research on student engagement

• Review article
• Open access
• Published: 22 January 2020

Mapping research in student engagement and educational technology in higher education: a systematic evidence map

• Melissa Bond   ORCID: orcid.org/0000-0002-8267-031X 1 ,
• Katja Buntins 2 ,
• Svenja Bedenlier 1 ,
• Olaf Zawacki-Richter 1 &
• Michael Kerres 2  

International Journal of Educational Technology in Higher Education volume  17 , Article number:  2 ( 2020 ) Cite this article

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Digital technology has become a central aspect of higher education, inherently affecting all aspects of the student experience. It has also been linked to an increase in behavioural, affective and cognitive student engagement, the facilitation of which is a central concern of educators. In order to delineate the complex nexus of technology and student engagement, this article systematically maps research from 243 studies published between 2007 and 2016. Research within the corpus was predominantly undertaken within the United States and the United Kingdom, with only limited research undertaken in the Global South, and largely focused on the fields of Arts & Humanities, Education, and Natural Sciences, Mathematics & Statistics. Studies most often used quantitative methods, followed by mixed methods, with little qualitative research methods employed. Few studies provided a definition of student engagement, and less than half were guided by a theoretical framework. The courses investigated used blended learning and text-based tools (e.g. discussion forums) most often, with undergraduate students as the primary target group. Stemming from the use of educational technology, behavioural engagement was by far the most often identified dimension, followed by affective and cognitive engagement. This mapping article provides the grounds for further exploration into discipline-specific use of technology to foster student engagement.

Introduction

Over the past decade, the conceptualisation and measurement of ‘student engagement’ has received increasing attention from researchers, practitioners, and policy makers alike. Seminal works such as Astin’s ( 1999 ) theory of involvement, Fredricks, Blumenfeld, and Paris’s ( 2004 ) conceptualisation of the three dimensions of student engagement (behavioural, emotional, cognitive), and sociocultural theories of engagement such as Kahu ( 2013 ) and Kahu and Nelson ( 2018 ), have done much to shape and refine our understanding of this complex phenomenon. However, criticism about the strength and depth of student engagement theorising remains e.g. (Boekaerts, 2016 ; Kahn, 2014 ; Zepke, 2018 ), the quality of which has had a direct impact on the rigour of subsequent research (Lawson & Lawson, 2013 ; Trowler, 2010 ), prompting calls for further synthesis (Azevedo, 2015 ; Eccles, 2016 ).

In parallel to this increased attention on student engagement, digital technology has become a central aspect of higher education, inherently affecting all aspects of the student experience (Barak, 2018 ; Henderson, Selwyn, & Aston, 2017 ; Selwyn, 2016 ). International recognition of the importance of ICT skills and digital literacy has been growing, alongside mounting recognition of its importance for active citizenship (Choi, Glassman, & Cristol, 2017 ; OECD, 2015a ; Redecker, 2017 ), and the development of interdisciplinary and collaborative skills (Barak & Levenberg, 2016 ; Oliver, & de St Jorre, Trina, 2018 ). Using technology has the potential to make teaching and learning processes more intensive (Kerres, 2013 ), improve student self-regulation and self-efficacy (Alioon & Delialioğlu, 2017 ; Bouta, Retalis, & Paraskeva, 2012 ), increase participation and involvement in courses as well as the wider university community (Junco, 2012 ; Salaber, 2014 ), and predict increased student engagement (Chen, Lambert, & Guidry, 2010 ; Rashid & Asghar, 2016 ). There is, however, no guarantee of active student engagement as a result of using technology (Kirkwood, 2009 ), with Tamim, Bernard, Borokhovski, Abrami, and Schmid’s ( 2011 ) second-order meta-analysis finding only a small to moderate impact on student achievement across 40 years. Rather, careful planning, sound pedagogy and appropriate tools are vital (Englund, Olofsson, & Price, 2017 ; Koehler & Mishra, 2005 ; Popenici, 2013 ), as “technology can amplify great teaching, but great technology cannot replace poor teaching” (OECD, 2015b ), p. 4.

Due to the nature of its complexity, educational technology research has struggled to find a common definition and terminology with which to talk about student engagement, which has resulted in inconsistency across the field. For example, whilst 77% of articles reviewed by Henrie, Halverson, and Graham ( 2015 ) operationalised engagement from a behavioural perspective, most of the articles did not have a clearly defined statement of engagement, which is no longer considered acceptable in student engagement research (Appleton, Christenson, & Furlong, 2008 ; Christenson, Reschly, & Wylie, 2012 ). Linked to this, educational technology research has, however, lacked theoretical guidance (Al-Sakkaf, Omar, & Ahmad, 2019 ; Hew, Lan, Tang, Jia, & Lo, 2019 ; Lundin, Bergviken Rensfeldt, Hillman, Lantz-Andersson, & Peterson, 2018 ). A review of 44 random articles published in 2014 in the journals Educational Technology Research & Development and Computers & Education, for example, revealed that more than half had no guiding conceptual or theoretical framework (Antonenko, 2015 ), and only 13 out of 62 studies in a systematic review of flipped learning in engineering education reported theoretical grounding (Karabulut-Ilgu, Jaramillo Cherrez, & Jahren, 2018 ). Therefore, calls have been made for a greater understanding of the role that educational technology plays in affecting student engagement, in order to strengthen teaching practice and lead to improved outcomes for students (Castañeda & Selwyn, 2018 ; Krause & Coates, 2008 ; Nelson Laird & Kuh, 2005 ).

A reflection upon prior research that has been undertaken in the field is a necessary first step to engage in meaningful discussion on how to foster student engagement in the digital age. In support of this aim, this article provides a synthesis of student engagement theory research, and systematically maps empirical higher education research between 2007 and 2016 on student engagement in educational technology. Synthesising the vast body of literature on student engagement (for previous literature and systematic reviews, see Additional file  1 ), this article develops “a tentative theory” in the hopes of “plot[ting] the conceptual landscape…[and chart] possible routes to explore it” (Antonenko, 2015 , pp. 57–67) for researchers, practitioners, learning designers, administrators and policy makers. It then discusses student engagement against the background of educational technology research, exploring prior literature and systematic reviews that have been undertaken. The systematic review search method is then outlined, followed by the presentation and discussion of findings.

Literature review

What is student engagement.

Student engagement has been linked to improved achievement, persistence and retention (Finn, 2006 ; Kuh, Cruce, Shoup, Kinzie, & Gonyea, 2008 ), with disengagement having a profound effect on student learning outcomes and cognitive development (Ma, Han, Yang, & Cheng, 2015 ), and being a predictor of student dropout in both secondary school and higher education (Finn & Zimmer, 2012 ). Student engagement is a multifaceted and complex construct (Appleton et al., 2008 ; Ben-Eliyahu, Moore, Dorph, & Schunn, 2018 ), which some have called a ‘meta-construct’ (e.g. Fredricks et al., 2004 ; Kahu, 2013 ), and likened to blind men describing an elephant (Baron & Corbin, 2012 ; Eccles, 2016 ). There is ongoing disagreement about whether there are three components e.g., (Eccles, 2016 )—affective/emotional, cognitive and behavioural—or whether there are four, with the recent suggested addition of agentic engagement (Reeve, 2012 ; Reeve & Tseng, 2011 ) and social engagement (Fredricks, Filsecker, & Lawson, 2016 ). There has also been confusion as to whether the terms ‘engagement’ and ‘motivation’ can and should be used interchangeably (Reschly & Christenson, 2012 ), especially when used by policy makers and institutions (Eccles & Wang, 2012 ). However, the prevalent understanding across the literature is that motivation is an antecedent to engagement; it is the intent and unobservable force that energises behaviour (Lim, 2004 ; Reeve, 2012 ; Reschly & Christenson, 2012 ), whereas student engagement is energy and effort in action; an observable manifestation (Appleton et al., 2008 ; Eccles & Wang, 2012 ; Kuh, 2009 ; Skinner & Pitzer, 2012 ), evidenced through a range of indicators.

Whilst it is widely accepted that no one definition exists that will satisfy all stakeholders (Solomonides, 2013 ), and no one project can be expected to possibly examine every sub-construct of student engagement (Kahu, 2013 ), it is important for each research project to begin with a clear definition of their own understanding (Boekaerts, 2016 ). Therefore, in this project, student engagement is defined as follows:

Student engagement is the energy and effort that students employ within their learning community, observable via any number of behavioural, cognitive or affective indicators across a continuum. It is shaped by a range of structural and internal influences, including the complex interplay of relationships, learning activities and the learning environment. The more students are engaged and empowered within their learning community, the more likely they are to channel that energy back into their learning, leading to a range of short and long term outcomes, that can likewise further fuel engagement.

Dimensions and indicators of student engagement

There are three widely accepted dimensions of student engagement; affective, cognitive and behavioural. Within each component there are several indicators of engagement (see Additional file  2 ), as well as disengagement (see Additional file 2 ), which is now seen as a separate and distinct construct to engagement. It should be stated, however, that whilst these have been drawn from a range of literature, this is not a finite list, and it is recognised that students might experience these indicators on a continuum at varying times (Coates, 2007 ; Payne, 2017 ), depending on their valence (positive or negative) and activation (high or low) (Pekrun & Linnenbrink-Garcia, 2012 ). There has also been disagreement in terms of which dimension the indicators align with. For example, Järvelä, Järvenoja, Malmberg, Isohätälä, and Sobocinski ( 2016 ) argue that ‘interaction’ extends beyond behavioural engagement, covering both cognitive and/or emotional dimensions, as it involves collaboration between students, and Lawson and Lawson ( 2013 ) believe that ‘effort’ and ‘persistence’ are cognitive rather than behavioural constructs, as they “represent cognitive dispositions toward activity rather than an activity unto itself” (p. 465), which is represented in the table through the indicator ‘stay on task/focus’ (see Additional file 2 ). Further consideration of these disagreements represent an area for future research, however, as they are beyond the scope of this paper.

Student engagement within educational technology research

The potential that educational technology has to improve student engagement, has long been recognised (Norris & Coutas, 2014 ), however it is not merely a case of technology plus students equals engagement. Without careful planning and sound pedagogy, technology can promote disengagement and impede rather than help learning (Howard, Ma, & Yang, 2016 ; Popenici, 2013 ). Whilst still a young area, most of the research undertaken to gain insight into this, has been focused on undergraduate students e.g., (Henrie et al., 2015 ; Webb, Clough, O’Reilly, Wilmott, & Witham, 2017 ), with Chen et al. ( 2010 ) finding a positive relationship between the use of technology and student engagement, particularly earlier in university study. Research has also been predominantly STEM and medicine focused (e.g., Li, van der Spek, Feijs, Wang, & Hu, 2017 ; Nikou & Economides, 2018 ), with at least five literature or systematic reviews published in the last 5 years focused on medicine, and nursing in particular (see Additional file  3 ). This indicates that further synthesis is needed of research in other disciplines, such as Arts & Humanities and Education, as well as further investigation into whether research continues to focus on undergraduate students.

The five most researched technologies in Henrie et al.’s ( 2015 ) review were online discussion boards, general websites, learning management systems (LMS), general campus software and videos, as opposed to Schindler, Burkholder, Morad, and Marsh’s ( 2017 ) literature review, which concentrated on social networking sites (Facebook and Twitter), digital games, wikis, web-conferencing software and blogs. Schindler et al. found that most of these technologies had a positive impact on multiple indicators of student engagement across the three dimensions of engagement, with digital games, web-conferencing software and Facebook the most effective. However, it must be noted that they only considered seven indicators of student engagement, which could be extended by considering further indicators of student engagement. Other reviews that have found at least a small positive impact on student engagement include those focused on audience response systems (Hunsu, Adesope, & Bayly, 2016 ; Kay & LeSage, 2009 ), mobile learning (Kaliisa & Picard, 2017 ), and social media (Cheston, Flickinger, & Chisolm, 2013 ). Specific indicators of engagement that increased as a result of technology include interest and enjoyment (Li et al., 2017 ), improved confidence (Smith & Lambert, 2014 ) and attitudes (Nikou & Economides, 2018 ), as well as enhanced relationships with peers and teachers e.g., (Alrasheedi, Capretz, & Raza, 2015 ; Atmacasoy & Aksu, 2018 ).

Literature and systematic reviews focused on student engagement and technology do not always include information on where studies have been conducted. Out of 27 identified reviews (see Additional file 3 ), only 14 report the countries included, and two of these were explicitly focused on a specific region or country, namely Africa and Turkey. Most of the research has been conducted in the USA, followed by the UK, Taiwan, Australia and China. Table  1 depicts the three countries from which most studies originated from in the respective reviews, and highlights a clear lack of research conducted within mainland Europe, South America and Africa. Whilst this could be due to the choice of databases in which the literature was searched for, this nevertheless highlights a substantial gap in the literature, and to that end, it will be interesting to see whether this review is able to substantiate or contradict these trends.

Research into student engagement and educational technology has predominantly used a quantitative methodology (see Additional file 3 ), with 11 literature and systematic reviews reporting that surveys, particularly self-report Likert-scale, are the most used source of measurement (e.g. Henrie et al., 2015 ). Reviews that have included research using a range of methodologies, have found a limited number of studies employing qualitative methods (e.g. Connolly, Boyle, MacArthur, Hainey, & Boyle, 2012 ; Kay & LeSage, 2009 ; Lundin et al., 2018 ). This has led to a call for further qualitative research to be undertaken, exploring student engagement and technology, as well as more rigorous research designs e.g., (Li et al., 2017 ; Nikou & Economides, 2018 ), including sampling strategies, data collection, and in experimental studies in particular (Cheston et al., 2013 ; Connolly et al., 2012 ). However, not all reviews included information on methodologies used. Crook ( 2019 ), in his recent editorial in the British Journal of Educational Technology , stated that research methodology is a “neglected topic” (p. 487) within educational technology research, and stressed its importance in order to conduct studies delving deeper into phenomena (e.g. longitudinal studies).

Therefore, this article presents an initial “evidence map” (Miake-Lye, Hempel, Shanman, & Shekelle, 2016 ), p. 19 of systematically identified literature on student engagement and educational technology within higher education, undertaken through a systematic review, in order to address the issues raised by prior research, and to identify research gaps. These issues include the disparity between field of study and study levels researched, the geographical distribution of studies, the methodologies used, and the theoretical fuzziness surrounding student engagement. This article, however, is intended to provide an initial overview of the systematic review method employed, as well as an overview of the overall corpus. Further synthesis of possible correlations between student engagement and disengagement indicators with the co-occurrence of technology tools, will be undertaken within field of study specific articles (e.g., Bedenlier, 2020b ; Bedenlier 2020a ), allowing more meaningful guidance on applying the findings in practice.

The following research questions guide this enquiry:

How do the studies in the sample ground student engagement and align with theory?

Which indicators of cognitive, behavioural and affective engagement were identified in studies where educational technology was used? Which indicators of student disengagement?

What are the learning scenarios, modes of delivery and educational technology tools employed in the studies?

Overview of the study

With the intent to systematically map empirical research on student engagement and educational technology in higher education, we conducted a systematic review. A systematic review is an explicitly and systematically conducted literature review, that answers a specific question through applying a replicable search strategy, with studies then included or excluded, based on explicit criteria (Gough, Oliver, & Thomas, 2012 ). Studies included for review are then coded and synthesised into findings that shine light on gaps, contradictions or inconsistencies in the literature, as well as providing guidance on applying findings in practice. This contribution maps the research corpus of 243 studies that were identified through a systematic search and ensuing random parameter-based sampling.

Search strategy and selection procedure

The initial inclusion criteria for the systematic review were peer-reviewed articles in the English language, empirically reporting on students and student engagement in higher education, and making use of educational technology. The search was limited to records between 1995 and 2016, chosen due to the implementation of the first Virtual Learning Environments and Learning Management Systems within higher education see (Bond, 2018 ). Articles were limited to those published in peer-reviewed journals, due to the rigorous process under which they are published, and their trustworthiness in academia (Nicholas et al., 2015 ), although concerns within the scientific community with the peer-review process are acknowledged e.g. (Smith, 2006 ).

Discussion arose on how to approach the “hard-to-detect” (O’Mara-Eves et al., 2014 , p. 51) concept of student engagement in regards to sensitivity versus precision (Brunton, Stansfield, & Thomas, 2012 ), particularly in light of engagement being Henrie et al.’s ( 2015 ) most important search term. The decision was made that the concept ‘student engagement’ would be identified from titles and abstracts at a later stage, during the screening process. In this way, it was assumed that articles would be included, which indeed are concerned with student engagement, but which use different terms to describe the concept. Given the nature of student engagement as a meta-construct e.g. (Appleton et al., 2008 ; Christenson et al., 2012 ; Kahu, 2013 ) and by limiting the search to only articles including the term engagement , important research on other elements of student engagement might be missed. Hence, we opted for recall over precision. According to Gough et al. ( 2012 ), p. 13 “electronic searching is imprecise and captures many studies that employ the same terms without sharing the same focus”, or would lead to disregarding studies that analyse the construct but use different terms to describe it.

With this in mind, the search strategy to identify relevant studies was developed iteratively with support from the University Research Librarian. As outlined in O’Mara-Eves et al. ( 2014 ) as a standard approach, we used reviewer knowledge—in this case strongly supported through not only reviewer knowledge but certified expertise—and previous literature (e.g. Henrie et al., 2015 ; Kahu, 2013 ) to elicit concepts with potential importance under the topics student engagement, higher education and educational technology . The final search string (see Fig.  1 ) encompasses clusters of different educational technologies that were searched for separately in order to avoid an overly long search string. It was decided not to include any brand names, e.g. Facebook, Twitter, Moodle etc. because it was again reasoned that in scientific publication, the broader term would be used (e.g. social media). The final search string was slightly adapted, e.g. the format required for truncations or wildcards, according to the settings of each database being used Footnote 1 .

Final search terms used in the systematic review

Four databases (ERIC, Web of Science, Scopus and PsycINFO) were searched in July 2017 and three researchers and a student assistant screened abstracts and titles of the retrieved references between August and November 2017, using EPPI Reviewer 4.0. An initial 77,508 references were retrieved, and with the elimination of duplicate records, 53,768 references remained (see Fig.  2 ). A first cursory screening of records revealed that older research was more concerned with technologies that are now considered outdated (e.g. overhead projectors, floppy disks). Therefore, we opted to adjust the period to include research published between 2007 and 2016, labeled as a phase of research and practice, entitled ‘online learning in the digital age’ (Bond, 2018 ). Whilst we initially opted for recall over precision, the decision was then made to search for specific facets of the student engagement construct (e.g. deep learning, interest and persistence) within EPPI-Reviewer, in order to further refine the corpus. These adaptations led to a remaining 18,068 records.

Systematic review PRISMA flow chart (slightly modified after Brunton et al., 2012 , p. 86; Moher, Liberati, Tetzlaff, & Altman, 2009 ), p. 8

Four researchers screened the first 150 titles and abstracts, in order to iteratively establish a joint understanding of the inclusion criteria. The remaining references were distributed equally amongst the screening team, which resulted in the inclusion of 4152 potentially relevant articles. Given the large number of articles for screening on full text, whilst facing restrained time as a condition in project-based and funded work, it was decided that a sample of articles would be drawn from this corpus for further analysis. With the intention to draw a sample that estimates the population parameters with a predetermined error range, we used methods of sample size estimation in the social sciences (Kupper & Hafner, 1989 ). To do so, the R Package MBESS (Kelley, Lai, Lai, & Suggests, 2018 ) was used. Accepting a 5% error range, a percentage of a half and an alpha of 5%, 349 articles were sampled, with this sample being then stratified by publishing year, as student engagement has become much more prevalent (Zepke, 2018 ) and educational technology has become more differentiated within the last decade (Bond, 2018 ). Two researchers screened the first 100 articles on full text, reaching an agreement of 88% on inclusion/exclusion. The researchers then discussed the discrepancies and came to an agreement on the remaining 12%. It was decided that further comparison screening was needed, to increase the level of reliability. After screening the sample on full text, 232 articles remained for data extraction, which contained 243 studies.

Data extraction process

In order to extract the article data, an extensive coding system was developed, including codes to extract information on the set-up and execution of the study (e.g. methodology, study sample) as well as information on the learning scenario, the mode of delivery and educational technology used. Learning scenarios included broader pedagogies, such as social collaborative learning and self-determined learning, but also specific pedagogies such as flipped learning, given the increasing number of studies and interest in these approaches (e.g., Lundin et al., 2018 ). Specific examples of student engagement and/or disengagement were coded under cognitive, affective or behavioural (dis)engagement. The facets of student (dis)engagement were identified based on the literature review undertaken (see Additional file 2 ), and applied in this detailed manner to not only capture the overarching dimensions of the concept, but rather their diverse sub-meanings. New indicators also emerged during the coding process, which had not initially been identified from the literature review, including ‘confidence’ and ‘assuming responsibility’. The 243 studies were coded with this extensive code set and any disagreements that occurred between the coders were reconciled. Footnote 2

As a plethora of over 50 individual educational technology applications and tools were identified in the 243 studies, in line with results found in other large-scale systematic reviews (e.g., Lai & Bower, 2019 ), concerns were raised over how the research team could meaningfully analyse and report the results. The decision was therefore made to employ Bower’s ( 2016 ) typology of learning technologies (see Additional file  4 ), in order to channel the tools into groups that share the same characteristics or “structure of information” (Bower, 2016 ), p. 773. Whilst it is acknowledged that some of the technology could be classified into more than one type within the typology, e.g. wikis can be used in individual composition, for collaborative tasks, or for knowledge organisation and sharing, “the type of learning that results from the use of the tool is dependent on the task and the way people engage with it rather than the technology itself” therefore “the typology is presented as descriptions of what each type of tool enables and example use cases rather than prescriptions of any particular pedagogical value system” (Bower, 2016 ), p. 774. For further elaboration on each category, please see Bower ( 2015 ).

Study characteristics

Geographical characteristics.

The systematic mapping reveals that the 243 studies were set in 33 different countries, whilst seven studies investigated settings in an international context, and three studies did not indicate their country setting. In 2% of the studies, the country was allocated based on the author country of origin, if the two authors came from the same country. The top five countries account for 158 studies (see Fig.  3 ), with 35.4% ( n  = 86) studies conducted in the United States (US), 10.7% ( n  = 26) in the United Kingdom (UK), 7.8% ( n  = 19) in Australia, 7.4% ( n  = 18) in Taiwan, and 3.7% ( n  = 9) in China. Across the corpus, studies from countries employing English as the official or one of the official languages total up to 59.7% of the entire sample, followed by East Asian countries that in total account for 18.8% of the sample. With the exception of the UK, European countries are largely absent from the sample, only 7.3% of the articles originate from this region, with countries such as France, Belgium, Italy or Portugal having no studies and countries such as Germany or the Netherlands having one respectively. Thus, with eight articles, Spain is the most prolific European country outside of the UK. The geographical distribution of study settings also clearly shows an almost complete absence of studies undertaken within African contexts, with five studies from South Africa and one from Tunisia. Studies from South-East Asia, the Middle East, and South America are likewise low in number this review. Whilst the global picture evokes an imbalance, this might be partially due to our search and sampling strategy, having focused on English language journals, indexed in four primarily Western-focused databases.

Percentage deviation from the average relative frequencies of the different data collection formats per country (≥ 3 articles). Note. NS = not stated; AUS = Australia; CAN = Canada; CHN = China; HKG = Hong Kong; inter = international; IRI = Iran; JAP = Japan; MYS = Malaysia; SGP = Singapore; ZAF = South Africa; KOR = South Korea; ESP = Spain; SWE = Sweden; TWN = Taiwan; TUR = Turkey; GBR = United Kingdom; USA = United States of America

Methodological characteristics

Within this literature corpus, 103 studies (42%) employed quantitative methods, 84 (35%) mixed methods, and 56 (23%) qualitative. Relating these numbers back to the contributing countries, different preferences for and frequencies of methods used become apparent (see Fig. 3 ). As a general tendency, mixed methods and qualitative research occurs more often in Western countries, whereas quantitative research is the preferred method in East Asian countries. For example, studies originating from Australia employ mixed methods research 28% more often than the average, whereas Singapore is far below average in mixed methods research, with 34.5% less than the other countries in the sample. In Taiwan, on the other hand, mixed methods studies are being conducted 23.5% below average and qualitative research 6.4% less often than average. However, quantitative research occurs more often than in other countries, with 29.8% above average.

Amongst the qualitative studies, qualitative content analysis ( n  = 30) was the most frequently used analysis approach, followed by thematic analysis ( n  = 21) and grounded theory ( n  = 12). However, a lot of times ( n  = 37) the exact analysis approach was not reported, could not be allocated to a specific classification ( n  = 22), or no method of analysis was identifiable ( n  = 11). Within studies using quantitative methods, mean comparison was used in 100 studies, frequency data was collected and analysed in 83 studies, and in 40 studies regression models were used. Furthermore, looking at the correlation between the different analysis approaches, only one significant correlation can be identified, this being between mean comparison and frequency data (−.246). Besides that, correlations are small, for example, in only 14% of the studies both mean comparisons and regressions models are employed.

Study population characteristics

Research in the corpus focused on universities as the prime institution type ( n  = 191, 79%), followed by 24 (10%) non-specified institution types, and colleges ( n  = 21, 8.2%) (see Fig.  4 ). Five studies (2%) included institutions classified as ‘other’, and two studies (0.8%) included both college and university students. The most frequently studied student population was undergraduate students (60%, n  = 146), as opposed to 33 studies (14%) focused on postgraduate students (see Fig.  6 ). A combination of undergraduate and postgraduate students were the subject of interest in 23 studies (9%), with 41 studies (17%) not specifying the level of study of research participants.

Relative frequencies of study field in dependence of countries with ≥3 articles. Note. Country abbreviations are as per Figure 4. A&H = Arts & Humanities; BA&L = Business, Administration and Law; EDU = Education; EM&C = Engineering, Manufacturing & Construction; H&W = Health & Welfare; ICT = Information & Communication Technologies; ID = interdisciplinary; NS,M&S = Natural Science, Mathematics & Statistics; NS = Not specified; SoS = Social Sciences, Journalism & Information

Based on the UNESCO (2015) ISCED classification, eight broad study fields are covered in the sample, with Arts & Humanities (42 studies), Education (42 studies), and Natural Sciences, Mathematics & Statistics (37) being the top three study fields, followed by Health & Welfare (30 studies), Social Sciences, Journalism & Information (22), Business, Administration & Law (19 studies), Information & Communication Technologies (13), Engineering, Manufacturing & Construction (11), and another 26 studies of interdisciplinary character. One study did not specify a field of study.

An expectancy value was calculated, according to which, the distribution of studies per discipline should occur per country. The actual deviation from this value then showed that several Asian countries are home to more articles in the field of Arts & Humanities than was expected: Japan with 3.3 articles more, China with 5.4 and Taiwan with 5.9. Furthermore, internationally located research also shows 2.3 more interdisciplinary studies than expected, whereas studies on Social Sciences occur more often than expected in the UK (5.7 more articles) and Australia (3.3 articles) but less often than expected across all other countries. Interestingly, the USA have 9.9 studies less in Arts & Humanities than was expected but 5.6 articles more than expected in Natural Science.

Question One: How do the studies in the sample ground student engagement and align with theory?

Defining student engagement.

It is striking that almost all of the studies ( n  = 225, 93%) in this corpus lack a definition of student engagement, with only 18 (7%) articles attempting to define the concept. However, this is not too surprising, as the search strategy was set up with the assumption that researchers investigating student engagement (dimensions and indicators) would not necessarily label them as student engagement. When developing their definitions, authors in these 18 studies referenced 22 different sources, with the work of Kuh and colleagues e.g., (Hu & Kuh, 2002 ; Kuh, 2001 ; Kuh et al., 2006 ), as well as Astin ( 1984 ), the only authors referred to more than once. The most popular definition of student engagement within these studies was that of active participation and involvement in learning and university life e.g., (Bolden & Nahachewsky, 2015 ; bFukuzawa & Boyd, 2016 ), which was also found by Joksimović et al. ( 2018 ) in their review of MOOC research. Interaction, especially between peers and with faculty, was the next most prevalent definition e.g., (Andrew, Ewens, & Maslin-Prothero, 2015 ; Bigatel & Williams, 2015 ). Time and effort was given as a definition in four studies (Gleason, 2012 ; Hatzipanagos & Code, 2016 ; Price, Richardson, & Jelfs, 2007 ; Sun & Rueda, 2012 ), with expending physical and psychological energy (Ivala & Gachago, 2012 ) another definition. This variance in definitions and sources reflects the ongoing complexity of the construct (Zepke, 2018 ), and serves to reinforce the need for a clearer understanding across the field (Schindler et al., 2017 ).

Theoretical underpinnings

Reflecting findings from other systematic and literature reviews on the topic (Abdool, Nirula, Bonato, Rajji, & Silver, 2017 ; Hunsu et al., 2016 ; Kaliisa & Picard, 2017 ; Lundin et al., 2018 ), 59% ( n  = 100) of studies did not employ a theoretical model in their research. Of the 41% ( n  = 100) that did, 18 studies drew on social constructivism, followed by the Community of Inquiry model ( n  = 8), Sociocultural Learning Theory ( n  = 5), and Community of Practice models ( n  = 4). These findings also reflect the state of the field in general (Al-Sakkaf et al., 2019 ; Bond, 2019b ; Hennessy, Girvan, Mavrikis, Price, & Winters, 2018 ).

Another interesting finding of this research is that whilst 144 studies (59%) provided research questions, 99 studies (41%) did not. Although it is recognised that not all studies have research questions (Bryman, 2007 ), or only develop them throughout the research process, such as with grounded theory (Glaser & Strauss, 1967 ), a surprising number of quantitative studies (36%, n  = 37) did not have research questions. This is a reflection on the lack of theoretical guidance, as 30 of these 37 studies also did not draw on a theoretical or conceptual framework.

Question 2: which indicators of cognitive, behavioural and affective engagement were identified in studies where educational technology was used? Which indicators of student disengagement?

Student engagement indicators.

Within the corpus, the behavioural engagement dimension was documented in some form in 209 studies (86%), whereas the dimension of affective engagement was reported in 163 studies (67%) and the cognitive dimension in only 136 (56%) studies. However, the ten most often identified student engagement indicators across the studies overall (see Table  2 ) were evenly distributed over all three dimensions (see Table  3 ). The indicators participation/interaction/involvement , achievement and positive interactions with peers and teachers each appear in at least 100 studies, which is almost double the amount of the next most frequent student engagement indicator.

Across the 243 studies in the corpus, 117 (48%) showed all three dimensions of affective, cognitive and behavioural student engagement e.g., (Szabo & Schwartz, 2011 ), including six studies that used established student engagement questionnaires, such as the NSSE (e.g., Delialioglu, 2012 ), or self-developed addressing these three dimensions. Another 54 studies (22%) displayed at least two student engagement dimensions e.g., (Hatzipanagos & Code, 2016 ), including six questionnaire studies. Studies exhibiting one student engagement dimension only, was reported in 71 studies (29%) e.g., (Vural, 2013 ).

Student disengagement indicators

Indicators of student disengagement (see Table  4 ) were identified considerably less often across the corpus, which could be explained by the purpose of the studies being to primarily address/measure positive engagement, but on the other hand this could potentially be due to a form of self-selected or publication bias, due to less frequently reporting and/or publishing studies with negative results. The three disengagement indicators that were most often indicated were frustration ( n  = 33, 14%) e.g., (Ikpeze, 2007 ), opposition/rejection ( n  = 20, 8%) e.g., (Smidt, Bunk, McGrory, Li, & Gatenby, 2014 ) and disappointment e.g., (Granberg, 2010 ) , as well as other affective disengagement ( n  = 18, 7% each).

Technology tool typology and engagement/disengagement indicators

Across the 243 studies, a plethora of over 50 individual educational technology tools were employed. The top five most frequently researched tools were LMS ( n  = 89), discussion forums ( n  = 80), videos ( n  = 44), recorded lectures ( n  = 25), and chat ( n  = 24). Following a slightly modified version of Bower’s ( 2016 ) educational tools typology, 17 broad categories of tools were identified (see Additional file 4 for classification, and 3.2 for further information). The frequency with which tools from the respective groups employed in studies varied considerably (see Additional file 4 ), with the top five categories being text-based tools ( n  = 138), followed by knowledge organisation & sharing tools ( n  = 104), multimodal production tools ( n  = 89), assessment tools ( n  = 65) and website creation tools ( n  = 29).

Figure  5 shows what percentage of each engagement dimension (e.g., affective engagement or cognitive disengagement) was fostered through each specific technology type. Given the results in 4.2.1 on student engagement, it was somewhat unsurprising to see the prevalence of text-based tools , knowledge organisation & sharing tools, and multimodal production tools having the highest proportion of affective, behavioural and cognitive engagement. For example, affective engagement was identified in 163 studies, with 63% of these studies using text-based tools (e.g., Bulu & Yildirim, 2008 ) , and cognitive engagement identified in 136 studies, with 47% of those using knowledge organisation & sharing tools e.g., (Shonfeld & Ronen, 2015 ). However, further analysis of studies employing discussion forums (a text-based tool ) revealed that, whilst the top affective and behavioural engagement indicators were found in almost two-thirds of studies (see Additional file  5 ), there was a substantial gap between that and the next most prevalent engagement indicator, with the exact pattern (and indicators) emerging for wikis. This represents an area for future research.

Engagement and disengagement by tool typology. Note. TBT = text-based tools; MPT = multimodal production tools; WCT = website creation tools; KO&S = knowledge organisation and sharing tools; DAT = data analysis tools; DST = digital storytelling tools; AT = assessment tools; SNT = social networking tools; SCT = synchronous collaboration tools; ML = mobile learning; VW = virtual worlds; LS = learning software; OL = online learning; A&H = Arts & Humanities; BA&L = Business, Administration and Law; EDU = Education; EM&C = Engineering, Manufacturing & Construction; H&W = Health & Welfare; ICT = Information & Communication Technologies; ID = interdisciplinary; NS,M&S = Natural Science, Mathematics & Statistics; NS = Not specified; SoS = Social Sciences, Journalism & Information

Interestingly, studies using website creation tools reported more disengagement than engagement indicators across all three domains (see Fig.  5 ), with studies using assessment tools and social networking tools also reporting increased instances of disengagement across two domains (affective and cognitive, and behavioural and cognitive respectively). 23 of the studies (79%) using website creation tools , used blogs, with students showing, for example, disinterest in topics chosen e.g., (Sullivan & Longnecker, 2014 ), anxiety over their lack of blogging knowledge and skills e.g., (Mansouri & Piki, 2016 ), and continued avoidance of using blogs in some cases, despite introductory training e.g., (Keiller & Inglis-Jassiem, 2015 ). In studies where assessment tools were used, students found timed assessment stressful, particularly when trying to complete complex mathematical solutions e.g., (Gupta, 2009 ), as well as quizzes given at the end of lectures, with some students preferring take-up time of content first e.g., (DePaolo & Wilkinson, 2014 ). Disengagement in studies where social networking tools were used, indicated that some students found it difficult to express themselves in short posts e.g., (Cook & Bissonnette, 2016 ), that conversations lacked authenticity e.g., (Arnold & Paulus, 2010 ), and that some did not want to mix personal and academic spaces e.g., (Ivala & Gachago, 2012 ).

Question 3: What are the learning scenarios, modes of delivery and educational technology tools employed in the studies?

Learning scenarios.

With 58.4% across the sample, social-collaborative learning (SCL) was the scenario most often employed ( n  = 142), followed by 43.2% of studies investigating self-directed learning (SDL) ( n  = 105) and 5.8% of studies using game-based learning (GBL) ( n  = 14) (see Fig. 6 ). Studies coded as SCL included those exploring social learning (Bandura, 1971 ) and social constructivist approaches (Vygotsky, 1978 ). Personal learning environments (PLE) were found for 2.9% of studies, 1.3% studies used other scenarios ( n  = 3), whereas another 13.2% did not provide specification of their learning scenarios ( n  = 32). It is noteworthy that in 45% of possible cases for employing SDL scenarios, SCL was also used. Other learning scenarios were also used mostly in combination with SCL and SDL. Given the rising number of higher education studies exploring flipped learning (Lundin et al., 2018 ), studies exploring the approach were also specifically coded (3%, n  = 7).

Co-occurrence of learning scenarios across the sample ( n  = 243). Note. SDL = self-directed learning; SCL = social collaborative learning; GBL = game-based learning; PLE = personal learning environments; other = other learning scenario

Modes of delivery

In 84% of studies ( n  = 204), a single mode of delivery was used, with blended learning the most researched (109 studies), followed by distance education (72 studies), and face-to-face instruction (55 studies). Of the remaining 39 studies, 12 did not indicate their mode of delivery, whilst the other 27 studies combined or compared modes of delivery, e.g. comparing face to face courses to blended learning, such as the study on using iPads in undergraduate nursing education by Davies ( 2014 ).

Educational technology tools investigated

Most studies in this corpus (55%) used technology asynchronously, with 12% of studies researching synchronous tools, and 18% of studies using both asynchronous and synchronous. When exploring the use of tools, the results are not surprising, with a heavy reliance on asynchronous technology. However, when looking at tool usage with studies in face-to-face contexts, the number of synchronous tools (31%) is almost as many as the number of asynchronous tools (41%), and surprisingly low within studies in distance education (7%).

Tool categories were used in combination, with text-based tools most often used in combination with other technology types (see Fig.  7 ). For example, in 60% of all possible cases using multimodal production tools, in 69% of all possible synchronous production tool cases, in 72% of all possible knowledge, organisation & sharing tool cases , and a striking 89% of all possible learning software cases and 100% of all possible MOOC cases. On the contrary, text-based tools were never used in combination with games or data analysis tools . However, studies using gaming tools were used in 67% of possible assessment tool cases as well. Assessment tools, however, constitute somewhat of a special case when studies using website creation tools are concerned, with only 7% of possible cases having employed assessment tools .

Co-occurrence of tools across the sample ( n  = 243). Note. TBT = text-based tools; MPT = multimodal production tools; WCT = website creation tools; KO&S = knowledge organisation and sharing tools; DAT = data analysis tools; DST = digital storytelling tools; AT = assessment tools; SNT = social networking tools; SCT = synchronous collaboration tools; ML = mobile learning; VW = virtual worlds; LS = learning software; OL = online learning

In order to gain further understanding into how educational technology was used, we examined how often a combination of two variables should occur in the sample and how often it actually occurs, with deviations described as either ‘more than’ or ‘less than’ the expected value. This provides further insight into potential gaps in the literature, which can inform future research. For example, an analysis of educational technology tool usage amongst study populations (see Fig.  8 ) reveals that 5.0 more studies than expected looked at knowledge organisation & sharing for graduate students, but 5.0 studies less than expected investigated assessment tools for this group. By contrast, 5 studies more than expected researched assessment tools for unspecified study levels, and 4.3 studies less than expected employed knowledge organisation & sharing for undergraduate students.

Relative frequency of educational technology tools used according to study level Note. Abbreviations are explained in Fig. 7

Educational technology tools were also used differently from the expected pattern within various fields of study (see Fig.  9 ), most obviously for the cases of the top five tools. However, also for virtual worlds, found in 5.8 studies more in Health & Welfare than expected, and learning software, used in 6.4 studies more in Arts & Humanities than expected. In all other disciplines, learning software was used less often than assumed. Text-based tools were used more often than expected in fields of study that are already text-intensive, including Arts & Humanities, Education, Business, Administration & Law as well as Social Sciences - but less often than thought in fields such as Engineering, Health & Welfare, and Natural Sciences, Mathematics & Statistics. Multimodal production tools were used more often only in Health & Welfare, ICT and Natural Sciences, and less often than assumed across all other disciplines. Assessment tools deviated most clearly, with 11.9 studies more in Natural Sciences, Mathematics & Statistics than assumed, but with 5.2 studies less in both Education and Arts & Humanities.

Relative frequency of educational technology tools used according to field of study. Note. TBT = text-based tools; MPT = multimodal production tools; WCT = website creation tools; KO&S = knowledge organisation and sharing tools; DAT = data analysis tools; DST = digital storytelling tools; AT = assessment tools; SNT = social networking tools; SCT = synchronous collaboration tools; ML = mobile learning; VW = virtual worlds; LS = learning software; OL = online learning

In regards to mode of delivery and educational technology tools used, it is interesting to see that from the five top tools, except for assessment tools , all tools were used in face-to-face instruction less often than expected (see Fig.  10 ); from 1.6 studies less for website creation tools to 14.5 studies less for knowledge organisation & sharing tools . Assessment tools , however, were used in 3.3 studies more than expected - but less often than assumed (although moderately) in blended learning and distance education formats. Text-based tools, multimodal production tools and knowledge organisation & sharing tools were employed more often than expected in blended and distance learning, especially obvious in 13.1 studies more on t ext-based tools and 8.2 studies on knowledge organisation & sharing tools in distance education. Contrary to what one would perhaps expect, social networking tools were used in 4.2 studies less than expected for this mode of delivery.

Relative frequency of educational technology tools used according mode of delivery. Note. Tool abbreviations as per Figure 10. BL = Blended learning; DE = Distance education; F2F = Face-to-face; NS = Not stated

The findings of this study confirm those of previous research, with the most prolific countries being the US, UK, Australia, Taiwan and China. This is rather representative of the field, with an analysis of instructional design and technology research from 2007 to 2017 listing the most productive countries as the US, Taiwan, UK, Australia and Turkey (Bodily, Leary, & West, 2019 ). Likewise, an analysis of 40 years of research in Computers & Education (CAE) found that the US, UK and Taiwan accounted for 49.9% of all publications (Bond, 2018 ). By contrast, a lack of African research was apparent in this review, which is also evident in educational technology research in top tier peer-reviewed journals, with only 4% of articles published in the British Journal of Educational Technology ( BJET ) in the past decade (Bond, 2019b ) and 2% of articles in the Australasian Journal of Educational Technology (AJET) (Bond, 2018 ) hailing from Africa. Similar results were also found in previous literature and systematic reviews (see Table 1 ), which again raises questions of literature search and inclusion strategies, which will be further discussed in the limitations section.

Whilst other reviews of educational technology and student engagement have found studies to be largely STEM focused (Boyle et al., 2016 ; Li et al., 2017 ; Lundin et al., 2018 ; Nikou & Economides, 2018 ), this corpus features a more balanced scope of research, with the fields of Arts & Humanities (42 studies, 17.3%) and Education (42 studies, 17.3%) constituting roughly one third of all studies in the corpus - and Natural Sciences, Mathematics & Statistics, nevertheless, assuming rank 3 with 38 studies (15.6%). Beyond these three fields, further research is needed within underrepresented fields of study, in order to gain more comprehensive insights into the usage of educational technology tools (Kay & LeSage, 2009 ; Nikou & Economides, 2018 ).

Results of the systematic map further confirm the focus that prior educational technology research has placed on undergraduate students as the target group and participants in technology-enhanced learning settings e.g. (Cheston et al., 2013 ; Henrie et al., 2015 ). With the overwhelming number of 146 studies researching undergraduate students—compared to 33 studies on graduate students and 23 studies investigating both study levels—this also indicates that further investigation into the graduate student experience is needed. Furthermore, the fact that 41 studies do not report on the study level of their participants is an interesting albeit problematic fact, as implications might not easily be drawn for application to one’s own specific teaching context if the target group under investigation is not clearly denominated. A more precise reporting of participants’ details, as well as specification of the study context (country, institution, study level to name a few) is needed to transfer and apply study results to practice—being then able to take into account why some interventions succeed and others do not.

In line with other studies e.g. (Henrie et al., 2015 ), this review has also demonstrated that student engagement remains an under-theorised concept, that is often only considered fragmentally in research. Whilst studies in this review have often focused on isolated aspects of student engagement, their results are nevertheless interesting and valuable. However, it is important to relate these individual facets to the larger framework of student engagement, by considering how these aspects are connected and linked to each other. This is especially helpful to integrate research findings into practice, given that student engagement and disengagement are rarely one-dimensional; it is not enough to focus only on one aspect of engagement, but also to look at aspects that are adjacent to it (Pekrun & Linnenbrink-Garcia, 2012 ). It is also vital, therefore, that researchers develop and refine an understanding of student engagement, and make this explicit in their research (Appleton et al., 2008 ; Christenson et al., 2012 ).

Reflective of current conversations in the field of educational technology (Bond, 2019b ; Castañeda & Selwyn, 2018 ; Hew et al., 2019 ), as well as other reviews (Abdool et al., 2017 ; Hunsu et al., 2016 ; Kaliisa & Picard, 2017 ; Lundin et al., 2018 ), a substantial number of studies in this corpus did not have any theoretical underpinnings. Kaliisa and Picard ( 2017 ) argue that, without theory, research can result in disorganised accounts and issues with interpreting data, with research effectively “sit[ting] in a void if it’s not theoretically connected” (Kara, 2017 ), p. 56. Therefore, framing research in educational technology with a stronger theoretical basis, can assist with locating the “field’s disciplinary alignment” (Crook, 2019 ), p. 486 and further drive conversations forward.

The application of methods in this corpus was interesting in two ways. First, it is noticeable that quantitative studies are prevalent across the 243 articles in the sample. The number of studies employing qualitative research methods in the sample was comparatively low (56 studies as opposed to 84 mixed method studies and 103 quantitative studies). This is also reflected in the educational technology field at large, with a review of articles published in BJET and Educational Technology Research & Development (ETR&D) from 2002 to 2014 revealing that 40% of articles used quantitative methods, 26% qualitative and 13% mixed (Baydas, Kucuk, Yilmaz, Aydemir, & Goktas, 2015 ), and likewise a review of educational technology research from Turkey 1990–2011 revealed that 53% of articles used quantitative methods, 22% qualitative and 10% mixed methods (Kucuk, Aydemir, Yildirim, Arpacik, & Goktas, 2013 ). Quantitative studies primarily show that an intervention has worked or not when applied to e.g. a group of students in a certain setting as done in the study on using mobile apps on student performance in engineering education by Jou, Lin, and Tsai ( 2016 ), however, not all student engagement indicators can actually be measured in this way. The lower numbers of affective and cognitive engagement found in the studies in the corpus, reflect a wider call to the field to increase research on these two domains (Henrie et al., 2015 ; Joksimović et al., 2018 ; O’Flaherty & Phillips, 2015 ; Schindler et al., 2017 ). Whilst it is arguably more difficult to measure these two than behavioural engagement, the use of more rigorous and accurate surveys could be one possibility, as they can “capture unobservable aspects” (Henrie et al., 2015 ), p. 45 such as student feelings and information about the cognitive strategies they employ (Finn & Zimmer, 2012 ). However, they are often lengthy and onerous, or subject to the limitations of self-selection.

Whereas low numbers of qualitative studies researching student engagement and educational technology were previously identified in other student engagement and technology reviews (Connolly et al., 2012 ; Kay & LeSage, 2009 ; Lundin et al., 2018 ), it is studies like that by Lopera Medina ( 2014 ) in this sample, which reveal how people perceive this educational experience and the actual how of the process. Therefore, more qualitative and ethnographic measures should also be employed, such as student observations with thick descriptions, which can help shed light on the complexity of teaching and learning environments (Fredricks et al., 2004 ; Heflin, Shewmaker, & Nguyen, 2017 ). Conducting observations can be costly, however, both in time and money, so this is suggested in combination with computerised learning analytic data, which can provide measurable, objective and timely insight into how certain manifestations of engagement change over time (Henrie et al., 2015 ; Ma et al., 2015 ).

Whereas other results of this review have confirmed previous results in the field, the technology tools that were used in the studies and considered in their relation to student engagement in this corpus deviate. Whilst Henrie et al. ( 2015 ) found that the most frequently researched tools were discussion forums, general websites, LMS, general campus software and videos, the studies here focused predominantly on LMS, discussion forums, videos, recorded lectures and chat. Furthermore, whilst Schindler et al. ( 2017 ) found that digital games, web-conferencing software and Facebook were the most effective tools at enhancing student engagement, this review found that it was rather text-based tools , knowledge organisation & sharing , and multimodal production tools .

Limitations

During the execution of this systematic review, we tried to adhere to the method as rigorously as possible. However, several challenges were also encountered - some of which are addressed and discussed in another publication (Bedenlier, 2020b ) - resulting in limitations to this study. Four large, general educational research databases were searched, which are international in scope. However, by applying the criterion of articles published in English, research published on this topic in languages other than English was not included in this review. The same applies to research documented in, for example, grey literature, book chapters or monographs, or even articles from journals that are not indexed in the four databases searched. Another limitation is that only research published within the period 2007–2016 was investigated. Whilst we are cognisant of this being a restriction, we also think that the technological advances and the implications to be drawn from this time-frame relate more meaningfully to the current situation, than would have been the case for technologies used in the 1990s see (Bond, 2019b ). The sampling strategy also most likely accounts for the low number of studies from certain countries, e.g. in South America and Africa.

Studies included in this review represent various academic fields, and they also vary in the rigour with which they were conducted. Harden and Gough ( 2012 ) stress that the appraisal of quality and relevance of studies “ensure[s] that only the most appropriate, trustworthy and relevant studies are used to develop the conclusions of the review” (p. 154), we have included the criterion of being a peer reviewed contribution as a formal inclusion criterion from the beginning. In doing so, we reason that studies met a baseline of quality as applicable to published research in a specific field - otherwise they would not have been accepted for publication by the respective community. Finally, whilst the studies were diligently read and coded, and disagreements also discussed and reconciled, the human flaw of having overlooked or misinterpreted information provided in the individual articles cannot fully be excluded.

Finally, the results presented here provide an initial window into the overall body of research identified during the search, and further research is being undertaken to provide deeper insight into discipline specific use of technology and resulting student engagement using subsets of this sample (Bedenlier, 2020a ; Bond, M., Bedenlier, S., Buntins, K., Kerres, M., & Zawacki-Richter, O.: Facilitating student engagement through educational technology: A systematic review in the field of education, forthcoming).

Recommendations for future work and implications for practice

Whilst the evidence map presented in this article has confirmed previous research on the nexus of educational technology and student engagement, it has also elucidated a number of areas that further research is invited to address. Although these findings are similar to that of previous reviews, in order to more fully and comprehensively understand student engagement as a multi-faceted construct, it is not enough to focus only on indicators of engagement that can easily be measured, but rather the more complex endeavour of uncovering and investigating those indicators that reside below the surface. This also includes the careful alignment of theory and methodological design, in order to both adequately analyse the phenomenon under investigation, as well as contributing to the soundly executed body of research within the field of educational technology. Further research is invited in particular into how educational technology affects cognitive and affective engagement, whilst considering how this fits within the broader sociocultural framework of engagement (Bond, 2019a ). Further research is also invited into how educational technology affects student engagement within fields of study beyond Arts & Humanities, Education and Natural Sciences, Mathematics & Statistics, as well as within graduate level courses. The use of more qualitative research methods is particularly encouraged.

The findings of this review suggest that research gaps exist with particular combinations of tools, study levels and modes of delivery. With respect to study level, the use of assessment tools with graduate students, as well as knowledge organisation & sharing tools with undergraduate students, are topics researched far less than expected. The use of text-based tools in Engineering, Health & Welfare and Natural Sciences, Mathematics & Statistics, as well as the use of multimodal production tools outside of these disciplines, are also areas for future research, as is the use of assessment tools in the fields of Education and Arts & Humanities in particular.

With 109 studies in this systematic review using a blended learning design, this is a confirmation of the argument that online distance education and traditional face-to-face education are becoming increasingly more integrated with one another. Whilst this indicates that a lot of educators have made the move from face-to-face teaching to technology-enhanced learning, this also makes a case for the need for further professional development, in order to apply these tools effectively within their own teaching contexts, with this review indicating that further research is needed in particlar into the use of social networking tools in online/distance education. The question also needs to be asked, not only why the number of published studies are low within certain countries and regions, but also to enquire into the nature of why that is the case. This entails questioning the conditions under which research is being conducted, potentially criticising publication policies of major, Western-based journals, but also ultimately to reflect on one’s search strategy and research assumptions as a Western educator-researcher.

Based on the findings of this review, educators within higher education institutions are encouraged to use text-based tools , knowledge, organisation and sharing tools , and multimodal production tools in particular and, whilst any technology can lead to disengagement if not employed effectively, to be mindful that website creation tools (blogs and ePortfolios), social networking tools and assessment tools have been found to be more disengaging than engaging in this review. Therefore, educators are encouraged to ensure that students receive sufficient and ongoing training for any new technology used, including those that might appear straightforward, e.g. blogs, and that they may require extra writing support. Ensure that discussion/blog topics are interesting, that they allow student agency, and they are authentic to students, including the use of social media. Social networking tools that augment student professional learning networks are particularly useful. Educators should also be aware, however, that some students do not want to mix their academic and personal lives, and so the decision to use certain social platforms could be decided together with students.

Availability of data and materials

All data will be made publicly available, as part of the funding requirements, via https://www.researchgate.net/project/Facilitating-student-engagement-with-digital-media-in-higher-education-ActiveLeaRn .

The detailed search strategy, including the modified search strings according to the individual databases, can be retrieved from https://www.researchgate.net/project/Facilitating-student-engagement-with-digital-media-in-higher-education-ActiveLeaRn

The full code set can be retrieved from the review protocol at https://www.researchgate.net/project/Facilitating-student-engagement-with-digital-media-in-higher-education-ActiveLeaRn .

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The authors thank the two student assistants who helped during the article retrieval and screening stage.

This research resulted from the ActiveLearn project, funded by the Bundesministerium für Bildung und Forschung (BMBF-German Ministry of Education and Research) [grant number 16DHL1007].

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All authors contributed to the design and conceptualisation of the systematic review. MB, KB and SB conducted the systematic review search and data extraction. MB undertook the literature review on student engagement and educational technology, co-wrote the method, results, discussion and conclusion section. KB designed and executed the sampling strategy and produced all of the graphs and tables, as well as assisted with the formulation of the article. SB co-wrote the method, results, discussion and conclusion sections, and proof read the introduction and literature review sections. All authors read and approved the final manuscript.

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Literature reviews (LR) and systematic reviews (SR) on student engagement

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Indicators of engagement and disengagement

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Literature reviews (LR) and systematic reviews (SR) on student engagement and technology in higher education (HE)

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Educational technology tool typology based on Bower ( 2016 ) and Educational technology tools used

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Text-based tool examples by engagement domain

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Bond, M., Buntins, K., Bedenlier, S. et al. Mapping research in student engagement and educational technology in higher education: a systematic evidence map. Int J Educ Technol High Educ 17 , 2 (2020). https://doi.org/10.1186/s41239-019-0176-8

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Facilitating Student Engagement Through Educational Technology: Towards a Conceptual Framework

• Melissa Bond
• Svenja Bedenlier

The concept of student engagement has become somewhat of an enigma for educators and researchers, with ongoing discussions about its nature and complexity, and criticism about the depth and breadth of theorising and operationalisation within empirical research. This equally applies to research conducted in the field of educational technology and its application in schools and higher education. Recognising the inherent role that technology now plays in education, and the potential it has to engage students, this paper draws on a range of student engagement literature and conceptualises a provisional bioecological framework of student engagement that explicitly includes technology as one influential factor. This paper first proposes a definition of student engagement and provides an exploration of positive student engagement indicators. It then presents a bioecological framework, and the microsystemic facets of technology, teacher and curriculum are further explored in their relation to fostering student engagement. Based on this framework, implications for further theory-based research into student engagement and its relation to educational technology are discussed and recommendations for educators are given.

• student engagement
• educational technology
• theoretical framework
• bioecological model
• higher education

Introduction

The concept of student engagement has become somewhat of an enigma for educators and researchers, with ongoing discussions about its nature and complexity, and criticism about the depth and breadth of theorising and operationalisation within empirical research (e.g. Kahn, 2014 ; Zepke, 2018a ). The role that digital technology plays in affecting student engagement is a particular area of interest, as it has become a central feature within the student educational experience ( Henderson, Selwyn and Aston, 2017 ; Selwyn, 2016 ). Recognition is growing of the importance of digital literacy and information and communications technology (ICT) skills (Organisation for Economic Co-operation and Development [OECD], 2015 ; Redecker, 2017 ), as is evidence of technology’s potential to increase self-efficacy, self-regulation and involvement within the wider educational community ( Alioon and Delialioğlu, 2019 ; Junco, 2012 ). The field of educational technology has, however, lacked theoretical guidance ( Antonenko, 2015 ; Karabulut-Ilgu, Jaramillo Cherrez and Jahren, 2018 ), with the operationalisation and understanding of student engagement being a particular issue ( Henrie, Halverson and Graham, 2015 ). Calls have been made, therefore, for a strengthening of theoretical understanding and the use of theory within empirical research in the field (e.g. Hennessy et al. 2019 ; Hew et al. 2019 ), as well as for further understanding of how educational technology can affect student engagement in particular (e.g. Castañeda and Selwyn, 2018 ; Nelson Laird and Kuh, 2005 ). Although recent efforts have investigated the interplay of engagement and educational technology, these have been limited to informal learning contexts (e.g. MOOCs, see Joksimović et al. 2018 ) and online learning in higher education (e.g. Redmond et al. 2018 ).

This paper forms part of the first author’s PhD by publication, which is an exploration into the complexity of the ever-evolving concept of student engagement, in an effort to gain further understanding of how technology interacts with and affects aspects of the learning environment in both school and higher education contexts. It also forms the theoretical basis of a larger research project on student engagement and technology in higher education. 1 The present paper presents a bioecological student engagement framework developed by the first author, in order to guide and ground further research on this complex topic. The model includes influences on student engagement at the macro, exo, meso and micro levels, with a particular focus on the microsystem – the student’s immediate learning environment – as this is where practitioners are able to exert the most influence. Recommendations are then provided on how the framework can be used by practitioners, and how it can help improve practice.

What is student engagement?

Student engagement has long been recognised as an enigmatic and multifaceted meta-construct ( Appleton, Christenson and Furlong, 2008 ; Fredricks, Blumenfeld and Paris, 2004 ), with seminal works such as Astin’s ( 1999 ) theory of involvement and Kahu’s ( 2013 ; Kahu and Nelson, 2018 ) sociocultural conceptualisation of engagement, influencing ongoing conversations about the nature of and research into engagement (e.g. Boekaerts, 2016 ; Eccles, 2016 ). Often confused with motivation, which is seen as an antecedent and the force that energises behaviour ( Lim, 2004 ; Reschly and Christenson, 2012 ), engagement is defined as:

The energy and effort that students employ within their learning community, observable via any number of behavioural, cognitive or affective indicators across a continuum. It is shaped by a range of structural and internal influences, including the complex interplay of relationships, learning activities and the learning environment. The more students are engaged and empowered within their learning community, the more likely they are to channel that energy back into their learning, leading to a range of short and long term outcomes, that can likewise further fuel engagement. ( Bond et al. Manuscript in preparation: 2–3 )

This definition arose in part out of literature stressing the importance of agentic engagement ( Reeve, 2012 ; Reeve and Tseng, 2011 ); the more students have a say within their learning environment, the more engagement and achievement are likely to increase ( Peters et al. 2019 ; Reeve, 2013 ; Zepke, 2018b ), the more likely they are then to feedback positively into the learning environment ( Matos et al. 2018 ). The concept of social engagement ( Finn and Zimmer, 2012 ; Linnenbrink-Garcia, Rogat and Koskey, 2011 ), where students’ affect is influenced by social elements within the learning environment, is also represented within the acknowledgement of social, alongside internal, influences.

Dimensions and indicators of student engagement

Cognitive, affective and behavioural engagement are the three widely accepted dimensions of student engagement ( Fredricks et al. 2004 ; Fredricks, Filsecker and Lawson, 2016 ). Cognitive engagement relates to deep learning strategies, self-regulation and understanding; affective engagement relates to positive reactions to the learning environment, peers and teachers, as well as their sense of belonging and interest; and behavioural engagement relates to participation, persistence and positive conduct. However, each dimension of engagement comprises a range of indicators (see Table 1 ), experienced on a continuum at varying times ( Coates, 2007 ; Payne, 2017 ), depending on their activation (low or high) and valence (positive or negative) ( Pekrun and Linnenbrink-Garcia, 2012 ). The term ‘indicators’ is used here, following the use by Fredricks et al. ( 2004 ), and is understood in the sense of indicating or being a manifestation of student engagement and is expressed—and eventually observable and measurable—through cognitive, affective or behavioural action or reaction. The authors do, however, acknowledge that sometimes these are referred to as ‘facets’ of engagement (e.g. Coates, 2009 ). It is also important to note that, although not discussed at length in the present paper, disengagement needs to be included as well, when talking about engagement; not necessarily as a distinct concept, but rather as residing on the other side of a continuum of (dis)engagement, expressed either as an active action of disengaging from a learning context or even as a character trait (e.g. Chipchase et al. 2017 ).

Indicators of student engagement (Adapted from Bond et al. Manuscript in preparation ).

Sociocultural positioning of student engagement

Engagement does not occur in a vacuum; rather, it is impacted and influenced by many contextual factors, and it is vital that these wider influences be considered when exploring student engagement ( Appleton et al. 2008 ; Kahu, 2013 ; Quin, 2017 ). Within her conceptual framework of student engagement in higher education, Kahu ( 2013, p. 766 ) differentiated between sociocultural influences, such as the political and social environment; structural influences, such as the university context and student background; and psychosocial influences, such as the teaching environment, teacher-student relationships and student motivation. By considering the wider sociopolitical context that influences student engagement, a more holistic and clearer understanding of the concept can be gained, which allows educators more insight into how to further build engagement and ultimately improve outcomes for students ( Appleton et al. 2008 ). Kahu’s framework has been criticised, however, for a lack of clear focus on what students were engaging with ( Ashwin and McVitty, 2015 ), which resulted in a revised framework emphasising the ‘educational interface’ ( Kahu and Nelson, 2018 ). However, given the emphasis that has been placed on the possibility of technology playing a formative role in student engagement ( Coates, 2007 ; Nelson Laird and Kuh, 2005 ; Schindler et al. 2017 ), further theorising of how technology fits within a framework of engagement is warranted.

Bronfenbrenner and colleagues (e.g. Bronfenbrenner, 1979 , 1986 ; Bronfenbrenner and Ceci, 1994 ) developed a bioecological model of external influences affecting families and child development, used to guide a range of research on child learning and parent engagement (e.g. Ansong et al. 2017 ; Heatly and Votruba-Drzal, 2018 ). This model has been particularly useful in educational practice, as it provides a conceptual framework for understanding how multiple settings and actors influence students at the same time (e.g. Sontag, 1996 ). Nested within a system of intertwined milieus, the individual student sits at the centre of the microsystem, which encompasses their immediate setting, e.g. classroom, or home. The mesosystem level represents the interactions between microsystems, as well as between the micro and exosystems. The exosystem includes the wider social structures that impact on the learner, such as educational institutions, the media, government, the world of work and social services, and the macrosystem encompasses the wider economic, social, legal, political and educational systems in which the other systems are located. This model was used, in conjunction with Schwab’s ( 1973 ) framework of curriculum redevelopment, to develop a bioecological model of influences on student engagement, as the theoretical framework for a case study on flipped learning in secondary classrooms ( Bond, 2019 ). The interconnected dimensions of curriculum, students, teachers and milieus (school, classrooms, family/parents, community) within Schwab’s ( 1973 ) framework, as well as the inclusion of technology by Willis et al. ( 2018 ) in their study of parent engagement with their child’s learning, allowed the first author to visualise more easily the interconnected, fluid relationship between the external influences on student engagement. This model is a vehicle through which to explore and visualise further how technology affects student engagement.

Bioecological student engagement framework

There are a range of structural and psychosocial influences that affect the learning environment, learning processes, student engagement and subsequent outcomes at all levels of the bioecological model (see Figure 1 ). Drawing on educational technology literature from two systematic reviews ( Bond, Manuscript in preparation ; Bond et al. Manuscript in preparation ), as well as wider literature, technological influences on student engagement are examined at each of the macro, exo, meso and microsystem levels.

Bioecological model of influences on student engagement, based on Bond ( 2019 ) and adapted from Bronfenbrenner and colleagues ( Bronfenbrenner, 1979 , 1986 ; Bronfenbrenner and Ceci, 1994 ).

Macrosystem

The rapid onset of digitalisation is having, and will continue to have, a profound effect on governmental policy and educational institutions ( EDUCAUSE, 2018 ). Each country is reacting to digital transformation in different ways, with some, e.g., Germany (see Bond et al. 2018 ), investing heavily in research and development, including specific funding calls for research projects. The German government sponsored higher education think tank, Hochschulforum Digitalisierung, has recognised that “the use of digital media contributes to the improvement of higher education teaching”; however, “there is no shortage of digital teaching and learning innovations at universities but their structural and strategic advancement is deficient” ( Hochschulforum Digitalisierung, 2016: n.p. ). Therefore, funding is being provided by the Bundesministerium für Bildung und Forschung (BMBF – German Ministry of Education and Research) on the topics of ‘Adaptive learning and assessment environments’, ‘Interactivity and multimediality of digital learning environments’, ‘Researching theory and practice in digital learning environments’, and digitalisation in higher education ( Bundesministerium für Bildung und Forschung, Referat Digitaler Wandel in der Bildung, 2018 ), alongside peer-to-peer coaching for institution leaders and educators, to implement digital learning strategies and develop technological pedagogical skills. These projects will inform teaching and learning, and influence technology integration (infrastructure) and application (within the classroom) ( Hochschulforum Digitalisierung, 2016 ).

In Australia, digitalisation has meant the introduction of a National Broadband Network (NBN), in an attempt to “bridge the digital divide” ( NBN Co., 2018: 2 ), as well as boost the national gross domestic product. However, the process has been marred by cost blowouts ( Tucker, 2015 ) and delays ( Alizadeh, 2017 ), with Australia still lagging well behind other nations in Internet speed, ranked 50th in the world ( Akamai, 2017 ). This has had implications for families, especially those in rural areas where the NBN has yet to roll out and/or who cannot afford to buy credit on pre-paid Internet dongles or mobile phones. For example, within a case study on the flipped learning approach in rural South Australia ( Bond, 2019 ), a lack of access to the NBN has contributed to reduced parent engagement with students’ learning and within the school community, as well as having had a direct impact on students’ ability to engage with their learning.

Institutions that develop a culture of student success, with high expectations of both students and staff, and that invest in support services and infrastructure, such as reliable Internet connections and technology (e.g. desktop computers, wifi repeaters), are far more likely to promote positive student engagement ( Almarghani and Mijatovic, 2017 ; Peters et al. 2019 ; Umbach and Wawrzynski, 2005 ; Zepke, 2018a ). Institutional leadership and attitudes have a direct bearing on student learning, as well as on teacher attitudes towards using educational technology ( Cheng and Weng, 2017 ). This includes institutional policies on teacher professional development and the expectation of technology use within teaching and learning ( Gerick, Eickelmann and Bos, 2017 ), policies about staffing of classes ( Hill and Tyson, 2009 ), which may impede the development of effective relationships between educators, students and their families, as well as policies on student technology use, such as Bring Your Own Device (BYOD) programs ( Adhikari, Mathrani and Scogings, 2016 ). It is particularly important to remain cognisant of potential digital divide issues ( Adams Becker et al. 2018 ), including student ownership and use of devices that are incompatible with institutional devices, as this can impact participation and engagement ( Bond, 2019 ).

The mesosystem level reflects the relationships between elements of the exosystem and the microsystem. However, it also represents a student’s background and social milieu ( Eng, Szmodis and Mulsow, 2014 ), and the interplay of their (family) socioeconomic status and geographical location. This can impact on family income and their ability to afford devices ( Adhikari et al. 2016 ; Hohlfeld, Ritzhaupt and Barron, 2010 ; Warschauer and Xu, 2018 ), as well as their access to the Internet ( Beckmann, 2010 ; Bond, 2019 ), and thereby affect their attitudes towards technology ( Hollingworth et al. 2011 ). Therefore, it is vital that low-cost hardware and software are made available to students and families, to reduce this digital divide ( Adams Becker et al. 2018 ; Daniels and Holtman, 2014 ), but also that institutions conduct needs analyses, so as to deepen understanding of real and potential barriers for students and families ( Education Endowment Foundation, 2018 ; Goodall and Vorhaus, 2011 ). Further ideas for increasing technology access include opening up computer labs to students and families ( Lewin and Luckin, 2010 ) or establishing loan equipment schemes ( Hohlfeld et al. 2010 ).

Microsystem

The microsystem technology-enhanced learning environment is reflective of other models that have focused on the relationship between learner-teacher-content ( Bundick et al. 2014 ; Martin and Bolliger, 2018 ; Moore, 1989 ), including interaction with peers, teachers, authentic and worthwhile tasks ( Kearsley and Shneiderman, 1998 ; Lim, 2004 ), and technology ( Koehler and Mishra, 2005 ). These ‘external’ relationships, or the ‘inter-individual factors’ ( Bundick et al. 2014 ), play a vital role in ongoing student wellbeing, sense of connectedness, engagement and success ( Aldridge and McChesney, 2018 ; Wimpenny and Savin-Baden, 2013 ). It is also important to consider that a student’s life load, including employment, health, finances and family problems, can impact the amount that a student can become actively involved within school or university life ( Baron and Corbin, 2012 ), and to recognise that there are ‘internal’ psychosocial influences (see Figure 2 ), or ‘intra-individual factors’, that influence student engagement. These include a student’s self-concept, skills, motivation, self-efficacy, self-regulation, subject/discipline interest and wellbeing ( Bandura, 1995 ; Reschly and Christenson, 2012 ; Zepke, 2014 ), as well as their prior technology experience and acceptance ( Moos and Azevedo, 2009 ), as negative feelings about technology are related to disengagement ( Bartle, Longnecker and Pegrum, 2011 ; Howard, Ma and Yang, 2016 ).

Internal psychosocial influences on student engagement.

Learning environment and technology

There are a variety of factors that influence student engagement when using technology (see Figure 3 ). Students’ access to technology is an issue, which may also impact on their level of confidence and prior level of experience ( Zweekhorst and Maas, 2015 ). Assuming that technology and the Internet can be accessed, the provision of technical (and sometimes emotional) support is necessary, to ensure not losing students along the way due, for example, to anxiety of receiving lower grades as a result of technology issues ( Mejia, 2016 ). Potential problems can be mitigated through introductory sessions to the technology being used ( Shepherd and Hannafin, 2011 ) or having a continuous technical support team present ( Levin, Whitsett and Wood, 2013 ). Providing thorough and clear explanations of how technology is to be used ( Lim, 2004 ; Peck, 2012 ; Salaber, 2014 ), including an emphasis on using ICT for self-directed learning ( Sumuer, 2018 ), and why it is being employed in a specific course setting ( Cakir, 2013 ; Northey et al. 2015 ; Skinner, 2009 ) is also helpful, if not necessary, to ensure student engagement. Consideration should be given to allowing students a choice in which technologies are used ( Martin and Bolliger, 2018 ), as familiar technology can eradicate issues of low technology confidence ( Northey et al. 2018 ). Including out-of-class technology activities in assessment has also been shown to improve engagement and student buy-in ( Northey et al. 2018 ; Zhu, 2006 ).

Learning environment and technology influences on student engagement.

Engagement is more likely to develop when student-teacher relationships are strong ( Martin and Bolliger, 2018 ; Quin, 2017 ; Zepke and Leach, 2010 ; Zhang and Aasheim, 2011 ) and when students perceive the teacher to be knowledgeable, supportive, invested and effective ( Beer, Clark and Jones, 2010 ; Zhu, 2006 ) (see Figure 4 ). Teachers are more likely to employ and be successful using technology when they are confident that they have the skills to use it ( Jääskelä, Häkkinen and Rasku-Puttonen, 2017 ; Marcelo and Yot-Domínguez, 2019 ). Ongoing professional development is crucial to ensure that teachers have the requisite technology knowledge and skills, and can actually foster student engagement ( Bigatel and Williams, 2015 ). Providing regular, personalised, clear and constructive feedback can also enhance engagement ( Ma et al. 2015 ; Martin and Bolliger, 2018 ; Whipp and Lorentz, 2009 ) and influence student agency ( Coates, 2007 ), alongside the use of humour within online discussions ( Imlawi, Gregg and Karimi, 2015 ). By giving feedback in the form of asking questions, students are encouraged to reflect more deeply ( Alcaraz-Salarirche et al. 2011 ). Providing ongoing encouragement to students to contact teachers proactively when needed has also been found to be particularly effective ( Leese, 2009 ), as has providing ongoing attention and follow-up with students ( Zhang et al. 2014 ).

Teacher influences on student engagement.

The learner-content relationship is crucial ( Xiao, 2017 ). Therefore content that is relevant and challenging ( Bundick et al. 2014 ; Cakir, 2013 ; Coates, 2007 ), and taught using active and collaborative learning techniques ( Almarghani and Mijatovic, 2017 ; Umbach and Wawrzynski, 2005 ; Wimpenny and Savin-Baden, 2013 ), has been shown to be highly effective at promoting student engagement (see Figure 5 ). Designing meaningful learning activities is essential, relating directly to students and/or content. For example, Abate, Gomes and Linton ( 2011 ) stress the importance of choosing appropriate and meaningful questions when using audience response systems, to avoid student disengagement. It is important to avoid redundantly doubling up on activities, such as using both online journals and online discussions ( Ruckert et al. 2014 ), and activities should be related to real life (e.g. Alshaikhi and Madini, 2016 ), as this makes them more useful to students. Likewise, ensuring that technology-enhanced activities are of high quality was found to be one aspect to engage students successfully, the lack of it resulting in students asking for “greater content rigor, depth, and relevancy” ( Eick and King Jr., 2012: 29 ) in, for example, YouTube videos used in class.

Curriculum/activity influences on student engagement.

Creating learning communities in which students can interact collaboratively with others to build effective peer-peer relationships—with or without technology—is extremely valuable to engagement ( Nelson Laird and Kuh, 2005 ; Northey et al. 2015 ; Zepke and Leach, 2010 ) (see Figure 6 ). Students who collaborate actively in the group space, as part of the flipped learning approach, for example, have been found to experience deeper learning, increased confidence and greater achievement ( D’addato and Miller, 2016 ; de Araujo, Otten and Birisci, 2017 ; Grypp and Luebeck, 2015 ; Lee, 2018 ). Yildiz ( 2009 ), in her investigation of social presence in the online classroom, found that knowing what class members look like and having well-meaning social interactions, was conducive to increased confidence and sense of knowing each other. However, students in the study by Sullivan and Longnecker ( 2014, p. 397 ) referred to the course requirement of having to post comments to fellow students’ blogs as “the worst aspect of the blog”. Thus, peer interaction, and the value and meaning attached to it, is strongly related to how learning activities and digital tools are designed and used within a course.

Peer influences on student engagement.

Family relationships, level of parent education, and parental involvement and engagement with student learning can play a large role in student engagement ( Diogo, Silva and Viana, 2018 ; Doctoroff and Arnold, 2017 ; Howell, 2013 ) (see Figure 7 ), as well as in students’ motivation towards schooling ( Heatly and Votruba-Drzal, 2018 ), achievement ( Castro et al. 2015 ; Hill and Tyson, 2009 ), self-efficacy ( Vekiri, 2010 ) and psychological wellbeing ( Wong et al. 2018 ). In particular, families can also affect the level of student involvement with, use of and attitude towards technology ( Krause, 2014 ; Stevenson, 2008 ), with students also often learning their computing skills from their parents ( Ihme and Senkbeil, 2017 ).

Family influences on student engagement.

Enhanced student engagement through using technology can lead to a number of short and long term academic and social outcomes (see Figure 8 ), termed proximal and distal consequences by Kahu ( 2013 ). Short term outcomes include increased discipline specific knowledge and higher order thinking skills ( Nelson Laird and Kuh, 2005 ; Salaber, 2014 ), increased motivation ( Akbari et al. 2016 ), enhanced sense of belonging and wellbeing ( Lear, Ansorge and Steckelberg, 2010 ), and improved relationships through peer-to-peer learning and collaboration ( Zweekhorst and Maas, 2015 ). Long term outcomes include lifelong learning ( Karabulut-Ilgu et al. 2018 ), enhanced personal development ( Alioon and Delialioğlu, 2019 ), and increased involvement in the wider educational community ( Chen, Lambert and Guidry, 2010 ; Junco, 2012 ).

Short and long term outcomes of student engagement.

Student engagement within a technology-enhanced learning (TEL) microsystem

Bringing these ideas together, the following framework shows the interplay between the TEL microsystem, student engagement and ensuing outcomes (see Figure 9 ). It reflects the definition of student engagement initially provided, whereby engagement is influenced by a range of internal and external factors. The more students are engaged and empowered within their learning community, the more likely it is that engagement will lead to a range of outcomes, and the more likely it is that this energy, effort and engagement will then feed back into the activities and learning environment.

Student engagement framework.

In this article, the authors have synthesised a range of student engagement and educational technology literature, and sought to present an in-depth analysis of a bioecological student engagement framework, conceptualising how educational technology can influence engagement in the K-12 and higher education classroom. Although the body of literature exploring the interplay between student engagement and technology continues to grow, there is an obvious gap in its theoretical understanding and grounding (e.g. Henrie et al. 2015 ). With its focus on the macro, exo, meso and micro levels, this framework zooms in on the microsystem of the classroom and its constituents—these are also ultimately the factors that can be impacted by educators and further elaborated on by educational research. Owing to a lack of space in the present paper, further work is needed to examine the macro, exo and meso levels. Although the framework presented in this contribution is only one way of viewing this complex phenomenon, it offers a clear conceptual structure that other researchers, instructional designers, policy advisors and practitioners may find useful, and could help guide future student engagement research.

Grounding future research

By understanding the range of influences on student engagement, researchers could choose to focus on how certain factors affect engagement, and use the model presented here to frame their investigation and subsequent results discussion. So too, research may focus on one or all three engagement dimensions (e.g. cognitive engagement), and/or individual or multiple indicators of engagement (e.g. critical thinking and learning from peers). Using the first author’s flipped learning case study as an example ( Bond, 2019 ), the bioecological model was used to frame the results and identify recommendations for schools on successful flipped learning implementation. A new model was then presented, which clearly reflected the influences pertaining to that particular case study. The merit of applying a strong theoretical grounding and framework for analysing student engagement and educational technology is in substantiating research, which is still, however, lacking ( Castañeda and Selwyn, 2018 ). For example, the results of an extensive review of educational technology literature revealed that only 174 of 503 studies (35%) actually used a theoretical framework ( Hew et al. 2019 ), and much research specifically investigating student engagement lacked appropriate definition and operationalisation ( Henrie et al. 2015 ). As Antonenko ( 2015, p. 53 ) concisely states, “conceptual frameworks should be viewed as an instrument for organizing inquiry and creating a compelling theory-based and data-driven argument for the importance of the problem, rigor of the method, and implications for further development of theory and enhancement of practice”.

Implications for practice

The model presented in this paper is of interest to practitioners to raise and focus their attention to the different layers of their students’ environments. Although most educators have this perspective, this model places technology as an integral part of this environment, identifying it as an influential factor, that can equally be influenced through the educator in his or her practice. Whereas educators are able to influence the meso and macrosystem components only marginally, they do have the power and responsibility to ensure that the microsystem is set up in a way that is conducive to student engagement—especially in regard to using educational technology. This involves reflection on their own ability and confidence in using technology, as well as seeing themselves as facilitators and initiators of technology use within (and outside of) the classroom, as stressed in the analysis of the microsystem components of the framework presented here. Practitioners are encouraged to use the figures provided in this paper (e.g. Figure 4 ) to conduct periodic (self-)assessments, reflecting on the extent to which these factors are having a positive influence.

Providing ongoing support to enable students’ actual use of technology, as well as ensuring instructor presence throughout the course, has been seen as a crucial element for engaged students. As has been argued, the integration of educational technology facilitates engagement if students find it meaningful, related to real life, and can act without anxiety. In this context, providing opportunities for students to engage agentically in their learning, through activity and technology choice, as well as through collaborative activities, can also enhance engagement. Through thoughtful engagement with and application of technology, and by providing students with opportunities for active participation, student engagement can be nurtured.

See http://www.researchgate.net/project/Facilitating-student-engagement-with-digital-media-in-higher-education-ActiveLeaRn for further information.  

Funding Information

This research resulted from the ActiveLearn project, funded by the Bundesministerium für Bildung und Forschung (BMBF—German Ministry of Education and Research) [grant number 16DHL1007].

Competing Interests

The authors have no competing interests to declare.

Author Contributions

The first author conducted the literature review and developed the framework. The second author contributed to the conceptualisation of the microsystem. Both authors wrote the conclusion and developed the overall flow of the paper.

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Staying Engaged: Knowledge and Research Needs in Student Engagement

Ming-te wang.

School of Education, Learning Research and Development Center, Department of Psychology, University of Pittsburgh

Jessica Degol

School of Education, University of Pittsburgh

In this article, we review knowledge about student engagement and look ahead to the future of study in this area. We begin by describing how researchers in the field define and study student engagement. In particular, we describe the levels, contexts, and dimensions that constitute the measurement of engagement, summarize the contexts that shape engagement and the outcomes that result from it, and articulate person-centered approaches for analyzing engagement. We conclude by addressing limitations to the research and providing recommendations for study. Specifically, we point to the importance of incorporating more work on how learning-related emotions, personality characteristics, prior learning experiences, shared values across contexts, and engagement in nonacademic activities influence individual differences in student engagement. We also stress the need to improve our understanding of the nuances involved in developing engagement over time by incorporating more extensive longitudinal analyses, intervention trials, research on affective neuroscience, and interactions among levels and dimensions of engagement.

Over the past 25 years, student engagement has become prominent in psychology and education because of its potential for addressing problems of student boredom, low achievement, and high dropout rates. When students are engaged with learning, they can focus attention and energy on mastering the task, persist when difficulties arise, build supportive relationships with adults and peers, and connect to their school ( Wang & Eccles, 2012a , 2012b ). Therefore, student engagement is critical for successful learning ( Appleton, Christenson, & Furlong, 2008 ). In this article, we review research on student engagement in school and articulate the key features of student engagement. In addition, we provide recommendations for research on student engagement to address limits to our understanding, apply what we have learned to practice, and focus on aspects that warrant further investigation.

KEY FEATURES OF STUDENT ENGAGEMENT

Engagement is distinct from motivation.

Engagement is a broadly defined construct encompassing a variety of goal-directed behaviors, thoughts, or affective states ( Fredricks, Blumenfeld, & Paris, 2004 ). Although definitions of engagement vary across studies ( Reschly & Christenson, 2012 ), engagement is distinguished from motivation. A common conceptualization, though not universally established, is that engagement is the effort directed toward completing a task, or the action or energy component of motivation ( Appleton et al., 2008 ). For example, motivation has been defined as the psychological processes that underlie the energy, purpose, and durability of activities, while engagement is defined as the outward manifestation of motivation ( Skinner, Kindermann, Connell, & Wellborn, 2009 ). Engagement can take the form of observable behavior (e.g., participation in the learning activity, on-task behavior), or manifest as internal affective (e.g., interest, positive feelings about the task) and cognitive (e.g., metacognition, self-regulated learning) states ( Christenson et al., 2008 ). Therefore, when motivation to pursue a goal or succeed at an academic task is put into action deliberately, the energized result is engagement.

Engagement Is Multilevel

Engagement is a multilevel construct, embedded within several different levels of increasing hierarchy ( Eccles & Wang, 2012 ). Researchers have focused on at least three levels in relation to student engagement ( Skinner & Pitzer, 2012 ). The first level represents student involvement within the school community (e.g., involvement in school activities). The second level narrows the focus to the classroom or subject domain (e.g., how students interact with math teachers and curriculum). The third level examines student engagement in specific learning activities within the classroom, emphasizing the moment-to-moment or situation-to-situation variations in activity and experience.

Engagement Is Multidimensional

Although most researchers agree that student engagement is multidimensional, consensus is lacking over the dimensions that should be distinguished ( Fredricks et al., 2004 ). Most models contain both a behavioral (e.g., active participation within the school) and an emotional (e.g., affective responses to school experiences) component ( Finn, 1989 ). Other researchers have identified cognitive engagement as a third factor that incorporates mental efforts that strengthen learning and performance, such as self-regulated planning and preference for challenge ( Connell & Wellborn, 1991 ; Wang, Willett, & Eccles, 2011 ). Although not as widely recognized, a fourth dimension, agentic engagement , reflects a student’s direct and intentional attempts to enrich the learning process by actively influencing teacher instruction, whereas behavioral, emotional, and cognitive engagement typically represent student reactions to classroom experiences ( Reeve & Tseng, 2011 ). Given the variety of definitions of engagement throughout the field, researchers must specify their dimensions and ensure that their measures align properly with these descriptions of engagement.

Engagement Is Malleable

Student engagement is shaped by context, so it holds potential as a locus for interventions ( Wang & Holcombe, 2010 ). When students have positive learning experiences, supportive relationships with adults and peers, and reaffirmations of their developmental needs in learning contexts, they are more likely to remain actively engaged in school ( Wang & Eccles, 2013 ). Structural features of schools (e.g., class size, school location) have also been attributed to creating an educational atmosphere that influences student engagement and achievement. However, structural characteristics may not directly alter student engagement, but may in fact alter classroom processes, which in turn affect engagement ( Benner, Graham, & Mistry, 2008 ).

Several aspects of classroom processes are central to student engagement. For example, engagement is greater in classrooms where tasks are hands-on, challenging, and authentic ( Marks, 2000 ). Teachers who provide clear expectations and instructions, strong guidance during lessons, and constructive feedback have students who are more behaviorally and cognitively engaged ( Jang, Reeve, & Deci, 2010 ). Researchers have also linked high parental expectations to persistence and interest in school ( Spera, 2005 ), and linked high parental involvement to academic success and mental health both directly and indirectly through behavioral and emotional engagement ( Wang & Sheikh-Khalil, 2014 ). Conceptualizing student engagement as a malleable construct enables researchers to identify features of the environment that can be altered to increase student engagement and learning.

Engagement Predicts Student Outcomes

Student engagement is a strong predictor of educational outcomes. Students with higher behavioral and cognitive engagement have higher grades and aspire to higher education ( Wang & Eccles, 2012a ). Emotional engagement is also correlated positively with academic performance ( Stewart, 2008 ). Student engagement also operates as a mediator between supportive school contexts and academic achievement and school completion ( Wang & Holcombe, 2010 ). Therefore, increasing student engagement is a critical aspect of many intervention efforts aimed at reducing school dropout rates ( Archambault, Janosz, Morizot, & Pagani, 2009 ; Christenson & Reschly, 2010 ; Wang & Fredricks, 2014 ). Moreover, engagement is linked to other facets of child development. Youth with more positive trajectories of behavioral and emotional engagement are less depressed and less likely to be involved in delinquency and substance abuse ( Li & Lerner, 2011 ). School disengagement has been linked to negative indicators of youth development, including higher rates of substance use, problem behaviors, and delinquency ( Henry, Knight, & Thornberry, 2012 ). Some of these associations may actually be reciprocal, so that high engagement may lead to greater academic success, and greater academic success may then lead to even greater academic engagement ( Hughes, Luo, Kwok, & Loyd, 2008 ).

Engagement Comes in Qualitatively Different Patterns

Using person-centered approaches to study engagement advances our understanding of student variation in multivariate engagement profiles and the differential impact of these profiles on child development. One study ( Wang & Peck, 2013 ) used latent profile analysis to classify students into five groups of varying patterns of behavioral, emotional, and cognitive engagement, which were associated differentially with educational and psychological functioning. For example, a group of emotionally disengaged youth was identified (high behavioral and cognitive engagement, but low emotional engagement) with grade point averages and dropout rates comparable to those of the highly engaged group of youth (high on all three dimensions). However, despite their academic success, the emotionally disengaged students had a greater risk of poor mental health, reporting higher rates of symptoms of depression than any other group. Furthermore, growth mixture modeling analysis with a combined measure of behavioral, cognitive, and emotional engagement showed that unlike most individuals who experienced high to moderately stable trajectories of engagement throughout adolescence, many students experienced linear or nonlinear growth or declines ( Janosz, Archambault, Morizot, & Pagani, 2008 ). Students with unstable patterns of engagement were more likely to drop out. These developmental patterns and profiles cannot be detected by variable-centered approaches that focus on population means and overlook heterogeneity across groups. As person-centered research becomes more common, targeted intervention programs should be more effective at serving unique subgroups of students with specific developmental needs.

Disengagement Is More Than the Lack of Engagement

One of the inconsistencies found in the research is whether we should distinguish engagement from disengagement and measure these constructs on the same continuum or as separate continua. Most studies consider engagement as the opposite of disengagement with lower levels of engagement indicating more disengagement. However, some researchers have begun to view disengagement as a separate and distinct psychological process that makes unique contributions to academic outcomes, not simply as the absence of engagement ( Jimerson, Campos, & Greif, 2003 ). For example, behavioral and emotional indicators of engagement (e.g., effort, interest, persistence) and disaffection (e.g., withdrawal, boredom, frustration) can be treated as separate constructs, indicating that although similar, engagement and disaffection do not overlap completely ( Skinner, Furrer, Marchand, & Kindermann, 2008 ). Researchers should incorporate separate measures of engagement and disengagement into their work to determine the unique contributions of each construct to academic, behavioral, and psychological outcomes.

LOOKING AHEAD

Although we know much from research on student engagement, a number of areas require clarification and expansion.

Affective Arousal and Engagement

Emotions in educational contexts can enhance or impede learning by shaping the motivational and cognitive strategies that individuals use when faced with a new challenge. Negative emotions such as anxiety may interfere with performing a task by reducing the working memory, energy, and attention directed at completing the task, whereas positive emotions such as enjoyment, hope, and pride may increase performance by focusing attention on the task and promoting adaptive coping strategies ( Pekrun, Goetz, Titz, & Perry, 2002 ; Reschly, Huebner, Appleton, & Antaramian, 2008 ). However, much of the work on emotions and engagement focuses on general dispositions toward the learning environment, such as measuring interest in or valuing of school ( Stewart, 2008 ). Far less is known about how students’ actual emotions or affective states during specific learning activities influence their academic engagement and achievement ( Linnenbrink-Garcia & Pekrun, 2011 ). Researchers rarely measure how emotions relate to subsequent engagement, relying predominantly on retrospective student self-reports to measure affective states. Useful supplements to students’ reports would be psychophysiological indicators of emotional distress (e.g., facial expression, heart rate) and experience sampling methods to assess situational emotional states during classroom activities.

With the advancement of brain imaging technology, neuroimaging studies show that affective states during learning are important in determining how efficiently the brain processes new information ( Schwabe & Wolf, 2012 ). Although neuroimaging cannot be used to measure classroom engagement in real time, neuroscience techniques are valuable tools that may advance our understanding of how emotional experiences shape neural processing of information and affect engagement during a task. For example, do prolonged states of boredom in the classroom actually alter the shape and functionality of the brain over time, and can we intervene in these processes to reverse the negative effects of boredom or apathy? We also need a more thorough understanding of how genetic predispositions and environmental conditions interact to alter brain chemistry. Studies should identify precursors to or triggers for negative affective experiences, and identify environmental supports that can eliminate these negative emotions, foster adaptive coping strategies, and increase learning engagement and performance.

Interactions Among Levels

Engagement is represented at many hierarchical levels in the educational environment (e.g., school, classroom, momentary level). However, researchers rarely frame their conceptualizations and assessments of engagement in terms of a hierarchical system or process, so we lack understanding about how student engagement at these various levels interacts to influence performance. Learning is a continuous developmental process, not an instantaneous event, and engagement is the energy that directs mental, behavioral, and psychological faculties to the learning process. By focusing on only one level of engagement, we understand little about the process through which engagement is formed and leads ultimately to academic achievement.

Are there reciprocal interrelations between more immediate states of engagement and broader representations, such that moment-to-moment engagement within the classroom informs feelings and behaviors toward the school as a whole, which then trickle down to influence momentary classroom engagement through a continuous feedback loop? Are these levels additive or multiplicative, such that higher engagement across the board is associated with better academic outcomes than high engagement at only one or two levels? Or does engagement at one level compensate for lower engagement at another level, demonstrating that high engagement across all levels is not necessary for optimal functioning? Broadening the focus of research to incorporate engagement at many micro and macro levels of the educational context would advance our understanding of how different levels develop and interact to shape student engagement, and the differential pathways that lead to academic success.

Development of Many Dimensions

Despite the consensus over the multidimensionality of student engagement, the role that each dimension plays in shaping academic outcomes remains unclear ( Skinner et al., 2008 ). Three avenues warrant exploration: (a) independent relations, (b) emotional engagement (which drives behavioral and cognitive engagement), and (c) reciprocal relations.

Independent relations suggest that each dimension of engagement makes unique contributions to student functioning. In other words, high behavioral engagement cannot compensate for the effects of low emotional engagement, given that both shape student outcomes independently.

The second avenue posits that emotional engagement could be a prerequisite for behavioral and cognitive engagement. According to this viewpoint, students who enjoy learning should participate in classroom activities more often and take more ownership over their learning. Emotional engagement sets the stage for developing cognitive and behavioral processes of student engagement.

The third possibility suggests bidirectional relations among the organizational constructs of engagement, with each dimension influencing the others cyclically. For example, enjoyment of learning or high emotional engagement may lead to greater use of self-regulated learning strategies or cognitive engagement and greater behavioral engagement within the classroom. This increased behavioral participation and use of cognitive strategies to improve performance may elicit positive feedback from classmates and teachers, further increasing enjoyment of learning, and so on. With reciprocal relations, each process reinforces and feeds into the others. For researchers to understand the developmental progression of engagement over time, they should tease apart the unique versus compounded effects of each dimension of engagement.

Longitudinal Research Across Developmental Periods

Some research on how student engagement unfolds and changes over time has shown average declines in various indicators of engagement throughout adolescence and in the transition to secondary school ( Wang & Eccles, 2012a , 2012b ), but other studies have shown heterogeneity in engagement patterns across subgroups of individuals ( Archambault et al., 2009 ; Janosz et al., 2008 ; Li & Lerner, 2011 ). However, we know little about developmental trajectories of engagement spanning early childhood to late adolescence. Many studies track engagement only in early adolescence across a span of 3 or 4 years. Because the ability to become a self-regulated learner, set goals, and monitor progress advances as children mature and become active agents in their own learning, student engagement may take different forms in elementary school than it does in subsequent years ( Fredricks et al., 2004 ). Researchers should investigate how younger versus older students think of engagement, how engagement changes across developmental periods, and whether sociocultural and psychological factors differentially shape engagement at the elementary and secondary levels.

Students’ Prior Learning Experiences

Researchers should also explore the role of students’ previous learning experiences in shaping engagement. When students are confronted with new academic challenges, the emotions and cognitions attached to previous experiences should influence how they adjust or cope with these challenges. In particular, engagement and academic achievement decline during school transitions (e.g., elementary to middle school, middle school to high school), which can be stressful experiences for many students ( Eccles et al., 1993 ; Pekrun, 2006 ). Students with prior experiences of failure in school may be especially vulnerable to the alienating effects of school transitions. How do we discontinue students’ negative feelings about schoolwork and reengage them in their education? How do we maintain positive and engaging experiences for students through every grade level and every transition? Using students’ prior learning experiences to break the cycle of disengagement and strengthen the cycle of continuous interest and engagement could inform interventions, particularly during crucial transitory periods when students are most vulnerable to feelings of isolation, boredom, or alienation.

Intervention

Despite the malleability of student engagement and the connection between developmental contexts and engagement, very few theory- and evidence-based preventative programs have been developed, implemented, and tested on a large scale. A few interventions have increased student engagement. For example, Check & Connect, an evidence-based intervention program, has reduced rates of dropout and truancy, particularly for students at high risk of school failure ( Reschly & Christenson, 2012 ). Randomized control trials of schoolwide positive behavioral support programs have also improved student engagement and achievement, reducing discipline referrals and suspensions ( Horner et al., 2009 ; Ward & Gersten, 2013 ). However, many programs are small, intensive interventions that have not been implemented on a larger scale, raising concerns about implementation fidelity and reduced effectiveness. Many interventions also rely on one dose of services and track developmental changes over a short period, making it difficult to infer long-term benefits.

We need to develop comprehensive programs that adapt to the unique needs of individuals receiving services. Preventative programs often rely on one-size-fits-all models, so subgroups of students may not be served properly. Although universal interventions are beneficial for students in general, targeted programs might be more effective for students at greater risk of academic or psychological problems. Therefore, interventions should be implemented at many levels, incorporating a universal program for students in general and more selected services for at-risk students.

Engagement Across Contexts

We should also explore the relative alignment of educational messages, values, and goals across contexts and how this compatibility influences student engagement. Teachers, parents, and peers are not always in tune with each other over educational values, and these conflicting messages may impair how students engage fully with school. For example, parents might endorse educational excellence as a priority, whereas peers may endorse academic apathy. In these situations, students may have to set aside their personal values and pursue or coordinate the values of others, or try to integrate their personal values with the values of the other group. Students’ ability to coordinate the messages, goals, and values from different agents in their social circles will also determine how they see themselves as learners.

We lack studies on how students reconcile inconsistencies in these messages across groups and how it affects their engagement. If peer groups promote antiachievement goals that are directly in conflict with the educational ideals transmitted by parents, will students conform to peer norms or seek out friends with achievement values that are more aligned with the values endorsed by their families? Is misalignment of educational goals across social contexts a risk factor for school dropout, particularly among students from disadvantaged backgrounds? Researchers need to address this area to help students cope with the inconsistent messages about education in their social circles and to consolidate a stronger academic identity.

Student Character and Engagement

Although researchers have examined how contextual, sociocultural, and motivational factors influence student engagement, the influence of student character or personality factors is less well understood. Research on the Big Five personality traits has found conscientiousness, an indicator of perseverance, to be the most consistent predictor of academic achievement ( Poropat, 2009 ).

Persistence has been examined through grit , a characteristic that entails working passionately and laboriously to achieve a long-term goal, and persisting despite challenges, setbacks, or failures ( Duckworth, Peterson, Matthews, & Kelly, 2007 ). Individuals with grit are more likely to exert effort to prepare and practice to achieve their goals, leading them to be more successful than individuals who use less effortful strategies ( Duckworth, Kirby, Tsukayama, Berstein, & Ericsson, 2011 ).

Nevertheless, we know little about how personality traits might interact with environmental contexts to shape student engagement. Additionally, researchers have yet to examine how profiles of personality traits might interact with each other to influence student engagement. More nuanced research in these areas will aid in the development of learning strategies and educational contexts that may yield the most successful outcomes for various personality types.

Beyond Academic Engagement

Research on student engagement has focused on academic engagement or academic-related activities. Although academic experiences are critical determinants of educational success, school is also a place where students socialize with their friends and engage in nonacademic activities. Focusing exclusively on academic engagement neglects the school’s role as a developmental context in which students engage in a wide range of academic, social, and extracurricular activities that shape their identities as academically capable, socially integrated individuals who are committed to learning. For example, students who struggle with academic learning but are athletic may experience more engagement on the football field than in the classroom. Through participating in these types of nonacademic social activities, students build skills and learn life lessons such as collaborating as a team and becoming a leader. Thus, students’ schooling experiences should involve many forms of engagement, including academic, social, and extracurricular engagement. More research is needed to integrate these forms of engagement in school and examine how they interact to influence students’ academic and socioemotional well-being collectively.

Since its conception more than two decades ago, research on student engagement has permeated the fields of psychology and education. Over this period, we have learned much about engagement. We know that engagement can be measured as a multidimensional construct, including both observable and unobservable phenomena. We have come to appreciate the importance of engagement in preventing dropout and promoting academic success. We also understand that engagement is responsive to variations in classroom and family characteristics.

But in spite of the accrued knowledge on engagement, we have barely scratched the surface in understanding how engagement and disengagement can affect academic development, and how engagement unfolds over time by tracking interactions across contexts, dimensions, and levels. We also cannot dismiss the personal traits and affective states that students bring to the classroom, which may influence engagement regardless of the supportive nature of the environment. We lack knowledge about the extent to which large-scale interventions can produce long-term improvements in engagement across diverse groups. As we move forward with engagement research, we must apply what we have learned and focus on aspects that warrant further exploration. The insight this research provides will allow educators to create supportive learning environments in which diverse groups of students not only stay engaged but also experience the academic learning and success that is a byproduct of continuous engagement.

Acknowledgments

This project was supported by Grant DRL1315943 from the National Science Foundation and Grant DA034151-02 from the National Institute on Drug Abuse at the National Institute of Health to Ming-Te Wang.

Contributor Information

Ming-Te Wang, School of Education, Learning Research and Development Center, Department of Psychology, University of Pittsburgh.

Jessica Degol, School of Education, University of Pittsburgh.

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• Our Mission

To Increase Student Engagement, Focus on Motivation

Teachers can motivate middle and high school students by providing structure while also allowing them some control over their learning.

A 2018 Gallup study found that as students get older, they become less engaged, or “ involved, enthusiastic, and committed .” The study contained some alarming findings: In fifth grade, most students (74 percent) report high levels of engagement with school. However, by middle school, only half of students are engaged, and by high school the number of engaged students shrinks to about one-third. 

Student engagement continued to be a pressing concern for parents and educators during and after the pandemic. Approximately half of parents (45 percent), 77 percent of administrators, and 81 percent of teachers said that keeping students engaged was difficult during remote learning. In addition, 94 percent of educators considered student engagement to be the most important metric to look at when determining student success. Gallup found that students who are engaged with school not only report achieving higher grades but also feel more hopeful about their future. 

Student motivation

Engagement and motivation are separate, related, but often confused. Motivation is the driving force that causes a student to take action. Engagement is the observable behavior or evidence of that motivation. Motivation is necessary for engagement, but successful engagement could also help students to feel motivated in the future. 

In my book The Independent Learner , I discuss how self-regulated learning strategies help students to increase their motivation and willingness to engage in learning because they create feelings of autonomy, competence, and relatedness. According to research by Ryan and Deci, these are the three components that facilitate motivation :

• Autonomy is a “sense of initiative and ownership in one’s actions.”
• Competence is a “feeling of mastery” and a sense that with effort a student can “succeed and grow.”
• Relatedness is when the school setting “conveys respect and caring” that results in the student feeling “a sense of belonging and connection.”

Motivating students in the classroom

It is important not to confuse engagement with entertainment. In an EdWeek survey , researchers found that the entertaining activities that teachers expect to engage students are not necessarily working. While the majority of teachers who had increased their use of digital games assumed that games would engage students, only 27 percent of students reported feeling more engaged when digital games were involved. In addition, 30 percent of students said learning was actually less engaging. 

So what creates engagement? Gallup found that students who strongly agreed with the following two statements were 30 times more likely to report high levels of engagement with school:

• My school is “committed to building the strengths of each student.”
• I have “at least one teacher who makes me excited about the future.”

In other words, engaged students recognize that they have the support of caring adults who are willing to partner with them in their learning. 

Not all classrooms create these conditions. Controlling classrooms lower autonomy and motivation and increase student frustration. In controlling classrooms, students avoid challenges because they are afraid of failure. They work toward external rewards or to avoid possible anxiety or shame caused by mistakes. The teacher controls the answers and learning materials and uses language like “should” or “have to,” and students feel pressured to behave and achieve.

In contrast, creating classroom environments where students feel autonomy, competence, and relatedness helps students to maintain motivation and increase their engagement in school activities. Classrooms that foster motivation and increase engagement are high in structure but low in top-down control . These classrooms have the following qualities.

Supportive: Teachers support autonomy by listening and attempting to understand and respond to students’ perspectives. They look at what a student can currently do and where they need to go to reach the standard or objective, and they help the student by building scaffolds or supports to bridge the gap. This makes achievement toward grade-level content possible, even for learners who are not quite there yet.

Personal and individualized: Students feel like they are able to customize their assignments in order to explore their own interests. Students can also be taught to make their own connections to what they are learning through creating their own hooks for a lesson . Recognizing students’ unique qualities and special talents, getting to know what interests students and incorporating these interests into lessons or assignments, and reaching out to parents with a note or email when a student does something well are all strategies that I use to make learning personal and increase relatedness in the classroom. 

Structured and goal-oriented: When teachers give students strategies, provide frequent feedback, and show them how to use those strategies effectively, students are motivated by observing their own progress. Teachers can provide a rationale or standard and guide students in setting short-term mastery goals for each required task. They can also help students to align their daily actions and effort with the results they are hoping to achieve by making a process plan . I have found that when students graph their own progress or use their process plan as a checklist, this makes growth visual and allows students to see the steps they are accomplishing each day toward their goal. In addition, clear expectations, consistency in classroom structure, clear rules, and set routines are all important. 

Collaborative: Teachers provide students with choices and opportunities to partner with the teacher in their learning experiences and show ownership in the tasks that are assigned to them. When teachers encourage students to begin to make choices and take responsibility for their own learning, students see a purpose in school activities. One way to do this is through using self-assessment to prompt reflection on strategy use. I have students analyze their graded assignments to decide what strategies to keep and what to do differently next time. When students see errors as a signal that they need to reflect on the process and learning strategies they used, they realize there are no real mistakes, just opportunities for learning.

Although the pandemic has been difficult, the majority of students (69 percent) report feeling hopeful about the future. Students who are hopeful and engaged are less likely to get suspended or expelled, have chronic absenteeism, skip school, or drop out of school. When educators put effort into the goal of creating a school environment guided by student engagement, motivation, and autonomy, students can see their own growth. This creates an excitement for learning that helps students to maintain hope for the future even through difficult circumstances.

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Focus on Student Engagement for Better Academic Outcomes

Story Highlights

• Social-emotional learning is rapidly growing in popularity in U.S. schools
• Student engagement and hope are significantly related to student academic outcomes
• The Gallup Student Poll helps you discover elements that drive student success

With the current shift in U.S. education policies putting a priority on social-emotional learning (SEL), the importance of teachers and schools having SEL resources -- proven to create positive student outcomes -- is growing rapidly. As the new goals and guidelines are implemented in accordance with the Every Student Succeeds Act (ESSA), schools have, unfortunately, been flooded with instructional guides, curriculum advice, and other implementation options that are founded on minimal research or unproven outcomes.

Measure What Matters Most for Student Success

For the past 12 years, Gallup and been surveying students through the Gallup Student Poll to research and fine-tune an SEL-based solution with proven impacts and outcomes. Statistically linked to a majority of the mandated ESSA accountability measures, the outcome of Gallup's research is two indexes that measure students' engagement and hope . These indexes consist of nine and seven items, respectively, and indicate students' "involvement in and enthusiasm for school" and "ideas and energy for the future." The outcomes of these indexes go beyond showing the level of engagement and hope of a school's students -- they are linked to the overall success of districts and schools, based on the measures to which they are being held accountable.

Discover the Elements That Drive Success

One recent Gallup study including 128 schools and more than 110,000 students found that student engagement and hope were significantly positively related to student academic achievement progress (growth) in math, reading, and all subjects combined, along with postsecondary readiness in math and writing. Focusing specifically on student engagement, the study found that schools in the top quartile of student engagement had significantly more students exceeding and meeting proficiency requirements than schools in the bottom quartile of engagement.

When compounded with another Gallup study finding that identified links from student engagement and hope to student discipline and behaviors , implementing strategies for measuring and growing both of these is a win for our education system as a whole. The impact these foundational notions of student engagement and hope have on students and corresponding school outcomes is precisely the focus of the newly minted ESSA state, district and school report cards. The report cards' measures include a combination of student achievement, student growth, English language proficiency, high school graduation rates and a school quality or student success measure, which were selected by each state autonomously.

ESSA and the SEL movements are steering the education system in a great direction, focusing education leaders' and schools' efforts where they should always be. As Gallup's founder Don Clifton put it, "Our greatest contribution is to be sure there is a teacher in every classroom who cares that every student, every day, learns and grows and feels like a real human being."

Create the Right Classroom Environment

The means to impact student engagement and hope can be found in multiple avenues. One is the straightforward path of measuring student engagement and hope to identify areas of strength within a school and areas of opportunity in which to implement different solutions. Leveraging the different items within the engagement and hope indexes provides building blocks for this step, while still maintaining the autonomy of teachers and schools to decide the best way to do so.

Another point of impact is the effect teacher engagement has on student engagement . By providing teachers with the opportunity to voice their opinions on different areas that affect their engagement, principals can better partner with teachers to enhance a culture of engagement. Together, they can create an environment that is more conducive to teacher productivity, which fosters the classroom environment best suited for student engagement, hope and learning.

"Our greatest contribution is to be sure there is a teacher in every classroom who cares that every student, every day, learns and grows and feels like a real human being."

A third and final intervention that can support student engagement and hope is the identification and development of student talents and strengths . While great teachers innately practice this without any external resources, the simple identification and acknowledgment of the things a student naturally does well can be an immediate boost to their self-esteem, wellbeing, engagement and hope.

No matter the policy or level of standards in place, by focusing on interventions that provide positive outcomes for students and teachers alike, our education system will develop as it was always intended.

Gallup can help you improve teacher and student outcomes:

• Read Positive Relationships Between Student Engagement and Hope and Student Behavior to uncover more key findings from a recent Gallup Student Poll analysis.
• Discover how to help kids have great lives by focusing on what they do best.
• Get in touch with a Gallup expert for more information on how we can help you improve your school's success.

Mark Reckmeyer is a K-12 Education Researcher and Consultant at Gallup.

Optimize Your School Engage students to help them get the most out of their education.

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October 30, 2019 Gallup https://www.gallup.com/education/267521/focus-student-engagement-better-academic-outcomes.aspx Gallup World Headquarters, 901 F Street, Washington, D.C., 20001, U.S.A +1 202.715.3030

Created by the Great Schools Partnership , the GLOSSARY OF EDUCATION REFORM is a comprehensive online resource that describes widely used school-improvement terms, concepts, and strategies for journalists, parents, and community members. | Learn more »

Student Engagement

In education, student engagement refers to the degree of attention, curiosity, interest, optimism, and passion that students show when they are learning or being taught, which extends to the level of motivation they have to learn and progress in their education. Generally speaking, the concept of “student engagement” is predicated on the belief that learning improves when students are inquisitive, interested, or inspired, and that learning tends to suffer when students are bored, dispassionate, disaffected, or otherwise “disengaged.” Stronger student engagement or improved student engagement are common instructional objectives expressed by educators.

In many contexts, however, student engagement may also refer to the ways in which school leaders, educators, and other adults might “engage” students more fully in the governance and decision-making processes in school, in the design of programs and learning opportunities, or in the civic life of their community. For example, many schools survey students to determine their views on any number of issues, and then use the survey findings to modify policies or programs in ways that honor or respond to student perspectives and concerns. Students may also create their own questions, survey their peers, and then present the results to school leaders or the school board to advocate for changes in programs or policies. Some schools have created alternative forms of student governance, “student advisory committees,” student appointments to the school board, and other formal and informal ways for students to contribute to the governance of a school or advise superintendents, principals, and local policy makers. These broader forms of “student engagement” can take a wide variety of forms—far too many to extensively catalog here. Yet a few illustrative examples include school-supported  volunteer programs and community-service requirements (engaging students in public service and learning through public service), student organizing (engaging students in advocacy, community organizing, and constructive protest), and any number of potential student-led groups, forums, presentations, and events (engaging students in community leadership, public speaking, and other activities that contribute to “ positive youth development “). For a related discussion, see student voice .

In education, the term student engagement has grown in popularity in recent decades, most likely resulting from an increased understanding of the role that certain intellectual, emotional, behavioral, physical, and social factors play in the learning process and social development. For example, a wide variety of research studies on learning have revealed connections between so-called “non-cognitive factors” or “non-cognitive skills” (e.g., motivation, interest, curiosity, responsibility, determination, perseverance, attitude, work habits, self-regulation, social skills, etc.) and “cognitive” learning results (e.g., improved academic performance, test scores, information recall, skill acquisition, etc.). The concept of student engagement typically arises when educators discuss or prioritize educational strategies and teaching techniques that address the developmental, intellectual, emotional, behavioral, physical, and social factors that either enhance or undermine learning for students.

It should be noted that educators may hold different views on student engagement, and it may be defined or interpreted differently from place to place. For example, in one school observable behaviors such as attending class, listening attentively, participating in discussions, turning in work on time, and following rules and directions may be perceived as forms of “engagement,” while in another school the concept of “engagement” may be largely understood in terms of internal states such as enthusiasm, curiosity, optimism, motivation, or interest.

While the concept of student engagement seems straightforward, it can take fairly complex forms in practice. The following examples illustrate a few ways in which student engagement may be discussed or addressed in schools:

• Intellectual engagement: To increase student engagement in a course or subject, teachers may create lessons, assignments, or projects that appeal to student interests or that stimulate their curiosity. For example, teachers may give students more choice over the topics they are asked to write about (so students can choose a topic that specifically interests them) or they may let students choose the way they will investigate a topic or demonstrate what they have learned (some students may choose to write a paper, others may produce short video or audio documentary, and still others may create a multimedia presentation). Teachers may also introduce a unit of study with a problem or question that students need to solve. For example, students might be asked to investigate the causes of a local environmental problem, determine the species of an unknown animal from a few short descriptions of its physical characteristics and behaviors, or build a robot that can accomplish a specific task. In these cases, sparking student curiosity can increase “engagement” in the learning process. For related discussions, see authentic learning , community-based learning , differentiation , personalized learning , project-based learning , and  relevance .
• Emotional engagement: Educators may use a wide variety of strategies to promote positive emotions in students that will facilitate the learning process, minimize negative behaviors, or keep students from dropping out. For example, classrooms and other learning environments may be redesigned to make them more conducive to learning, teachers may make a point of monitoring student moods and asking them how they are feeling, or school programs may provide counseling, peer mentoring, or other services that generally seek to give students the support they need to succeed academically and feel positive, optimistic, or excited about school and learning. Strategies such as advisories , for example, are intended to build stronger relationships between students and adults in a school. The basic theory is that students will be more likely to succeed if at least one adult in the school is meeting with a student regularly, inquiring about academic and non-academic issues, giving her advice, and taking an interest in her out-of-school life, personal passions, future aspirations, and distinct learning challenges and needs.
• Behavioral engagement: Teachers may establish classroom routines, use consistent cues, or assign students roles that foster behaviors more conducive to learning. For example, elementary school teachers may use cues or gestures that help young students refocus on a lesson if they get distracted or boisterous. The teacher may clap three times or raise a hand, for example, which signals to students that it’s time to stop talking, return to their seats, or begin a new activity. Teachers may also establish consistent routines that help students stay on task or remain engaged during a class. For example, the class may regularly break up into small groups or move their seats into a circle for a group discussion, or the teacher may ask students on a rotating basis to lead certain activities. By introducing variation into a classroom routine, teachers can reduce the monotony and potential disengagement that may occur when students sit in the same seat, doing similar tasks, for extended periods of time. Research on brain-based learning has also provided evidence that variation, novelty, and physical activity can stimulate and improve learning. For a related discussion, see classroom management .
• Physical engagement: Teachers may use physical activities or routines to stimulate learning or interest. For example, “kinesthetic learning” refers to the use of physical motions and activities during the learning process. Instead of asking students to answer questions aloud, a teacher might ask students to walk up to the chalkboard and answer the question verbally while also writing the answer on the board (in this case, the theory is that students are more likely to remember information when they are using multiple parts of the brain at the same time—i.e., the various parts dedicated to speaking, writing, physical activity, etc.). Teachers may also introduce short periods of physical activity or quick exercises, particularly during the elementary years, to reduce antsy, fidgety, or distracted behaviors. In addition, more schools throughout the United States are addressing the physical needs of students by, for example, offering all students free breakfasts (because disengagement in learning and poor academic performance have been linked to hunger and malnutrition) or starting school later at a later time (because adolescent sleep patterns and needs differ from those of adults, and adolescents may be better able to learn later in the morning).
• Social engagement: Teachers may use a variety of strategies to stimulate engagement through social interactions. For example, students may be paired or grouped to work collaboratively on projects, or teachers may create academic contests that students compete in—e.g., a friendly competition in which teams of students build robots to complete a specific task in the shortest amount of time. Academic and co-curricular activities such as debate teams, robotics clubs, and science fairs also bring together learning experiences and social interactions. In addition, strategies such as demonstrations of learning or capstone projects may require students to give public presentations of their work, often to panels of experts from the local community, while strategies such as community-based learning or service learning (learning through volunteerism) can introduce civic and social issues into the learning process. In these cases, learning about societal problems, or participating actively in social causes, can improve engagement.
• Cultural engagement: Schools may take active steps to make students from diverse cultural backgrounds—particularly recently arrived immigrant or refugee students and their families—feel welcomed, accepted, safe, and valued. For example, administrators, teachers, and school staff may provide special orientation sessions for their new-American populations or offer translation services and informational materials translated into multiple languages. Students, families, and local cultural leaders from diverse backgrounds may be asked to speak about their experiences to students and school staff, and teachers may intentionally modify lessons to incorporate the history, literature, arts, and perspectives of the student ethnicities and nationalities represented in their classes. School activities may also incorporate multicultural songs, dances, and performances, while posters, flags, and other educational materials featured throughout the school may reflect the cultural diversity of the students and school community . The general goal of such strategies would be to reduce the feelings of confusion, alienation, disconnection, or exclusion that some students and families may experience, and thereby increase their engagement in academics and school activities. For related discussions, see dual-language education , English-language learner ,  multicultural education , and voice .

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Student Engagement in Higher Education: Conceptualizations, Measurement, and Research

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• Teniell L. Trolian   ORCID: orcid.org/0000-0001-5268-6081 3  

Part of the book series: Higher Education: Handbook of Theory and Research ((HATR,volume 39))

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Researchers have suggested that student engagement in the college and university learning environment is an important contributor to student learning, achievement, and outcomes. Higher education scholars have developed measures to assess students’ engagement in higher education, and they have examined experiences that contribute to student engagement, as well as whether student engagement leads to important college outcomes such as college persistence, academic achievement, cognitive development, and other affective outcomes. This chapter considers how student engagement has been conceptualized and measured by researchers and discusses research on student engagement in higher education, focusing on four sets of factors – precursors to student engagement, facilitators of student engagement, indicators of student engagement, and outcomes of student engagement. This chapter also offers recommendations for institutional policy and practice related to student engagement, as well as directions for future research focused on student engagement.

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Trolian, T.L. (2023). Student Engagement in Higher Education: Conceptualizations, Measurement, and Research. In: Perna, L.W. (eds) Higher Education: Handbook of Theory and Research. Higher Education: Handbook of Theory and Research, vol 39. Springer, Cham. https://doi.org/10.1007/978-3-031-32186-3_6-1

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Engaging Undergraduate Students in Course-based Research Improved Student Learning of Course Material

• Nicole T. Appel
• Ammar Tanveer
• Sara Brownell
• Joseph N. Blattman

School of Life Sciences, Arizona State University, Tempe, AZ 85281

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*Address correspondence to: Joseph N. Blattman ( E-mail Address: [email protected] ).

Course-based undergraduate research experiences (CUREs) offer students opportunities to engage in critical thinking and problem solving. However, quantitating the impact of incorporating research into undergraduate courses on student learning and performance has been difficult since most CUREs lack a comparable traditional course as a control. To determine how course-based research impacts student performance, we compared summative assessments of the traditional format for our upper division immunology course (2013–2016), in which students studied known immune development and responses, to the CURE format (2017–2019), in which students studied the effects of genetic mutations on immune development and responses. Because the overall class structure remained unaltered, we were able to quantitate the impact of incorporating research on student performance. Students in the CURE format class performed significantly better on quizzes, exams, and reports. There were no significant differences in academic levels, degree programs, or grade point averages, suggesting improved performance was due to increased engagement of students in research.

INTRODUCTION

Research experiences benefit undergraduate students by offering opportunities to engage in critical thinking and problem solving beyond the textbook and known experimental outcomes ( Kardash, 2000 ; Russell et al. , 2007 ; D’Arcy et al. , 2019 ). AAAS Vision and Change: A Call to Action suggested incorporating research into undergraduate education for students to appreciate the setbacks and unexpected outcomes of scientific research and to apply analytical skills and critical thinking to understanding their results (Bauerle et al. , 2011). Skills developed during undergraduate research experiences (UREs), such as teamwork, critical thinking, and oral and written communication, help prepare students for the workforce independent of whether they stay in a Science, Technology, Engineering, and Mathematics (STEM) field ( McClure-Brenchley et al. , 2020 ). Participating in undergraduate research is also associated with increased retention and likelihood of pursuing scientific research as a career ( Mastronardi et al. , 2021 ). Course-based undergraduate research experiences (CURE) provide students the opportunities to obtain scientific process skills while also having a greater outreach compared with one-on-one undergraduate research experiences ( Bangera and Brownell, 2014 ; Burmeister et al. , 2021 ). CURE classes are defined as integrating scientific practices, discovery, collaboration, and iteration with broadly relevant work. All five criteria must be met for a class to be a CURE, although every CURE may cover each criterion in varying degrees ( Auchincloss et al. , 2014 ).

Various surveys for undergraduate research experiences are available for measuring psychological and knowledge-based gains of participating in a CURE class. Using these tools, research has shown several benefits to transitioning to a CURE class. Biology Intensive Orientation Summer (BIOS) is a CURE that originated in China to help undergraduate students gain research skills and graduate students gain mentor skills. The CURE gave students the confidence and skills to pursue mentor based UREs, which was a graduation requirement ( Fendos et al. , 2022 ). Another study found that, after participating in a CURE for a year, students perceived gains in scientific literacy, data collection, presenting results via oral and written communication, and maintaining a lab notebook ( Peteroy-Kelly et al. , 2017 ). Other CUREs, such as the biochemistry authentic scientific inquiry laboratory (BASIL) CURE, measured student reported gains in lab skills, aka anticipated learning outcomes or course-based undergraduate research abilities. Students were asked about their knowledge, experience, and confidence in the seven anticipated learning outcomes in pretests and posttests and reported gains in all seven areas ( Irby et al. , 2020 ). Measuring gains in content knowledge is rarer, but a study by Wolkow et al. followed up with students 1 and 3 years after taking an introductory biology class to which students were randomly assigned to either a CURE or traditional lab class ( Wolkow et al. , 2014 ; Wolkow et al. , 2019 ). One year after the course, students in the CURE lab reported greater psychological gains, such as enjoying the class and considering a research career, and performed better on an assessment used to measure gains on topics covered in the CURE lab. The knowledge gains for general introductory biology were comparable between groups ( Wolkow et al. , 2014 ). By senior year, perceived gains were no longer different between those who were in the CURE and traditional lab classes as freshmen, and the knowledge gains for general introductory biology were comparable, too. However, the targeted knowledge gains of what was covered in the lab classes remained significantly higher in the CURE group ( Wolkow et al. , 2019 ).

In many CUREs, students develop their own questions and experiments to fulfill the five criteria of integrating scientific practices, discovery, collaboration, and iteration with broadly relevant work. Although students report positive outcomes when asked to compare CUREs with previous traditional labs they have taken, obtaining empirical, measurable benefits for students to engage in undergraduate research is difficult when questions and experiments vary by semester or even by lab group ( Linn et al. , 2015 ). In collaboration with Dr. Brownell, we agreed members from her lab could interview our students to determine whether any differences in cognitive and emotional ownership existed between the two class formats and, if so, whether that impacted student perceptions on collaboration, iteration, or discovery/relevance. The interviews were performed in 2016 (traditional), 2017 (CURE), and 2018 (CURE). Based on that collaboration, we learned that changing our Experimental Immunology class to a CURE format did not significantly impact collaboration or iteration, but the CURE format students perceived their data were novel and relevant outside of class. CURE format students also expressed increased cognitive and emotional ownership compared with traditional format students ( Cooper et al. , 2019 ). To ease the transition from a traditional format to the CURE format, the only change made between the formats was that the CURE students researched how a genetic change impacted the immune response alongside the control experiments by comparing the known immune responses of wild-type (WT) mice to previously uncharacterized genetically modified mice. The experiments and assessments were unchanged between class formats. We realized retrospectively that we were in a unique position to empirically measure whether and how incorporating research impacted students and therefore fulfill the gap in knowledge detailed by Linn et al. (2015) . We knew our students had increased cognitive and emotional ownership when research was incorporated into the course ( Cooper et al. , 2019 ), and ownership has been linked to improved student performance ( Martínez et al. , 2019 ). Therefore, the first question we asked was whether incorporating research into the course resulted in improved overall performance? This question was examined using three main variables: overall course performance; sets of quizzes, reports, and exams; and individual assessment items. While we could not quantitate the amount of literature read or scientific skills acquired, we were able to compare the scores of the direct summative assessments intended to measure student learning. In this lab course, we had direct summative assessments in the form of lab reports, quizzes, and two exams. During the semester, we also assessed participation and lab notebooks to encourage students to come prepared to perform experiments and to understand the material beforehand, but we did not use participation or notebook grades to measure how well students learned the course material since those assessments were tools to ensure students came to class knowing the procedures and actively participated. Therefore, we compared the total results from direct summative assessments (lab report, quiz, and exam grades) to determine whether incorporating research into a lab class resulted in any impact on student learning. The quizzes and exams remained identical between the two class formats, which provided another control variable when comparing class formats. Because the Teaching Assistants (TAs) grading the assessments did not know scores would be compared by the professor after the transition to a CURE format, we argue the assessments were graded without bias for CURE or traditional formats. Because students were not told beforehand whether the class was traditional or CURE format, the “volunteer effect” also did not impact our findings ( Brownell et al. , 2013 ). In a second experimental question, we asked whether additional factors influenced course performance or were distinct between traditional versus CURE formats, including grade point average (GPA), academic major, academic year, grading trends, and racial/ethnic or gender diversity.

MATERIALS AND METHODS

This study was conducted with an approved Institutional Review Board protocol (#4249) from Arizona State University.

Traditional Class Format

The class was divided into five sections each consisting of two or three laboratory exercises. The five sections were anatomy and cells of the immune system (labs 2–3), innate immunity (labs 4–6), adaptive immune system development (labs 7–8), acute adaptive immune response (labs 10–12), and immune memory and protection (labs 13–15). All experiments and protocols followed the lab manual “The Immune System: An Experimental Approach” ( Blattman et al. , 2016 ). The purpose of each lab could be copied from the lab manual. In other words, the traditional lab class format was prescriptive or “cookbook.” While students wrote individual hypotheses, the findings were not novel and no outside literature was needed to support the hypothesis; all necessary information to form a hypothesis was in the lab manual. All lab experiments were performed using immune cells from recently killed WT Bl6 mice (IACUC 19-1684T). The class structure was to start with a quiz, review the quiz, answer students’ questions regarding immunology and the day’s lab exercises, and then let the students perform the experiments and, when applicable, gather data the same day. Notebooks were signed at the end of class. Students were encouraged to have everything written in their notebooks and review class material before class started.

CURE Class Format

To transition to a CURE format, students studied mice with a genetic mutation that had not been studied in immunology thereby generating novel data. By collaborating with other laboratories within Arizona State University, students studied what effect knocking out Mohawk (2017, Alan Rawls), having a Raf1L613V mutation (2018, Jason Newborn), or knocking out Z-DNA-binding protein 1 (2019, Bertram Jacobs) had on the immune response. The five areas of immunology studied, the lab manual, and protocols remained unchanged between traditional and CURE formats. The purpose of each CURE lab shifted to understanding how the immune response of a genetically modified mouse differed from the WT mouse. CURE students were required to read outside literature before class started to generate a novel hypothesis. They were instructed to hypothesize how the immune response would differ (better, worse, or no change) between mice and provide their own reasoning as to why. Other than encouraging students to find publications on pubmed, instructors did not help students generate hypotheses. Before experiments started, students discussed their hypotheses in small groups before sharing their different hypotheses with the class. Instructors encouraged students to share their different hypotheses by asking, “Did anyone think the immune response would be better in the knock out mouse? Why? Who thought there’d be no change? Why?” and finally saying, “All these hypotheses are valid. We don’t know the answer yet because the experiment has never been done before.” The class structure was to start with the quiz, review the quiz, answer immunology questions, discuss hypotheses and reasoning, answer questions related to lab exercise, let the students perform the experiments and, when applicable, gather data the same day. Notebooks were signed at the end of class. Because there was no time to generate a hypothesis during class, students were required to read the course material and apply it to the genetic mutation before coming to class.

At the beginning of every lab, students took a quiz on the immunology on which the lab was based and the experiment itself. The first and last quizzes were omitted because the first quiz was used to show students how the class would flow throughout the semester and therefore did not count toward the final grade and the last quiz was a practical to determine student ability to analyze flow cytometry data.

Before class, both teaching methods required students to read the lab material, write the purpose, question, hypothesis, and procedures for the day’s lab in their notebooks, and take a quiz at the beginning of class. Students used the same book with the same optional practice questions and took the same quizzes. Although both class formats required students to come to class prepared, the CURE teaching method enforced that requirement because incorporating real scientific research into the class required CURE students to develop a novel hypothesis on how altering the gene of interest would impact the immune response. Due to time constraints, all reading for generating their novel hypothesis needed to happen before the class started. Immediately after reviewing the quiz, CURE students discussed their hypotheses in their groups for 2 minutes prior to sharing with the class via random call. To receive a notebook signature for the day’s lab, CURE students needed to have citations for their hypotheses. Students were expected to have hypotheses with citations before the start of class beginning with the second lab quiz.

Students wrote lab reports after the first four class sections. The fifth lab report was not included in this analysis since students taking the class 2013–2015 were not told to write a fifth report, and students in 2016–2019 could write the fifth report to replace the lowest report grade. Therefore, the significant difference between report grades was calculated based on reports 1 through 4 without replacing any scores since report 5 was omitted. Regardless of class format, students followed the same report rubric. Each report was worth 50 points, and the points were Introduction-7, Methods-5, Results-12, Discussion-15, References-5, Grammar-2, Legends/captions-2, and Formatting-2. In the introduction, students were expected to provide relevant information, purpose of the experiments, questions answered, and hypotheses. The CURE students not only provided the relevant immunology background for the report but also read additional literature for relevant background information regarding the gene of interest. This background reading (which took place before class and therefore before the quiz) then needed to provide a clear link to the hypothesis. Students were told the hypothesis should answer whether they expected the immune response would be greater, the same, or less than the WT mouse and why. The methods remained unchanged other than the CURE students had one additional sample to run due to also analyzing the immune response from the genetically modified mouse. For results, students in both class formats analyzed immune organ cell counts, flow cytometry data, ELISA results, and cytotoxicity data for their reports. However, students taking the CURE format had additional samples and needed to compare WT results with the genetically altered mouse. While the analysis itself was similar given the rubric (figures, description/summary, how data were obtained, and identifying controls in the experiments), CURE students analyzed two sets of data, learned how to prevent bias between samples, and then compared/contrasted the data in the discussion. In the discussion, both class formats read outside literature and discussed the impact of the data. Both formats analyzed WT data and determined whether the data obtained fit within expected values. For the CURE students, the WT data served as a control that then told them whether they correctly performed the experiment. Therefore, if the WT values fit the expected norm, then the data from the genetically altered mouse, which used the same methods and reagents, could be believed. Students then read further literature to try to understand the reasoning behind the results from the genetically altered mouse and then showed how their research regarding the gene of interest had impact outside of class. Both class formats discussed the impact of the established immunology and why the immunology was important to study (Supplemental Table S1).

The class had two exams: the open book take-home midterm was given as a hard copy before spring break and due when classes resumed and the closed book in-class final.

The students followed the same rubric for lab reports (Supplemental Table S1) and had the same quizzes and exams. Quiz and exam questions consisted of multiple choice, fill in the blank, drawing, short answer, identify cells or organs, and math. Short answer and drawing questions could have resulted in variance in TA grading. However, any variance was mitigated by reviewing all quiz answers in class and exam answers when requested. The professor, who did not change between formats, was present for classes and answered questions regarding which short answers were or were not acceptable and whether partial credit would be granted. Drawings were also reviewed in class using either a whiteboard or TV screens depending on class size.

If the increase in grades in the CURE format were due to students obtaining copies of previous quizzes, then we would expect quiz grades to have started rising during the 4 years the traditional format was taught. The midterm exam was always an open book take home exam given before students left for spring break. The final exam was a closed book, in class exam for both class formats and had the same questions and available number of points.

For both formats, TAs encouraged study practices for the final exam. In the traditional class, students were allowed a notecard during the final. In the CURE format, students had an in-class quiz-like review session 2 days before the final. While the TAs provided different study aids, students still studied on their own. In other words, the students in the traditional class could have still quizzed themselves while students in the CURE class were observed taking notes during the review session.

Regarding lab reports, different TAs graded the lab reports depending on the year. However, the same rubric was followed for grading, and the available number of points for each report remained consistent within the class format.

Finally, the quality of education remained consistent across the different class formats. The same professor was responsible for the class even though the TA teaching the class changed. In both formats, the class TAs were recognized for quality teaching. The traditional format was taught by a TA who was student-nominated and awarded ASU’s Teacher of the Year. The CURE format was taught by a TA who was self-nominated and awarded GPSA’s Teaching Excellence Award.

Student Demographics

GPA, degree program, and academic level were all analyzed in the results section as described below. The class did not have any prerequisites to enroll and was not required by any degree program at Arizona State University. In other words, the likelihood of students enrolling in Experimental Immunology remained consistent between class formats. A lecture class (MIC 420: Basic Immunology) was offered all the years that Experimental Immunology was taught. However, the lecture class was not required, and both the traditional and CURE class formats had a mixture of students who had and had not taken the lecture. Students did not elect to enroll in a CURE or traditional class and were not told prior to enrollment that the class format had changed to a CURE. Other demographics, including prior research experience, were assessed previously and not found to be significantly different between class formats ( Cooper et al. , 2019 ).

Statistical Analysis

Scores from quizzes, reports, and exams were pulled from 2013 to 2019 and analyzed for any differences via GraphPad Prism unpaired t tests. Correction from multiple t tests was done using false discovery rate determined by two-stage step-up (Benjamini, Krieger, and Yekutieli). GPA was also analyzed via unpaired t tests to determine significance.

The Shannon–Wiener diversity index, Chi-squared, and Fisher t test were used to determine level of diversity for degree program, academic level, race, and gender. The Shannon–Wiener diversity index is a way to measure diversity within a population ( Shannon, 1948 ). A t test was used to compare results from the Shannon–Wiener diversity index ( Hutcheson, 1970 ). Further analysis for degree program, academic level, and race used Chi-squared. Significant differences in gender were determined using the Fisher t test.

GraphPad Prism’s multiple regression analysis was used to determine the impact of the predictor variables GPA, class format (0-Traditional, 1-Cure), and academic level on the outcome variable (overall points earned). Further analysis studied the predictor variables on points earned on quizzes, reports, and exams separately.

Scores from quizzes 2013–2016 were analyzed via one-way ANOVA in GraphPad Prism.

Students in CURE Class Averaged ∼5% Higher than Students Taught via Traditional Method

To determine whether students learned more course material in the CURE format, we compared overall course grades and found that students in the CURE class performed better overall with a class average of 80% compared with students in the traditional format course who averaged 75% ( p < 0.0001) ( Figure 1A ). The aggregate semester grade was calculated from student quizzes (79% vs. 71%, p < 0.0001) ( Figure 1B ), lab reports (84% vs. 78%, p < 0.0001) ( Figure 1C ), and exams (82% vs. 77%, p < 0.01) ( Figure 1D ). On all three assessments, CURE format students had significantly improved performance, which suggests incorporating research into the immunology laboratory class resulted in improved understanding and application of the course material.

FIGURE 1. Changing class format to a CURE improved student performance. (A) Students enrolled in the CURE class format demonstrated improved mastery of course material compared with those in the traditional class format based on improved scores in quizzes, lab reports, and exams. (B) Quizzes were given at the beginning of class before the instructor/TA reviewed the material and experimental setup. Based on quiz scores, CURE students demonstrated increased understanding of and preparedness for class. (C) Lab reports assessed scientific writing and ability to analyze and interpret data. Students enrolled in the CURE format performed better overall on reports. (D) Students engaged in research scored higher on exams indicating improved mastery of course material. CURE students n = 139, traditional students n = 119; **** p < 0.0001, ** p < 0.01; unpaired t test was used to test statistical significance with false discovery rate determined by two-stage step-up (Benjamini, Krieger, and Yekutieli).

Changing Class Format to a CURE Improved Majority of Quiz Scores

We used quizzes to test student preparedness for class and understanding of important background information each laboratory period. CURE students achieved significantly higher scores on seven out of 13 quizzes ( Figure 2 ). Early in the semester, the CURE teaching method resulted in students performing significantly better on the second quiz (88% vs. 80%, p < 0.01). The CURE teaching method resulted in continued improved performance when viral infection was mimicked in quiz 5 by studying the innate immune response to poly(I:C) (61% vs. 51%, p < 0.01) and when lymphocyte development was studied in quiz 7 (88% vs. 78%, p < 0.0001) and quiz 8 (88% vs. 81%. p < 0.01). Later in the semester, lymphocyte response to virus was studied, specifically how T cells respond to viral infections. The difference in quiz scores between teaching methods then often exceeded 15% such as in quizzes 10 (76% vs. 58%, p < 0.0001), 11 (82% vs. 61%, p < 0.0001), and 14 (83% vs. 66%, p < 0.0001). Scores for quizzes 12 and 13 were not significantly different between teaching methods (70% vs. 62% and 80% vs. 75%, respectively). When the focus was on learning a new technique instead of forming a new hypothesis, such as quiz 6 (79% vs. 80%) and quiz 9 (77% vs. 75%), no significant difference in scores was noticed. Scores for quizzes 3 and 4 scores were also not significantly different between teaching methods (87% vs. 82% and 67% vs. 71%, respectively). Lab 3 studied cells of the immune system and reviewed fundamentals for flow cytometry, which the class used to analyze data. Lab 4 studied oxidative burst, which occurs when leukocytes encounter a pathogen. Overall, seven of the 13 quizzes were significantly improved for CURE format versus traditional format. Of the six quizzes that were not significantly improved, two involved learning a technique instead of generating a hypothesis before obtaining novel results.

FIGURE 2. Incorporating research into the course resulted in improved quiz scores. Of the quizzes analyzed, students engaged in research earned higher scores in seven of the 13 quizzes. Two of the four quizzes in which there was no significant difference did not have novel data in the lab classes for those quizzes. Unfilled bars represent the traditional format, and filled bars represent the CURE format. CURE students n = 139, traditional students n = 119; **** p < 0.0001, ** p < 0.01, * p < 0.05, ns = not significant; unpaired t test was used to test statistical significance with false discovery rate determined by two-stage step-up (Benjamini, Krieger, and Yekutieli).

Incorporating Scientific Research Resulted in Improved Performance on Reports

While quizzes demonstrated student preparedness for class, we used laboratory reports to assess student analytical skills for interpreting data, as well as critical thinking about how their work applied to current research outside the class. The rubric for grading reports was unchanged between class formats. We found significantly improved scores for all four analyzed laboratory reports from CURE format students compared with scores from traditional format students. As with quizzes, incorporating research into the class benefitted students from the beginning. The CURE format resulted in students earning 6% higher on the first report (74% vs. 68%, p < 0.01). Students appeared to incorporate feedback from the first report regardless of class format given the second report was one letter grade higher for both sets of students. However, the benefit of incorporating research early resulted in the CURE class still scoring 8% higher (87% vs. 79%, p < 0.0001). The third report (89% vs. 83%, p < 0.0001) and fourth report (87% vs. 84%, p < 0.05) also demonstrated improved scientific writing when research was incorporated ( Figure 3 ).

FIGURE 3. Students demonstrated better scientific writing when they produced and analyzed novel data. Lab reports assessed analytical skills and data interpretation and required students to look at contemporary literature to understand how their work was applicable outside class. CURE students were told from the beginning of the semester their work was novel. The same rubric was used for both class formats in which over half the grade came from the results and discussion sections. CURE students scored higher on all analyzed reports. Unfilled bars represent the traditional format, and filled bars represent the CURE format. CURE students n = 139, traditional students n = 119; **** p < 0.0001, ** p < 0.01, * p < 0.05; unpaired t test was used to test statistical significance with false discovery rate determined by two-stage step-up (Benjamini, Krieger, and Yekutieli).

Midterm, but not Final, Exam Scores Improved in CURE Format

Two exams were given to assess student mastery of the course material. A midterm exam assessed student understanding of labs 1–9, and a final exam covered material from labs 10 to 15. The questions and format were the same for exams for the traditional and CURE format courses. Again, students in the CURE format class performed significantly better on the midterm exam compared with students from the traditional format course (88% vs. 83%, p < 0.0001). However, student performance on the final exam did not differ significantly between traditional and CURE format courses (75% vs. 73%) ( Figure 4 ).

FIGURE 4. CURE students scored higher on the midterm but not the final. The exams tested student understanding of the course material; no questions were modified to incorporate research material. All students were provided with the same take-home, open-book midterm to be completed in the same timeframe. Although course-based research was not incorporated into the exam itself, CURE students scored higher on the midterm. The final exam was administered in class after all experiments were completed. CURE students and traditional students performed equally on the final. Unfilled bars represent the traditional format, and filled bars represent the CURE format. CURE students n = 139, traditional students n = 119; **** p < 0.0001, ns = not significant; unpaired t test was used to test statistical significance with false discovery rate determined by two-stage step-up (Benjamini, Krieger, and Yekutieli).

Student Demographics Remained Consistent Between Formats

The improved performance on quizzes, exams, and lab reports in the CURE format course compared with the traditional format course, despite no other differences in format or assessments, suggests incorporating research into a laboratory course increases student mastery. However, other factors including student demographics could also result in this change. To determine whether the student population changed, data on students’ overall academic GPAs, degree programs, and academic levels were analyzed. No significant difference was observed across GPA ( p = 0.07) ( Figure 5C ), degree program ( p = 0.6) ( Figure 5A ), or academic level ( p = 0.4) ( Figure 5B ). Therefore, the students who took the traditional class format were equally capable of mastering the course material as students who took the CURE class format. The improved performance observed in the CURE format was due to incorporating research into the teaching method.

FIGURE 5. Student demographics were unchanged between traditional and CURE formats. (A) Students enrolled in either the traditional or CURE format participated in similar degree programs. CURE students n = 139, traditional students n = 119; Shannon Diversity test followed by an unpaired t test was used to test statistical significance in differences between formats. (B) Both the traditional and CURE formats consisted mostly of seniors. No significant difference was observed in student academic level between the different formats. CURE students n = 139, traditional students n = 119; Shannon Diversity test followed by an unpaired t test was used to test statistical significance in differences between formats. (C) No significant difference was found in student GPAs between the class formats. CURE students n = 139, traditional students n = 119; ns = not significant; an unpaired t test was used to test statistical significance .

Multiple Linear Regression Analysis Indicates the Teaching Intervention Improved Student Scores

Multiple linear regression (MLR) controls for other variables that impact student performance and is therefore a reliable method for determining whether a teaching intervention, such as incorporating research, impacted student performance and learning or whether the change in performance was due to student-intrinsic factors ( Theobald and Freeman, 2014 ). In setting up the MLR analysis, we chose student GPA, class format, and academic level as the predictor, or control, variables. The outcome variable was the total number of points earned in the class. GPA represented overall academic performance. Academic level helped measure preparedness of previous coursework since more senior students would likely have taken more life science classes to prepare them for an upper division immunology course. Class format represented the teaching intervention, which was incorporating research. MLR analysis showed how well incorporating research impacted student performance ( p = 0.0004) in the class when the predictor variables were controlled ( Table 1 ). GPA also served as a good indicator of well a student would do in the class ( p < 0.0001), but academic level had no impact on how well students performed in class. Individual analyses were performed for quizzes, reports, and exams with similar findings (Supplemental Tables S2–S4).

Improved Scores were Due to Changing the Teaching Format without Changing Assessments

If the increase in grades in the CURE format were due to students obtaining copies of previous assessments, especially quizzes, then we hypothesized that we would see significant increases in quiz grades prior to changing class formats. We analyzed quiz averages across the 4 years the traditional lab was taught, 2013–2016. While there was an improvement between 2013 and 2014 (Supplemental Figure S1), no further improvement was noted across the 4 years. 2013 was the first year this course was taught. Nonetheless, we reanalyzed the overall scores for quizzes, reports, and exams to determine whether 2013 falsely lowered the scores from the traditional class format. All three assessment areas remained significant between class formats when the scores from the first ever class were omitted (Supplemental Figure S2).

Assessing Differences in TA Grading Showed No Significance Difference between Formats

The traditional format had four graders over the course of 4 years (two TAs and two assistant TAs). The CURE format had two graders over the course of 3 years (two TAs). The class professor, who was consistent across formats, also graded occasionally. Although unlikely that the clear divide in grades across class formats was due to grading variances since there were seven total graders for the class, we sought to determine whether differences in scores were due to grading. We therefore analyzed the coefficient of variance (%CV) within samples and compared the two class formats. Any %CV due to student-intrinsic factors would be similar between teaching methods because student demographics were comparable between class formats. If %CV were significantly different between formats, then other factors, including differences in TA grading, would likely be responsible. The %CV for quizzes, reports, and exams were not significant between class formats ( p = 0.0671, 0.3162, and 0.8858, respectively) ( Figure 6 ), which suggests that the grading rigor was comparable between class formats.

FIGURE 6. Grading practices were comparable between traditional and CURE formats. (A) The 13 quizzes were analyzed for %CV to determine intravariability in grading in both traditional and CURE formats. The %CVs were then analyzed via unpaired t test. No significant difference in intravariability was found between class formats. (B) The four reports were analyzed for %CV to determine intravariability in grading in both traditional and CURE formats. The %CVs were then analyzed via unpaired t test. No significant difference in intravariability was found between class formats. (C) The two exams were analyzed for %CV to determine intravariability in grading in both traditional and CURE formats. The %CVs were then analyzed via unpaired t test. No significant difference in intravariability was found between class formats.

Experimental Immunology Taught a Diverse Student Population

CUREs are known to include more students in research and have a broader outreach than the traditional one-on-one mentoring method ( Bangera and Brownell, 2014 ; Burmeister et al. , 2021 ). Both the traditional and CURE class formats rated high on the Shannon–Wiener diversity index with richness scores of 7 and 11, respectively. There was no significant difference between class diversity as calculated via Shannon–Wiener diversity index ( p = 0.2) or Chi-squared test ( p = 0.3255) ( Figure 7A ). Both the traditional and CURE class formats had over 50% female students, and the ratio of female to male students was not significantly different between class formats as calculated via Shannon–Wiener diversity index ( p = 0.2) or Fisher’s exact test ( p = 0.1298) ( Figure 7B ). Overall, the Experimental Immunology class serves a diverse group of students. By changing the class format to incorporate research, we included the diverse student population we serve in critical thinking and problem solving.

FIGURE 7. Traditional and CURE formats served equally diverse student populations. (A) Students enrolled in the course came from diverse racial backgrounds, several of which are underrepresented in science. CURE students n = 139, traditional students n = 119; Shannon diversity test followed by an unpaired t test was used to test statistical significance in differences between formats. Chi-squared test was also tested. No significant difference was observed between class formats. (B) More women than men enrolled in Experimental Immunology. CURE students n = 139, traditional students n = 119; Shannon diversity test followed by an unpaired t test was used to test statistical significance in differences between formats. Fisher’s exact test was also used. No significant difference was observed between class formats.

While incorporating research into existing laboratory courses benefits students by encouraging critical thinking, problem solving, and reading current literature to show how their work is novel and applicable outside class, quantitating the impact of integrating research on student mastery of the course material has been difficult ( Linn et al. , 2015 ). We changed the format of an upper division immunology lab course into a CURE class by having students study the immune response of previously uncharacterized genetically altered mice compared with the known response of WT mice. The class structure, such as the experiments performed, lab manual used, and assessments, remained unchanged between formats thereby allowing us to compare the effect incorporating research has on student performance in the class. We realized retroactively that we were therefore in a unique position to determine whether incorporating research improved student learning of the original course material as evidenced by improved scores.

Overall, we found incorporating research into the class resulted in students performing significantly better in all assessment areas. The overall difference in student performance was not surprising since every assessment showed that incorporating research improved student performance. The difference in quiz scores could be due to the nature of the hypotheses required for both classes. Because students studied known outcomes in the traditional class format, the lab manual often provided enough information for students to know what to expect and why. However, the lab manual did not detail any genetic mutations. While CURE students would have read the same immunology background from the lab manual, they had the additional responsibility to apply what they learned to whether a genetic mutation would impact the immune system and, if so, how. Encouraging students to apply their knowledge before taking any assessment likely resulted in improved learning of the course material and therefore higher quiz scores ( Freeman et al. , 2014 ).

Incorporating research improved students’ scientific writing as evidenced by improved lab report scores. Writing about real scientific results in the results and discussion sections, which are responsible for over half the lab report grade, likely made scientific writing more approachable. For example, the discussion section required students to compare their results with outside literature and explain why their results did or did not agree with current literature. The CURE teaching method required students to start reading outside literature before writing the report. Therefore, CURE students had an advantage regarding which literature sources to cite for the discussion because they already read multiple sources to formulate a hypothesis. The increased ownership and engagement in the class ( Cooper et al. , 2019 ) may have also resulted in increased scores as they had to write why their novel results were important outside of class ( Conley and French, 2014 ; Cannata et al. , 2019 ; Martínez et al. , 2019 ). The traditional teaching format encouraged outside reading before writing the report but did not require it. The results were not novel for the traditional teaching method, and therefore the impact of what was studied focused less on their results and more on how the experiments studied are still used in current research.

The significant difference in exam scores resulted from the CURE teaching method improving student scores by 5% on the midterm exam. Because the midterm exam was an open book take home exam completed over spring break, students in both class formats had equal access to the lab manual and previous quizzes to do equally well on the exam. Therefore, the difference in scores was likely due to student motivation to devote the time to do well on the exam ( Dweck, 1986 ) possibly due to increased project ownership ( Cannata et al. , 2017 ). The midterm also correlated with the increased peak in quiz performance suggesting that students in the CURE format exhibited higher levels of engagement immediately after spring break.

The difference in scores began to decline when quizzes and assignments for the class overlapped with projects necessary for students to graduate, such as capstone and honors thesis projects. If students experienced equal levels of burnout or had multiple assignments due around the time the final was taken, then student engagement in the class would be comparable between class formats and therefore explain why the scores on the final exam were not significantly different.

Active learning encourages students to take ownership of their education by actively participating in what they learn. By changing the lab format to a CURE class, students received guidance on how to look up and interpret journal articles. Once empowered in ways to educate themselves and look up information beyond what was provided in the course materials, the improved scores suggest students truly engaged in the class and took ownership of their projects and their education. Teaching the students scientific processes, such as graphing, data analysis, experimental design, scientific writing, and science communication, before students enrolled in introductory science classes improved content learning when students later enrolled in introductory biology classes despite minimal differences in student GPA/SAT scores ( Dirks and Cunningham, 2006 ). CUREs teach scientific processes alongside class material ( Auchincloss et al. , 2014 ). Our work supports that CUREs are an effective way to improve undergraduate education by engaging more students in scientific research. We showed transitioning to a CURE format resulted in a similar improvement in scores compared with teaching scientific processes separately. This further shows the CURE format enhanced student learning of course material rather than distracted from it, which is a concern raised when educators express reasons for not integrating more active learning in their courses ( Kim et al. , 2018 ; Shadle et al. , 2017 ; Ul-Huda et al. , 2018 ).

One limitation of this study is that different TAs were present and responsibilities, such as grading, shifted as TAs shifted. To mitigate variation, answers were reviewed in class whenever possible (always for quizzes, upon request for exams). Regarding quizzes, since all those answers were approved by the professor who was present for both class formats, CURE students performed better than traditional students in seven of the 13 quizzes, or seven of the 11 quizzes that involved applying their knowledge to a genetic mutation. A subset of assessments was unavailable to regrade to verify the impact of adding research because assessments were handed back to students. Nonetheless, most questions did not have multiple correct answers. The only questions that could have different points awarded based on TA leniency were drawings and short answers. To understand whether there were any significant differences, we compared %CV between formats. We showed through multiple analyses (MLR, Chi-squared, diversity index, and previous work from Cooper et al. (2019) ) that student demographics were not responsible for the difference in scores. Any %CV related to student ability would remain consistent between formats. We reasoned that a change in %CV would therefore be due to other variables, such as TA grading leniency. Our data showed that %CV was not different between class formats. Therefore, we do not believe having different graders significantly impacted the scores because no variance was noted within the class formats themselves.

We also analyzed whether academic dishonesty in the form of obtaining quizzes from prior years could have resulted in improved scores. While the first year the class was taught did have lower scores compared with subsequent semesters, no further improvement occurred. The improvement from 2013 to 2014 likely resulted from having an experienced professor and experienced TAs.

Another limitation of this study is we had no information on differences in family support and/or responsibilities (parents, spouse, children), socioeconomics such as whether the students were working to support themselves or were supported by family, and other personal factors that could affect student performance in the class ( Mushtaq and Khan, 2012 ). Nonetheless, previous studies showed prior test scores, course knowledge, and experience had the highest correlations to student performance when compared with other factors such as learning/teaching styles, gender, and family stress ( Van Lanen et al. , 2000 ; Clark and Latshaw, 2012 ; Mushtaq and Khan, 2012 ). Because no significant difference was noted in GPA, degree program, or academic level between the two formats, the improved scores likely resulted from the teaching method and not the students. This was further supported via MLR analysis in which class format was found to contribute to student performance when all other variables, including GPA, were controlled. GPA was accessed when most of the students had graduated Arizona State University, which means the GPA analyzed was likely the students’ final GPAs or close to their final GPAs. We recognize that our students had both visible and invisible diversities that were not disclosed in interviews or through demographic information. Therefore, measuring to what extent transitioning the class format to a CURE class impacted each demographic is outside the scope of this study. Nonetheless, we did note that students in the CURE class performed better overall. We hope this information encourages educators to include active learning, particularly course-based research, in their classes and universities to offer rewards and incentives for educators to update courses as needed to improve student engagement and thereby improve student mastery of course material.

Overall, we showed incorporating research into an upper division lab improved student learning and mastery of course material. Changing the class to a CURE format resulted in students experiencing increased project ownership, which was likely associated with increased engagement in the course and ownership of their education which then translated to improved scores on assessments. We were able to show this because the overall class structure and assessments remained unaltered between the traditional and CURE class formats; the only change was students studied how a previously uncharacterized gene impacted immune system development, response, and memory. Over the course of 3 years, 139 students from diverse backgrounds, some of which are underrepresented in science, participated in scientific research through this class, which supports that CUREs can engage large numbers of diverse students in science ( Auchincloss et al. , 2014 ).

ACKNOWLEDGMENTS

We would like to thank all the labs that collaborated with Experimental Immunology to provide genetically modified mice. While the labs were already breeding mice for their own research, we are grateful for the work they put in to breed additional mice for this class. Special thanks to Cherie Alissa Lynch (2017), Michael Holter (2018), and Karen Kibler (2019) for helping make the CURE class possible. Student fees for Arizona State University’s MIC 421 Experimental Immunology class 435 were used to fund the class experiments.

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Submitted: 23 May 2022 Revised: 9 January 2024 Accepted: 22 July 2024

© 2024 N. T. Appel et al. CBE—Life Sciences Education © 2024 The American Society for Cell Biology. This article is distributed by The American Society for Cell Biology under license from the author(s). It is available to the public under an Attribution–Noncommercial–Share Alike 3.0 Unported Creative Commons License (http://creativecommons.org/licenses/by-nc-sa/3.0).

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Digital literacies in the classroom: Authentic opportunities for student engagement

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In efforts to improve students' digital literacies on a STEM-focused campus, one university created a digital literacies initiative to support both faculty and students. Faculty development programming supported the development of assignment parameters, detailed assessment rubrics, and scaffolding activities. A campus tutoring center was piloted to support students' acquisition of digital literacies. This chapter offers examples from three faculty members who participated in the digital literacies initiative and implemented digital literacy assignments in their courses. The researchers offer best practices for campuses interested in developing digital literacy initiatives.

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N2 - In efforts to improve students' digital literacies on a STEM-focused campus, one university created a digital literacies initiative to support both faculty and students. Faculty development programming supported the development of assignment parameters, detailed assessment rubrics, and scaffolding activities. A campus tutoring center was piloted to support students' acquisition of digital literacies. This chapter offers examples from three faculty members who participated in the digital literacies initiative and implemented digital literacy assignments in their courses. The researchers offer best practices for campuses interested in developing digital literacy initiatives.

AB - In efforts to improve students' digital literacies on a STEM-focused campus, one university created a digital literacies initiative to support both faculty and students. Faculty development programming supported the development of assignment parameters, detailed assessment rubrics, and scaffolding activities. A campus tutoring center was piloted to support students' acquisition of digital literacies. This chapter offers examples from three faculty members who participated in the digital literacies initiative and implemented digital literacy assignments in their courses. The researchers offer best practices for campuses interested in developing digital literacy initiatives.

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Study finds program boosts cognitive engagement of students with language and attention difficulties

A QUT-led study has found high school students with disabilities impacting language and information processing were able to better comprehend content when teachers adopted evidence-based strategies to increase the accessibility of classroom teaching.

The study, funded by the Australian Research Council Linkage Projects scheme and published in Learning Environments Research , is led by QUT Centre for Inclusive Education researchers Ms Haley Tancredi , Professor Linda J. Graham and Dr Callula Killingly , and Professor Naomi Sweller of Macquarie University.

The study involved 56 year 10 students and found a statistically significant increase in cognitive engagement when their teachers participated in the Accessible Pedagogies program.

Doctoral student Haley Tancredi, who was first author of the study, said the two most common disabilities impacting language and information processing were Developmental Language Disorder (DLD) and Attention Deficit Hyperactivity Disorder (ADHD), which together account for about 14 per cent of school students – or four students in every classroom of 30.

Ms Tancredi said students with DLD and ADHD often “ hide in plain sight ” in the classroom and underachieved relative to their potential. However, many of the barriers these students face could be proactively removed using accessible teaching practices.

Ms Tancredi and Professor Graham, Director of the QUT Centre for Inclusive Education , have spent the last seven years developing a program of learning to help teachers reach these students whether they have been identified or not.

“In prior research , we investigated the impact of the Accessible Pedagogies program on the accessibility of teachers’ practice,” Professor Graham said.

Professor Graham said findings were extremely positive with most teachers demonstrating improvements across a range of accessible teaching practices targeted in the program.

Ms Tancredi said this latest research “aimed to determine whether improved accessibility was noticed by students and whether it made any difference to their engagement and classroom learning experience”.

“Students participating in this study were interviewed before and after their teachers underwent the program,” Ms Tancredi said.

Professor Graham said that when asked what their teacher did to help them pay attention and to understand, students whose teachers participated in Accessible Pedagogies explicitly described increased use of practices emphasised in the program.

“Importantly, students did not know whether their teachers were participating in the program, nor did they know its content,” Professor Graham said.

Professor Sweller said students also completed questionnaires assessing their classroom engagement before and after their teachers participated in the Accessible Pedagogies program.

“Students whose teachers participated in the program reported a significant increase in cognitive engagement, which is a measure of the level of investment that students put into comprehending complicated ideas and mastering content. There was no such increase for students whose teachers did not participate,” Professor Sweller said.

Ms Tancredi said that the team’s findings “suggest that teachers’ use of Accessible Pedagogies may help students redirect their mental effort towards learning, rather than expending that effort to overcome unnecessary instructional barriers”.

“The practices in Accessible Pedagogies are essential for students with DLD and ADHD, but help all students to pay attention, understand, and learn. Future research will investigate the use of Accessible Pedagogies in primary school classrooms and in schools serving disadvantaged communities,” Ms Tancredi said.

Read the full article, Investigating the impact of Accessible Pedagogies on the experiences and engagement of students with language and/or attentional difficulties , published online in the Learning Environments Research journal.

Main image (left to right): Haley Tancredi, Professor Linda Graham and Dr Callula Killingly.

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Great ‘techspectations’ by customers let down by retailers

QUT researchers have found that while Australians generally trust retail technology, they remain hesitant to swiftly adopt new advancements, largely due to concerns over security and privacy.

Digital map of Australia’s environmental health vulnerabilities

Another step in the creation of a national digital environmental health decision-support platform has been awarded $1.9 million from the Medical Research Future Fund National Critical Research Infrastructure program.

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A systematic review of the impact of emerging technologies on student learning, engagement, and employability in built environment education.

1. Introduction

2. technology in education, 2.1. enhancing teaching methods and learning experience, 2.2. addressing industry demands and real-world applications, 2.3. improving employability and soft skills development, 2.4. research gaps, 3. research method, 3.1. the review process, 3.2. database and keywords, 3.3. study selection process with inclusion and exclusion criteria, 3.4. data analysis, 4. review results, 4.1. descriptive analysis, 4.2. thematic analysis, 4.2.1. commonly used technologies in be education, 4.2.2. enhancing student engagement through technology in be education, 4.2.3. improving learning outcomes with technology in be education, 4.2.4. enhancing employability skills through technology in be education, 4.2.5. challenges in implementing technologies in be education, 5. conclusions, 6. future research, 7. theoretical and practical implications, author contributions, data availability statement, conflicts of interest, nomenclature.

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Ghanbaripour, A.N.; Talebian, N.; Miller, D.; Tumpa, R.J.; Zhang, W.; Golmoradi, M.; Skitmore, M. A Systematic Review of the Impact of Emerging Technologies on Student Learning, Engagement, and Employability in Built Environment Education. Buildings 2024 , 14 , 2769. https://doi.org/10.3390/buildings14092769

Ghanbaripour AN, Talebian N, Miller D, Tumpa RJ, Zhang W, Golmoradi M, Skitmore M. A Systematic Review of the Impact of Emerging Technologies on Student Learning, Engagement, and Employability in Built Environment Education. Buildings . 2024; 14(9):2769. https://doi.org/10.3390/buildings14092769

Ghanbaripour, Amir Naser, Nima Talebian, Dane Miller, Roksana Jahan Tumpa, Weiwei Zhang, Mehdi Golmoradi, and Martin Skitmore. 2024. "A Systematic Review of the Impact of Emerging Technologies on Student Learning, Engagement, and Employability in Built Environment Education" Buildings 14, no. 9: 2769. https://doi.org/10.3390/buildings14092769

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Schools struggling to find solutions to chronic absenteeism problem

by CORY SMITH | The National Desk

(TND) — Schools are having limited success in reducing chronic absenteeism, according to a new RAND report .

And more students are missing school than they were before the pandemic.

School leaders seem to believe that the pandemic shifted perspectives to the detriment of students.

“Yeah, it seems like parents are viewing school as more optional,” Heather Schwartz, an education policy expert and researcher at RAND, said Tuesday of the feedback she’s heard from school district leaders.

RAND’s report, released last week and based on a survey and interviews conducted late last school year, determined that 19% of students were chronically absent.

That’s down from the peak of 28% during the 2021-22 school year but remains elevated from the 13% and 15% absenteeism rates from 2016 to 2019.

And about 10% of districts had 30% or more of their students chronically absent last school year.

Chronic absenteeism is defined as a student missing at least 10% of their school days. A typical school year is 180 days, so a chronically absent student would be out of class for at least 18 days.

The American Federation of Teachers says chronic absenteeism predicts both low academic success and potentially which students may eventually drop out of school.

RAND found a quarter of districts didn’t find their approaches to reducing chronic absenteeism particularly effective.

Common approaches have been the adoption of an early warning system to flag students who are at risk of being chronically absent, home visits, having teachers call students’ homes when they miss school, and hiring staff focused on reducing absenteeism.

“It was disheartening to see the lack of any clear approach, any clear winner among the approaches,” Schwartz said.

Why are more students missing school?

There were common themes RAND heard from district leaders.

Some families, it seems, might be more apathetic towards school since the pandemic.

Some parents seem to have more zealous concerns about sending their children to school when they have minor illnesses, like colds.

There seems to be a growing sense among parents that doing class assignments online is a “good enough” replacement for attending school in person.

And then there’s an increased desire to ease anxiety or mental health concerns children might have about going to school.

“I would say those are the four buckets we hear,” Schwartz said.

Those sentiments seem to be more prevalent now than they were before the pandemic.

“To be fair, we didn't ask them (to) look in the crystal ball and be like, ‘Do you think this is forever?’ So, I don't know if they see it as like a receding tide that's still hanging around, or if it's ... the new normal,” Schwartz said of their survey of school leaders.

How can we fix this?

Schwartz said it’s clear there’s no one-size-fits-all solution to chronic absenteeism.

The same approach in two different places is going to yield different results, she said.

“The culture of a community, the size of the district, the types of students they serve, the needs of the family, the availability of transportation, on and on, are all highly dependent on the local context,” Schwartz said.

Beyond that, she said schools need to tailor their messages to the needs and challenges of different families.

A student who misses a lot of class time in one concentrated chunk is different than a student who misses a lot of days sprinkled throughout the school year.

A student who is missing time and struggling to keep up is different from a student who is missing time but managing to stay on track.

Schwartz said schools need to invest in testing different messages for different student populations.

“The problem is important enough that it's worth investing to find a solution,” she said. “Solutions, plural, because there isn't going to be just any one solution.”

And any approach needs to include “carrots, not just sticks,” she said.

Schools can’t just send truancy officers to homes and solve chronic absenteeism, she said.

“To fundamentally fix this issue, I don't think you can merely hang a bunch of threats over families. ... Make school compelling enough that kids actually feel that they in some sense want to come,” Schwartz said.

And relationship-building is key to making students want to go to school, she said.

“There is an adult who knows you, who notices when you're not there, cares about you, and who wants to see you there at school,” Schwartz said.

Become a Civic Engagement Scholar

Now in its 15th year, the Civic Engagement Scholars program hosted by the Center for Civic Engagement and Learning (CCEL), promotes and recognizes meaningful student involvement in the community. 

Scholars choose to complete a track of 25 or 45 civic engagement activity hours over the course of the academic year and participate in community-focused educational events. Upon successful completion, Scholars are recognized for their commitment to the community. 

The program is open to both CWRU undergraduate and graduate students of all years and majors. The Scholars enrollment form is due by 11:59 p.m. ET on Monday, Sept. 16. 

Interested students can visit the Civic Engagement Scholars website for more information or contact Erin Corwin with any questions.