the quarantine period of the COVID-19 pandemic in the Philippines from April to November 2021, the study shall occur with the population amid community quarantine and an abrupt transition to online classes. Since this is the Philippines’ first study that will look at the prevalence of depression, anxiety, and stress among college students during the COVID-19 pandemic, quarantine, and subsequent shift to online learning, the online survey will be utilized for the quantitative part of the study design. For the qualitative component of the study design, KIIs will determine facilitators or stressors among college students that modified their mental health status during the quarantine period.

Study population

The Red Cross Youth (RCY), one of the Philippine Red Cross’s significant services, is a network of youth volunteers that spans the entire country, having active members in Luzon, Visayas, and Mindanao. The group is clustered into different age ranges, with the College Red Cross Youth (18–25 years old) being the study’s population of interest. The RCY has over 26,060 students spread across 20 chapters located all over the country’s three major island groups. The RCY is heterogeneously composed, with some members classified as college students and some as out-of-school youth. Given their nationwide scope, disseminating information from the national to the local level is already in place; this is done primarily through email, social media platforms, and text blasts. The research team will leverage these platforms to distribute the online survey questionnaire.

In addition, the online survey will also be open to non-members of the RCY. It will be disseminated through social media and engagements with different university administrators in the country. Stratified random sampling will be done for the KIIs. The KII participants will be equally coming from the country’s four (4) primary areas: 5–10 each from the national capital region (NCR), Luzon, Visayas, and Mindanao, including members and non-members of the RCY.

Inclusion and exclusion criteria

The inclusion criteria for the online survey will include those who are 18–25 years old, currently enrolled in a university, can provide consent for the study, and are proficient in English or Filipino. The exclusion criteria will consist of those enrolled in graduate-level programs (e.g., MD, JD, Master’s, Doctorate), out-of-school youth, and those whose current curricula involve going on duty (e.g., MDs, nursing students, allied medical professions, etc.). The inclusion criteria for the KIIs will include online survey participants who are 18–25 years old, can provide consent for the study, are proficient in English or Filipino, and have access to the internet.

Sample size

A continuity correction method developed by Fleiss et al. (2013) was used to calculate the sample size needed [ 34 ]. For a two-sided confidence level of 95%, with 80% power and the least extreme odds ratio to be detected at 1.4, the computed sample size was 1890. With an adjustment for an estimated response rate of 90%, the total sample size needed for the study was 2,100. To achieve saturation for the qualitative part of the study, 20 to 40 participants will be randomly sampled for the KIIs using the respondents who participated in the online survey [ 35 ].

Study procedure

Self-Administered questionnaire

The study will involve creating, testing, and distributing a self-administered questionnaire (SAQ). All eligible study participants will answer the SAQ on socio-demographic factors such as age, sex, gender, sexual orientation, residence, household income, socioeconomic status, smoking status, family history of mental health, and COVID-19 sickness of immediate family members or friends. The two validated survey tools, Depression, Anxiety, and Stress Scale (DASS-21) and Brief-COPE, will be used for the mental health outcome assessment [ 36 – 39 ]. The DASS-21 will measure the negative emotional states of depression, anxiety, and stress [ 40 ], while the Brief-COPE will measure the students’ coping strategies [ 41 ].

For the exposure assessment of the students to social media and shift to online learning, the total time spent on social media (TSSM) per day will be ascertained by querying the participants to provide an estimated time spent daily on social media during and after their online classes. In addition, students will be asked to report their use of the eight commonly used social media sites identified at the start of the study. These sites include Facebook, Twitter, Instagram, LinkedIn, Pinterest, TikTok, YouTube, and social messaging sites Viber/WhatsApp and Facebook Messenger with response choices coded as "(1) never," "(2) less often," "(3) every few weeks," "(4) a few times a week," and “(5) daily” [ 42 – 44 ]. Furthermore, a global frequency score will be calculated by adding the response scores from the eight social media sites. The global frequency score will be used as an additional exposure marker of students to social media [ 45 ]. The shift to online learning will be assessed using questions that will determine the participants’ satisfaction with online learning. This assessment is comprised of 8 items in which participants will be asked to respond on a 5-point Likert scale ranging from ‘strongly disagree’ to ‘strongly agree.’

The online survey will be virtually distributed in English using the Qualtrics XM™ platform. Informed consent detailing the purpose, risks, benefits, methods, psychological referrals, and other ethical considerations will be included before the participants are allowed to answer the survey. Before administering the online survey, the SAQ shall undergo pilot testing among twenty (20) college students not involved with the study. It aims to measure total test-taking time, respondent satisfaction, and understandability of questions. The survey shall be edited according to the pilot test participant’s responses. Moreover, according to the Philippines’ Data Privacy Act, all the answers will be accessible and used only for research purposes.

Key informant interviews

The research team shall develop the KII concept note, focusing on the extraneous factors affecting the student’s mental health, behaviors, and coping mechanism. Some salient topics will include current stressors (e.g., personal, academic, social), emotional reactions to these stressors, and how they wish to receive support in response to these stressors. The KII will be facilitated by a certified psychologist/psychiatrist/social scientist and research assistants using various online video conferencing software such as Google Meet, Skype, or Zoom. All the KIIs will be recorded and transcribed for analysis. Furthermore, there will be a debriefing session post-KII to address the psychological needs of the participants. Fig 1 presents the diagrammatic flowchart of the study.

Data analyses

Quantitative data.

Descriptive statistics will be calculated, including the prevalence of mental health outcomes such as depression, anxiety, stress, and coping strategies. In addition, correlation coefficients will be estimated to assess the relations among the different mental health outcomes, covariates, and possible risk factors.

Associations between mental health outcomes and possible risk factors will be estimated using generalized linear models, a standard method for analyzing data in cross-sectional studies. Depending on how rare or common the mental health outcomes are, generalized linear models with either a Poisson distribution and log link function with a robust variance estimator or a Binomial distribution and logit link function will be used to estimate either the adjusted prevalence ratios (PRs) or odds ratios (ORs) with 95% confidence intervals (CIs), respectively [ 46 – 49 ]. Separate single-mental health outcome models will be evaluated, and the models will consider the general form:

where Y i will be the mental health outcome (depression, anxiety, stress, and coping strategy) status of subject i and covariates for subject i will be denoted by X 1i to X ri as the possible exposure risk factors (i.e., social media use and shift to online learning) and confounding factors (i.e., age, sex, gender, smoking status, family income, etc.). In addition, we will control for the covariates chosen a priori as potentially important predictors of mental health outcomes in all the models.

Several study characteristics as effect modifiers will also be assessed, including sex, gender, sexual orientation, family income, smoking status, family history of mental health, and Covid-19. We will include interaction terms between the dichotomized modifier variable and markers of social media use (total TSSM and global frequency score) and shift to online learning in the models. The significance of the interaction terms will be evaluated using the likelihood ratio test. All the regression analyses will be done in R ( http://www.r-project.org ). P values ≤ 0.05 will be considered statistically significant.

Qualitative data

After transcribing the interviews, the data transcripts will be analyzed using NVivo 1.4.1 software [ 50 ] by three research team members independently using the inductive logic approach in thematic analysis: familiarizing with the data, generating initial codes, searching for themes, reviewing the themes, defining and naming the themes, and producing the report [ 51 ]. Data familiarization will consist of reading and re-reading the data while noting initial ideas. Additionally, coding interesting features of the data will follow systematically across the entire dataset while collating data relevant to each code. Moreover, the open coding of the data will be performed to describe the data into concepts and themes, which will be further categorized to identify distinct concepts and themes [ 52 ].

The three researchers will discuss the results of their thematic analyses. They will compare and contrast the three analyses in order to come up with a thematic map. The final thematic map of the analysis will be generated after checking if the identified themes work in relation to the extracts and the entire dataset. In addition, the selection of clear, persuasive extract examples that will connect the analysis to the research question and literature will be reviewed before producing a scholarly report of the analysis. Additionally, the themes and sub-themes generated will be assessed and discussed in relevance to the study’s objectives. Furthermore, the gathering and analyzing of the data will continue until saturation is reached. Finally, pseudonyms will be used to present quotes from qualitative data.

Data triangulation

Data triangulation using the two different data sources will be conducted to examine the various aspects of the research and will be compared for convergence. This part of the analysis will require listing all the relevant topics or findings from each component of the study and considering where each method’s results converge, offer complementary information on the same issue, or appear to contradict each other. It is crucial to explicitly look for disagreements between findings from different data collection methods because exploration of any apparent inter-method discrepancy may lead to a better understanding of the research question [ 53 , 54 ].

Data management plan

The Project Leader will be responsible for overall quality assurance, with research associates and assistants undertaking specific activities to ensure quality control. Quality will be assured through routine monitoring by the Project Leader and periodic cross-checks against the protocols by the research assistants. Transcribed KIIs and the online survey questionnaire will be used for recording data for each participant in the study. The project leader will be responsible for ensuring the accuracy, completeness, legibility, and timeliness of the data captured in all the forms. Data captured from the online survey or KIIs should be consistent, clarified, and corrected. Each participant will have complete source documentation of records. Study staff will prepare appropriate source documents and make them available to the Project Leader upon request for review. In addition, study staff will extract all data collected in the KII notes or survey forms. These data will be secured and kept in a place accessible to the Project Leader. Data entry and cleaning will be conducted, and final data cleaning, data freezing, and data analysis will be performed. Key informant interviews will always involve two researchers. Where appropriate, quality control for the qualitative data collection will be assured through refresher KII training during research design workshops. The Project Leader will check through each transcript for consistency with agreed standards. Where translations are undertaken, the quality will be assured by one other researcher fluent in that language checking against the original recording or notes.

Ethics approval

The study shall abide by the Principles of the Declaration of Helsinki (2013). It will be conducted along with the Guidelines of the International Conference on Harmonization-Good Clinical Practice (ICH-GCP), E6 (R2), and other ICH-GCP 6 (as amended); National Ethical Guidelines for Health and Health-Related Research (NEGHHRR) of 2017. This protocol has been approved by the University of the Philippines Manila Research Ethics Board (UPMREB 2021-099-01 dated March 25, 2021).

The main concerns for ethics were consent, data privacy, and subject confidentiality. The risks, benefits, and conflicts of interest are discussed in this section from an ethical standpoint.

Recruitment

The participants will be recruited to answer the online SAQ voluntarily. The recruitment of participants for the KIIs will be chosen through stratified random sampling using a list of those who answered the online SAQ; this will minimize the risk of sampling bias. In addition, none of the participants in the study will have prior contact or association with the researchers. Moreover, power dynamics will not be contacted to recruit respondents. The research objectives, methods, risks, benefits, voluntary participation, withdrawal, and respondents’ rights will be discussed with the respondents in the consent form before KII.

Informed consent will be signified by the potential respondent ticking a box in the online informed consent form and the voluntary participation of the potential respondent to the study after a thorough discussion of the research details. The participant’s consent is voluntary and may be recanted by the participant any time s/he chooses.

Data privacy

All digital data will be stored in a cloud drive accessible only to the researchers. Subject confidentiality will be upheld through the assignment of control numbers and not requiring participants to divulge the name, address, and other identifying factors not necessary for analysis.

Compensation

No monetary compensation will be given to the participants, but several tokens will be raffled to all the participants who answered the online survey and did the KIIs.

This research will pose risks to data privacy, as discussed and addressed above. In addition, there will be a risk of social exclusion should data leaks arise due to the stigma against mental health. This risk will be mitigated by properly executing the data collection and analysis plan, excluding personal details and tight data privacy measures. Moreover, there is a risk of psychological distress among the participants due to the sensitive information. This risk will be addressed by subjecting the SAQ and the KII guidelines to the project team’s psychiatrist’s approval, ensuring proper communication with the participants. The KII will also be facilitated by registered clinical psychologists/psychiatrists/social scientists to ensure the participants’ appropriate handling; there will be a briefing and debriefing of the participants before and after the KII proper.

Participation in this study will entail health education and a voluntary referral to a study-affiliated psychiatrist, discussed in previous sections. Moreover, this would contribute to modifications in targeted mental-health campaigns for the 18–25 age group. Summarized findings and recommendations will be channeled to stakeholders for their perusal.

Dissemination

The results will be actively disseminated through conference presentations, peer-reviewed journals, social media, print and broadcast media, and various stakeholder activities.

This study protocol rationalizes the examination of the mental health of the college students in the Philippines during the COVID-19 pandemic as the traditional face-to-face classes transitioned to online and modular classes. The pandemic that started in March 2020 is now stretching for more than a year in which prolonged lockdown brings people to experience social isolation and disruption of everyday lifestyle. There is an urgent need to study the psychosocial aspects, particularly those populations that are vulnerable to mental health instability. In the Philippines, where community quarantine is still being imposed across the country, college students face several challenges amidst this pandemic. The pandemic continues to escalate, which may lead to fear and a spectrum of psychological consequences. Universities and colleges play an essential role in supporting college students in their academic, safety, and social needs. The courses of activities implemented by the different universities and colleges may significantly affect their mental well-being status. Our study is particularly interested in the effect of online classes on college students nationwide during the pandemic. The study will estimate this effect on their mental wellbeing since this abrupt transition can lead to depression, stress, or anxiety for some students due to insufficient time to adjust to the new learning environment. The role of social media is also an important exposure to some college students [ 55 , 56 ]. Social media exposure to COVID-19 may be considered a contributing factor to college students’ mental well-being, particularly their stress, depression, and anxiety [ 57 , 58 ]. Despite these known facts, little is known about the effect of transitioning to online learning and social media exposure on the mental health of college students during the COVID-19 pandemic in the Philippines. To our knowledge, this is the first study in the Philippines that will use a mixed-method study design to examine the mental health of college students in the entire country. The online survey is a powerful platform to employ our methods.

Additionally, our study will also utilize a qualitative assessment of the college students, which may give significant insights or findings of the experiences of the college students during these trying times that cannot be captured on our online survey. The thematic findings or narratives from the qualitative part of our study will be triangulated with the quantitative analysis for a more robust synthesis. The results will be used to draw conclusions about the mental health status among college students during the pandemic in the country, which will eventually be used to implement key interventions if deemed necessary. A cross-sectional study design for the online survey is one of our study’s limitations in which contrasts will be mainly between participants at a given point of time. In addition, bias arising from residual or unmeasured confounding factors cannot be ruled out.

The COVID-19 pandemic and its accompanying effects will persistently affect the mental wellbeing of college students. Mental health services must be delivered to combat mental instability. In addition, universities and colleges should create an environment that will foster mental health awareness among Filipino college students. The results of our study will tailor the possible coping strategies to meet the specific needs of college students nationwide, thereby promoting psychological resilience.

Acknowledgments

The researchers would like to extend their gratitude to the executives of the Philippine Red Cross, notably Senator Richard J. Gordon (Chairman), Ms. Elizabeth S. Zavalla (Secretary-General), and Ms. Maria Theresa S. Bongiad (Manager, Red Cross Youth), for making this project a reality. We also would like to thank all Red Cross Youth Chapters in the Philippines for helping in the pre-implementation stage of the project.

Funding Statement

This project is being supported by the American Red Cross through the Philippine Red Cross and Red Cross Youth. The funder will not have a role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.

• PLoS One. 2022; 17(5): e0267555.

Decision Letter 0

PONE-D-21-17998Assessing the Effect of the COVID-19 Pandemic, Shift to Online Learning, and Social Media Use on Mental Health Among College Students in the Philippines: A Mixed-Method Study ProtocolPLOS ONE

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Reviewers' comments:

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Comments to the Author

1. Does the manuscript provide a valid rationale for the proposed study, with clearly identified and justified research questions?

The research question outlined is expected to address a valid academic problem or topic and contribute to the base of knowledge in the field.

Reviewer #1: Yes

Reviewer #2: Partly

Reviewer #3: Yes

Reviewer #4: No

2. Is the protocol technically sound and planned in a manner that will lead to a meaningful outcome and allow testing the stated hypotheses?

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Reviewer #3: Partly

Reviewer #4: Partly

3. Is the methodology feasible and described in sufficient detail to allow the work to be replicable?

Descriptions of methods and materials in the protocol should be reported in sufficient detail for another researcher to reproduce all experiments and analyses. The protocol should describe the appropriate controls, sample size calculations, and replication needed to ensure that the data are robust and reproducible.

Reviewer #2: No

Reviewer #4: Yes

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Reviewer #3: No

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(Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: This study protocol aims to access the psychological effects among college students (18-25 years old) in the Philippines from the global pandemic, COVID-19, shift to online learning, and social media usage. The objectives of the study protocol address using a mixed-method study design that utilizes the quantitative and qualitative components. For the quantitative analysis, the authors propose sending an online self-administered questionnaire to the eligible participants to answer on socio-demographic factors. Based on the information provided, mental health outcomes will be assessed using two validated survey tools, Depression, Anxiety, and Stress Scale (DASS-21) and Brief-COPE. Additionally, the authors propose estimating the association between mental health outcomes and possible risk factors by using generalized linear models. Key informant interviews, a part of the qualitative component that addresses the stressors affecting the student’s mental health and behavior during the quarantine period. Finally, the authors suggest evaluating the data from quantitative and qualitative sources by using Data triangulation, which analyzes multiple sources of data to enhance the credibility of a research study.

The careful methodology provided in this study protocol will allow other researchers to apply this design to their studies. The validation of this study should provide a roadmap to study the effect of the pandemic on students in other countries.

The design of the study is very detailed for the most part. The authors have provided the necessary information about how the study population would be recruited and provided a justification for the sample size (quantitative data) that would be included in the study by providing relevant power calculations. However, for the qualitative study increasing the number of participants from different areas of the country would improve the quality of the outcome. Also, I would like to ask if any of the authors are Psychologists? If not, please acknowledge the Psychologists if the authors received any help in designing the study.

Also, the inclusion and exclusion criteria of the study population could be explained in more detail. It is not clear if only currently enrolled students would be included in the study. It would be important to justify the exclusion criteria.

Acknowledging similar studies (Copeland et al., 2021 and Fawaz et al., 2021) would help readers with a greater context. I would like to suggest the authors to cite the peer-reviewed version of the article titled “Barriers to online learning in the time of COVID-19: A national survey of medical students in the Philippines”. Also, I would like to ask the authors to change the references according to the journal requirements and have a uniform style.

Reviewer #2: Thank you for the opportunity to review the study protocol. The protocol is for a mixed-methods study looking at the impact of COVID19 and the subsequent shift to quarantine (stay at home), the implementation of online learning formats, and social media use on college students mental health. The study will provide insight into factors impacting mental health of college students. There are some issues with the study protocol that should be addressed.

Paragraph 2.

• If you refer to SARS-CoV-2 as COVID-19, please include swine flu when referring to H1N1.

Paragraph 3.

• It is not clear which pandemic the authors are referring to.

• Include reference examples for the first sentence. P

• rovide examples of how infection and death have "adversely affected" mental health.

• How many people responded to the survey measuring the psychological effects of COVID19 in the Philippines?

Paragraph 4.

• Provide references for the first three sentences.

• Provide details of the studies you reference.

• Final sentence is conflating being infected by COVID19 and suffering from mood and substance disorders - please clarify exactly what is meant.

Paragraph 5.

• University students are not generally accepted as a vulnerable population. Please provide a reference that supports this statement.

• Second sentence - provide the references for the multiple studies.

• Are Chinese university students similar to Filipino college students? Surely there are other studies from other countries that can be included here. Or is the social, cultural, and political situation similar between the Philippines and China?

Paragraph 6.

• Second sentence is not clear. Do you mean that academic performance is associated with student mental health? If so, just say that.

• What is "this" in the sentence: "Online learning poses multiple challenges to this".

• Provide an example of "Students’ various social support systems" that have to adapt.

• In this sentence: "These challenges are alarming because social support has been noted as a critical aspect of mediating acute distress disorder" it's not clear if the statement refers to students or some other population.

• References are needed for the following sentences: "In addition, loneliness has been rising for the past six years amongst this vulnerable demographic. One study showed that being a student is a risk factor for loneliness, exacerbated during the pandemic." Furthermore, please provide greater clarity around the population being discussed.

• The following sentence does not follow the logic from the preceding sentences: "Therefore, online learning must be perceived as an inclusive community and a safe space for peer-to-peer interactions (18)."

Paragraph 7.

• The following sentence needs a reference: "One research recommends clear and focused design elements on accommodating students living with depression."

• The argument presented in this paragraph is not clear.

Paragraph 8

• The first three sentences need references.

• The argument for examining the effect of social media on students mental health is weak and is only presented in the second to last paragraph.

Paragraph 9

• There is no inclusion of social media in this paragraph.

• This paragraph should present a strong argument for the study, including all the factors that are to be included in the study.

Study aim –

• Aims 1 and 2 are very similar. Aim 1 suggests you are going to describe the sample according to the categories of mental health. This is quite unusual and makes me think that maybe 'stratified is not the correct word for this aim. Perhaps what is meant is that the study aims to describe the characteristics of the sample population including mental health (e.g., depression, anxiety, stress)

• Aim 2 it is not clear if the determination of prevalence is before or post, the subsequent shift to online learning. Please clarify.

• Aim 3. It is not clear what the aim is. Please simplify. It might require breaking this one aim up into 2 or 3.

• Aim 4 Is similar to the last aspect of aim 3.

• Furthermore, the phrase "during the COVID-19 pandemic, quarantine, and subsequent shift to online learning." Is confusing - COVID 19 is current, it's not clear whether all or some of the students are in quarantine, and presumably, they have shifted to online learning (past). Please clarify the state in which the study will be conducted.

• When is the quarantine period. Please provide dates?

• Population RCY seems like a great way to recruit participants. However, this population may not reflect all college students as RCY are volunteers. Students who volunteer may have different values and attitudes towards mental health, and social justice and adapting to change. The authors must account for this in their study and ensure there are no differences between their RCY participants and the non-member RCY participants on critical factors (e.g., mental health etc).

• It is not clear how random sampling for the KII will be achieved. The reported sampling method reads more like stratified sampling.

Inclusion exclusion criteria

• It is not clear why those who identify as non-binary genders are not included. Why is gender an inclusion/ exclusion criterion?

• The sample size calculation - how was the number of KIIs determined? how data saturation will be determined

• It's not clear how the demographic factors will be collected.

• Variables are not clear (e.g., sickness of loved ones) - do you mean family members? friends? pets? do you mean chronic illness or acute illness?

• What other factors that could affect mental health are you going to measure?

• This statement is not accurate: "The DASS-21 will measure the prevalence of depression, anxiety, and stress-related issues affecting daily life (28)" please correct to more accurately describe the DASS.

• It is not clear how this variable will be measured: "the total time spent on social media (TSSM) per day will be ascertained by querying the participants to provide an estimated time spent daily on social media during and after their online classes." is it the total time in one day, or only during and after class. Why not all day? or why is it only during and after of interest? The assumption is that they will increase their use of social media, but what if their use of social media is the same or less than before the shift to online learning?

• Regarding the KII, it may be country-specific, but it's not clear how social scientists and research assistants facilitation of interviews will be the same quality as psychologists and psychiatrists. How will the difference in skills in interviewing be overcome?

• Will the results of the survey be used to develop interview questions?

Data Analysis

• Given that all the variables are known, the quantitative analysis could be clearer with examples of what the authors mean by 'covariate' and 'possible risk factors.

• Will the analyses be explorational? the literature review implies that some hypotheses may be developed. If so, the analyses should be designed to test those hypotheses.

• How will p-values be adjusted to account for the multiple analyse?

• Will the themes be developed independently by researchers? how many researchers will be involved in the coding? It is not clear from the description how will triangulation be established. Will multiple authors do the coding of the interview transcripts - independently? Will the results of the survey be used to inform the coding of the interviews?

• It is still not clear how random sampling will be achieved by the authors for recruitment for KII.

• What will the researchers do if a participants response to the DASS indicates they have clinical levels of Depression, Anxiety or Stress?

• References are needed throughout the discussion. For example, "The role of social media is also an important exposure to some college students. Social media exposure to COVID-19 may be considered a contributing factor to college students’ mental well-being, particularly their stress, depression, and anxiety."

Reviewer #3: The title of the protocol is timely and well presented. However, I don't agree on publishing protocols for cross-sectional studies. However, the results of this protocol are expected to add great value for public health.

Reviewer #4: This is just a proposal stage. Some part of the methods section is not well defined. Without any results, it is not suitable for a scientific publication yet.

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Reviewer #1: No

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Author response to Decision Letter 0

16 Jan 2022

Reviewer No. 1:

This study protocol aims to assess the psychological effects among college students (18-25 years old) in the Philippines from the global pandemic, COVID-19, shift to online learning, and social media usage. The objectives of the study protocol address using a mixed-method study design that utilizes the quantitative and qualitative components. For the quantitative analysis, the authors propose sending an online self-administered questionnaire to the eligible participants to answer on socio-demographic factors. Based on the information provided, mental health outcomes will be assessed using two validated survey tools, Depression, Anxiety, and Stress Scale (DASS-21) and Brief-COPE. Additionally, the authors propose estimating the association between mental health outcomes and possible risk factors by using generalized linear models. Key informant interviews, a part of the qualitative component that addresses the stressors affecting the student’s mental health and behavior during the quarantine period. Finally, the authors suggest evaluating the data from quantitative and qualitative sources by using data triangulation, which analyzes multiple sources of data to enhance the credibility of a research study.

The design of the study is very detailed for the most part. The authors have provided the necessary information about how the study population would be recruited and provided a justification for the sample size (quantitative data) that would be included in the study by providing relevant power calculations. However, for the qualitative study, increasing the number of participants from different areas of the country would improve the quality of the outcome. Also, I would like to ask if any of the authors are Psychologists? If not, please acknowledge the Psychologists if the authors received any help in designing the study.

Response: One of the authors is a senior consultant psychiatrist, Dr. Josefina T. Ly-Uson. She helped in the design of the study.

Response: The inclusion criteria now included “currently enrolled college students.” The exclusion criteria included those students in graduate-level programs and those whose current curricula involve going on duty. We purposely chose to exclude those students because they have a different set of schooling conditions compared to the rest of the regular college students.

Acknowledging similar studies (Copeland et al., 2021 and Fawaz et al., 2021) would help readers with a greater context. I would like to suggest the authors cite the peer-reviewed version of the article titled “Barriers to online learning in the time of COVID-19: A national survey of medical students in the Philippines”. Also, I would like to ask the authors to change the references according to the journal requirements and have a uniform style.

Response: Thank you for the suggestion. This comment is noted, and revisions have been made.

Reviewer No. 2

Thank you for the opportunity to review the study protocol. The protocol is for a mixed-methods study looking at the impact of COVID19 and the subsequent shift to quarantine (stay at home), the implementation of online learning formats, and social media use on college students’ mental health. The study will provide insight into factors impacting mental health of college students. There are some issues with the study protocol that should be addressed.

Response: This is noted, and revisions have been made.

• Include reference examples for the first sentence.

• Provide examples of how infection and death have "adversely affected" mental health.

Response: This is noted, and the statement was deleted.

Response: This is noted, and revisions have been made. We cited prevalence rates from three different countries (USA, Malaysia, and China).

Response: This is noted and revisions have been made.

Response: This is noted, and references have been added.

• The argument for examining the effect of social media on students' mental health is weak and is only presented in the second to last paragraph.

• Aims 1 and 2 are very similar. Aim 1 suggests you are going to describe the sample according to the categories of mental health. This is quite unusual and makes me think that maybe 'stratified' is not the correct word for this aim. Perhaps what is meant is that the study aims to describe the characteristics of the sample population including mental health (e.g., depression, anxiety, stress)

Response: This is noted. We replaced the word stratified with categorized. Aims 1 and 2 are not similar. The first aim will present the characteristics of the population, while the second aim will present the prevalence and risk factors.

Response: Aim 2 will determine the prevalence after the subsequent shift to online learning. We will not determine the pre-shift to online learning prevalence because there was an abrupt transition to online learning in the Philippines, which limits our time frame to collect data before the shift to online learning.

Response: Aim 3 was split into 2 aims. The first aim will be looking at the effect of social media use on markers of mental health (depression, anxiety, stress, and coping strategies towards stress). At the same time, the second aim will look at the effect of online learning shift on markers of mental health (depression, anxiety, stress, and coping strategies towards stress).

Response: Aim 4 is very different from Aim 3 because Aim 4 is the qualitative part of the study which will use key informant interviews to explore facilitators or stressors that modified the mental health status of the participants while Aim 3 is the quantitative part.

Response: Currently, the Philippines is still in quarantine due to COVID-19 and all classes made a shift to online learning and no face-to-face classes are allowed.

• When is the quarantine period? Please provide dates?

Response: Currently, the Philippines is still in quarantine. We opted to use April to November 2021 as the study period.

Response: RCY is connected with the majority of the universities and colleges in the Philippines. RCY will spearhead the distribution of the questionnaires to universities and colleges in the Philippines. Therefore, non-RCY volunteers are also encouraged and recruited to participate in the study. We will account for the differences between RCY and non-RCY volunteers by adding an indicator variable in our statistical models.

Response: Yes, it is a stratified random sampling using 4 major regions of the country. We have revised the section.

Response: Gender is not an exclusion/inclusion criterion. Revisions have been made.

Response: The saturation will be achieved once almost all of the interview transcripts have been generating no new information. We added the reference Hagaman and Wutich as basis for the KII sample size.

Response: Demographic factors will be collected using the SAQ we developed.

Response: We deleted “other factors that could affect mental health”.

• It is not clear how this variable will be measured: "the total time spent on social media (TSSM) per day will be ascertained by querying the participants to provide an estimated time spent daily on social media during and after their online classes." is it the total time in one day, or only during and after class. Why not all day? or why is it only during and after interest? The assumption is that they will increase their use of social media, but what if their use of social media is the same or less than before the shift to online learning?

Response: We will measure the social media during and outside online class hours. TSSM will measure the total time spent per day on social media. We will not account for the TSSM before shifting to online learning. Our objective is to measure their TSSM during and outside of class hours that is happening in a quarantine period.

Response: Dr. Uson, our board-certified psychiatrist, trained the research assistants to facilitate KIIs. She will also be present during the KII. In the Philippines, training of the research assistants to facilitate KII is done in order for them to learn and be adequately trained for future research. Research assistants may someday be the research primary investigators.

Response: The results of the survey will not be used to develop interview questions. But we will use the answers of the KII participants in the survey as a guide during the interviews of the KII participants.

Response: Yes, all the possible variables that will be used in the data analysis have been identified a priori as confounding factor covariates and risk factors.

• Will the analyses be explorational? The literature review implies that some hypotheses may be developed. If so, the analyses should be designed to test those hypotheses.

Response: The analysis will not be explorational. Important possible risk factors (exposures) and covariates (confounding factors) were chosen a priori and will be used for the multivariable generalized linear models.

• How will p-values be adjusted to account for the multiple analyses?

Response: We will not use a correction method to adjust the p-values, since our statistical analyses will be not that many to warrant an adjustment. Possible adjustments of p-values are for microarray datasets to correct the occurrence of false positives in the multiple analyses.

• Will the themes be developed independently by researchers? How many researchers will be involved in the coding? It is not clear from the description how triangulation will be established. Will multiple authors do the coding of the interview transcripts - independently? Will the results of the survey be used to inform the coding of the interviews?

Response: This is noted, and revisions have been made. The description of the triangulation was described in the “data triangulation” section and references have been updated to include the “comparison of datasets for convergence”. As mentioned earlier, the results of the survey will not be used to develop interview questions. But we will use their answers in the survey as a guide during the interview.

Response: From the pool of participants per area, using a software, we will randomly choose prospective KII participants and contact them if they are willing to participate. If they opted not to participate, we will randomly choose participants again from the pool of respondents per area.

• What will the researchers do if a participant's response to the DASS indicates they have clinical levels of Depression, Anxiety or Stress?

Response: It is our ethical duty to assist the participants if they have been screened to have high clinical levels of depression, anxiety, or stress. We will refer them to a tertiary government hospital for further evaluation, or treatment if needed.

Submitted filename: Response to Reviewers.docx

Decision Letter 1

15 Feb 2022

PONE-D-21-17998R1Assessing the Effect of the COVID-19 Pandemic, Shift to Online Learning, and Social Media Use on the Mental Health of College Students in the Philippines: A Mixed-Method Study ProtocolPLOS ONE

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

 Please submit your revised manuscript by Mar 31 2022 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at  gro.solp@enosolp . When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Jianhong Zhou

Associate Editor

Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

Reviewer #2: Yes

Reviewer #1: Appreciate the authors' efforts, they addressed all the concerns adequately.

However, I suggest authors to cite the peer-reviewed version of the article instead of medRxiv.

Good luck with your publication.

Reviewer #2: Thank you for the opportunity to review this protocol. The authors have made considerable changes to the protocol in response to reviewer comments. There are just a couple of minor points to be addressed.

Paragraph 6 – please include the years that these studies were conducted. Also, as these studies are cross-sectional, it is not entirely accurate to say that there is a growing prevalence (which you could do if there were similar studies conducted years apart on the same population, or a longitudinal study. But these are separate populations and the years have not been presented to the reader). Perhaps just delete ‘growing’

Qualitative data – paragraph 1- The description of the qualitative data is missing some information. For example, at what point will the three qualitative analysts discuss the themes? Will the three analysts do all of the transcripts or will they do a sample to establish consistency before dividing the transcripts? Please confirm whether inductive or deductive logic to the coding approach.

Reviewer #4: -The study is proposed to explore the effect of social media use, online learning upon mental health. It is hard to differentiate wither the effect is due to social media or online learning. (aim 3 and aim 4).

- The study participants (inclusion criteria) also include out-of-school youth as they are including all RCY who agreed.

Author response to Decision Letter 1

23 Feb 2022

RESPONSE TO REVIEWERS

PONE-D-21-17998: Assessing the Effect of the COVID-19 Pandemic, Shift to Online Learning, and Social Media Use on Mental Health Among College Students in the Philippines: A Mixed-Method Study Protocol

Reviewer #1: Appreciate the authors' efforts, they addressed all the concerns adequately. However, I suggest authors cite the peer-reviewed version of the article instead of medRxiv.

Response: We have removed the medRxiv references.

Reviewer #2: Thank you for the opportunity to review this protocol. The authors have made considerable changes to the protocol in response to reviewer comments. There are just a couple of minor points to be addressed.

Response: We have deleted the word “growing.”

Response: The qualitative data description was added with information regarding the discussion of thematic analysis of the three researchers. To ensure consistency, KII training, including transcription and quality assurance, will be done with the research team members as detailed in the Data Management Plan Section. Moreover, an inductive logic approach to coding will be conducted. All of these are reflected in the revised manuscript.

Reviewer #4: -The study is proposed to explore the effect of social media use, online learning on mental health. It is hard to differentiate whether the effect is due to social media or online learning. (Aim 3 and Aim 4).

Response: Generalized linear model using Poisson regression will be done to independently analyze the effect of social media usage or online learning on the participants' mental health. Thus, social media usage and online learning will be treated as two exposures independently.

Response: We have revised the inclusion and exclusion criteria. “Out-of-school youth” classification is now included in the exclusion criteria. Moreover, current enrollment in a university is part of the inclusion criteria.

Submitted filename: Response to Reviewers_Round 2_Plos One 22022022.docx

Decision Letter 2

12 Apr 2022

Assessing the Effect of the COVID-19 Pandemic, Shift to Online Learning, and Social Media Use on the Mental Health of College Students in the Philippines: A Mixed-Method Study Protocol

PONE-D-21-17998R2

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

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An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at http://www.editorialmanager.com/pone/ , click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at gro.solp@gnillibrohtua .

If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact gro.solp@sserpeno .

Elisa Panada

Staff Editor

Additional Editor Comments (optional):

Reviewer #1: I am satisfied with the revisions that were made to the manuscript. I endorse this manuscript for publication.

Reviewer #2: The manuscript provides a valid rationale for the proposed study and the study is technically sound. The methodology is feasible and described in detail.

Acceptance letter

21 Apr 2022

Dear Dr. Baja:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact gro.solp@sserpeno .

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Thank you for submitting your work to PLOS ONE and supporting open access.

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on behalf of

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3 case studies: How ready are Philippine schools for distance learning?

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This is AI generated summarization, which may have errors. For context, always refer to the full article.

Alejandro Edoria

As we approach school opening 2020, what is on everybody’s mind is how distance learning will be carried out in fact. 

Distance learning is completely new to all but a handful of private schools already attuned to online learning using the internet. Most schools and students, however, have connectivity and bandwidth limitations.  

Distance learning using school packets delivered and collected weekly will have to be the immediate solution because face-to-face contact carries with it the risk of spreading the coronavirus.  

The learning curve for distance learning will be steep.  

In development management, there is a principle of subsidiarity: Where a lower authority can handle a matter, a higher authority should not interfere. By driving authority as far down the decision-making chain as possible, this places decision-making closer to the people.  

In the case of education, this places decision-making at the level of the school.  

So, in this new normal, the drivers of distance education should not be the Department of Education (DepED) central office or the regions; rather, it should be the schools divisions and the schools themselves.  

Here are 3 cases to show how different levels are preparing for such.  

Bacjawan Sur ES (Concepcion, Iloilo)

In the 3rd class town of Concepcion, Iloilo, school principal Rogie Espulgar is working with his 14 teachers to figure out how to reorganize their small rural elementary school for distance learning this coming school year.

Bacjawan Sur Elementary School is located 3 kilometers from the town proper and is host to housing units of families displaced by Super Typhoon Yolanda (Haiyan) in November 2013. It has 330 pupils from kindergarten to Grade 6.   

Five modalities for meeting students have been identified:  

• Face-to-face (traditional, pre-Covid-19 modality)
• Online classes (using web-based and digitized lesson resources [LRs])
• Online-Offline modular (using web-based and digitized LRs )
• Offline modular (using digitized LRs)
• Modular (using printed LRs). 

With the DepED instruction of limited face-to-face contact, Principal Espulgar and his teachers have decided to meet their pupils in shifts.

Grades Kindergarten to Grade 2 will meet face-to-face . Kindergarten will meet daily for half the day, either in a morning or afternoon session. Grades 1 and 2 will be in shifts on alternate days (Monday, Wednesday, Friday or Tuesday, Thursday).   

Grades 3 to 6 will have a modified modular schedule with some face-to-face time . Grade 3 classes will do face-to-face on either Monday-Tuesday, Wednesday-Thursday, or Thursday-Friday (4 classes of 13 or 14 students per class). The other days will be modular with students working on learning assignments from home. A similar type of schedule will be worked out for Grades 4, 5, and 6.

Classes will be divided into groups with no more than 15 or 16 learners per group (Kindergarten is smaller at 10 per group).  This will allow for proper physical distancing when the kids meet face-to-face.

The total number of classrooms in the school are 13, but only 11 classrooms will be used; the other two classrooms will be utilized for online classes and as an isolation room in case of sickness.

“The world is rapidly changing,” said Principal Espulgar, “and along with it comes new innovations and technologies.

“Education has to evolve to keep pace.  The teacher’s role is not to be the sole provider of learning.  She has to be a guide, a motivator, and facilitator of learning…. Compassion, dedication, and commitment are no longer enough.  The modern-day teacher should also make herself (1) innovative, (2) tech-savvy, and (3) open to change,” he added.

How ready are the teachers?

Of the 14 teachers:

• 93% (13)  have smart phones
• 43% (6) have laptops or desktops
• 79% (11) have nternet connectivity
• 50% (7) have ICT gadgets and internet access sufficiency –
• 64% (9) have private space at home
• 29% (4) are able to do ICT troubleshooting with competence
• 79%-93% (11 to 13) are able to use web browser‘s, telecommunication platforms in messaging, social video platforms, video streaming platforms

To prepare for the new normal, the school went through the following types of training for the 14 teachers:

• Mental health and psychosocial debriefing seminar
• Walkthrough of the Minimum Education Learning Competencies (MELC) prescribed by DepED
• Basic and advanced computer software programs (depending on the level of experience of teachers)
• Different web-based platforms for communication, educational sites, learning approaches
• Orientation on the school’s learning continuity plan (LCP)

A physical facilities plan following health protocols was prepared in May to June. The single school entrance and exit for all 330 students plus faculty was modified and improved. More than half, or 9 of the 14 classrooms are considered makeshift classrooms .  Five of 14 classrooms are standard classrooms. One classroom (makeshift) has been set aside as an isolation room in case there are any health incidents. One standard is room is set aside for online classes.  There are 4 handwashing stations distributed in the center areas of the school.

In  July, before the start of classes, the teachers worked on the following:

• Learning resources plans
• School leadership expectations
• Parents participation and roles
• Community linkages
• School action plans
• The school risk management plan
• Health protocols and standards
• Enrollment guidelines

Navotas Schools Division (National Capital Region)

The Navotas Schools Division in Metro Manila is a small sized division of 24 schools of which 7 are high schools.  It is a highly urbanized, heavily populated schools division. 

“The schools in the division will use a modified modular distance learning approach,” schools division head Alejandro Ibanez explained.

“Individualized instruction will allow learners to use self-learning modules in print and digital form. Teachers will use Messenger chat or text messaging to communicate with and monitor students’ progress,” he added.

The schools division has designed a NAVOSchool in-a-box kit for every pupil and student in the division funded by DepED and the city government. 

At the kindergarten level, each child will receive a plastic bin loaded with learning packets, story books, donated school supplies, a hygiene kits and a toy from a partner. The kit also includes a Parent’s guide that covers home learning activities and a guide to organizing the study environment at home.  

Similar kits will be given by the division to students of all grade levels. The learning resource packets will include textbooks and self-learning modules by DepED, modules/materials prepared by the division office and schools, workbooks prepared by teachers, lesson guides for parents and guardians, school supplies, a dictionary, and a hygiene kit.

There is a project in the Division called Project PANATA (PAtnubay kay NApay at TAtay) which is a virtual training using Messenger and Google Meet intended for parents.  

Since the program will be largely packet-based given the connectivity difficulties, the process flow of the school-based modular distance learning is a weekly or biweekly cycle of packet distribution and collection throughout the school year for as long as face-to-face learning is disrupted.

To help students who might fall behind, a “Tutor A Learning Child” program is being organized with para-teacher tutor volunteers being recruited. The Navotas National HS has began recruiting young alumni at the university level to volunteer to work with students in difficult circumstances.  

5 operational stages

Stage 1:  Planning (Identify MELCs for module development by Education Supervisors and teachers).

Stage 2:   Development (by development teams) of learning materials with orientation sessions to provide a standards template.  

Stage 3:   Quality Assurance (QA team in coordination with learning area supervisors)

Stage 4:  Production and reproduction (procurement of teaching/learning resources through the Local School Board using the SEF [Special Education Fund] and the school MOOE [Maintenance and Other Operating Expenses]).

Stage 5:  Distribution of kits and packets

Teachers prepare learning materials, weekly study guides, and other tools which will be distributed in one of three ways:  Pick up from school, Hatid-Aral delivery to homes, or through distribution to barangay or community learning centers.

Taytay Senior High School, Rizal

Recently an ad was flashed on FaceBook that reads: “Do you have a bicycle or motorbike? Do you have an internet connection? Do you own a sari-sari store? Or do you love teaching? Why not be a volunteer of Taytay Senior High School?”

Four modes of voluntarism were spelled out:

• Learning Resource Mover (LR Mover) – Volunteer riders’ or bicycling group who will help deliver learning resources to homes or community kiosks of learners.
• Connect-a-Learner – Volunteer households who will provide learning space in their homes for internet access in their neighborhood.
• Learning Resources Pasabay/Kiosks – Sari-sari store and/or landmarks owners in far-flung communities to serve as pick-up centers for learning resources.
• Community-based Tutorial – Volunteers who will be tutoring learners within his/her community.

As shown in the above cases, DepED schools and divisions have worked hard to design a system to address the new normal of distance learning.  The challenge: Moving from simulation to full implementation where large numbers weekly will put stress on the system.  

How will the system address backlogs, shortages, and bottlenecks in real time?  How will the system address slow learners, learners falling behind or even learners becoming absent and dropping out?   

There will be two things to look at immediately: System efficiency and system effectiveness.

System efficiency

How well do the different parts interact and deliver as planned? What will stress the system is when week-in-and-week-out packets are going back and forth.   If families or teachers fall behind, what kind of support can help them catch up?  If a teacher cannot cope with the demands of distance learning, is there a system for substitution or support?  How do you keep the education materials production flowing efficiently and within budget?

System effectiveness

How do you ensure that learning is actually happening?  For Grades 1 and 2, this would be the 3 Rs (Reading, Writing, and Arithmetic or Literacy and Numeracy). For other grade levels, it is reading and learning at Grade level indicators.  

How do you pick up slow learners or learners with specific difficulties? Recognizing learning difficulties from a distance will be a challenge. Divisions and schools will be totally consumed with implementation issues when the school year starts with distance learning as the new normal.  They might miss many concerns. This is where the regional office comes in:  Quality Assurance, oversight (ensuring that schools and divisions are not overlooking processes or taking shortcuts), and monitoring and evaluation.

The regional office should be doing random testing of students to check effectiveness of the distance education modality and study the efficiencies of this new modality.  

The new normal must be matched by a new imagination about education.

In a recent meeting discussing the education budgets, former DepED Undersecretary for Finance Rey Laguda said: “It’s not enough to just plan for the future based on what we need today.  We need to imagine what an education future will look like.  Because we’ve never had to address something like distance learning at scale before, we need to let our imaginations help draw a picture of what that might be.”

We need to think of new approaches to on how our schools will operate in this new normal, from Imagination (What are the best ways to deliver distance learning?) to a theory of learning about distance learning. Plans can then be drawn up for delivery with scale done.  Once the school year has started, periodic and robust monitoring and evaluation will help us answer the most important question of all:  Are our children learning in this new normal?

Experimentation with distance learning will have to be led by schools and teachers who are closest to students at home. The degree of innovation at this level is a good indication of an education system that is slowly maturing. – Rappler.com

Juan Miguel Luz is former Head, Zuellig School of Development Management at the Asian Institute of Management.  Former Undersecretary, Department of Education. 

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