research on student engagement and achievement

• 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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Additional file 1..

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

Additional file 2.

Indicators of engagement and disengagement

Additional file 3.

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

Additional file 4.

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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Student Engagement: What Is It? Why Does It Matter?

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This chapter considers the relationships of student engagement with ­academic achievement, graduating from high school, and entering postsecondary schooling. Older and newer models of engagement are described and critiqued, and four common components are identified. Research on the relationship of each component with academic outcomes is reviewed. The main themes are that engagement is essential for learning, that engagement is multifaceted with behavioral and psychological components, that engagement and disengagement are developmental and occur over a period of years, and that student engagement can be modified through school policies and practices to improve the prognoses of students at risk. The chapter concludes with a 13-year longitudinal study that shows the relationships of academic achievement, behavioral and affective engagement, and dropping out of high school.

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 Adapted from Fredericks et al. ( 2004 , p. 60).

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Finn, J.D., Zimmer, K.S. (2012). Student Engagement: What Is It? Why Does It Matter?. In: Christenson, S., Reschly, A., Wylie, C. (eds) Handbook of Research on Student Engagement. Springer, Boston, MA. https://doi.org/10.1007/978-1-4614-2018-7_5

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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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Jordan Amoako ’24, center, is a biology and society major in the College of Arts and Sciences.

Through service, students deepen Cornell’s ties to community

By aaron arm strategic communications.

Beyond Cornell’s main campus, thousands of students can be found serving others in the Ithaca area, from tutoring in schools to assisting in animal shelters to language translation and interpretation. The Einhorn Center for Community Engagement helps Cornellians make a positive impact both locally and globally – through learning courses, research and student programs. To celebrate National Volunteer Week, a few students shared their experiences. 

Jordan Amoako ’24 is a biology and society major in the College of Arts and Sciences. She is a member of the Raising Educational Attainment Challenge (REACH) program, where she tutors and mentors students in local schools. She also volunteers at the Ithaca Free Clinic.  

What drew you to REACH? 

For me, education is essential. Coming from a first-generation [American] household, I’ve seen how mobilizing education can be. My brother and I, around our first year of high school, founded a nonprofit called the One Book Foundation, which emphasizes increasing child literacy rates within Ghana. Education has always been something that I’ve had incredible value in.  

Is there a moment that stood out to you when working with a student? 

Something that stood out to me is the eagerness to learn. The students want to learn. They want to be in the classroom. They’re asking questions, are so inquisitive, and they know so much more than I think I ever did when I was in fifth grade. 

Has your work in the community sparked any interest or inspiration for your future plans? 

I see myself working as a medical educator, wanting to educate BIPOC groups and serve as a source of information to help close health care disparities. Education is something that we can all use to help create change in the world, no matter what profession you have.  

Dean Zhang ’25 is majoring in biological sciences, computer science and music in the College of Arts and Sciences. As a member of Elderly Partnership at Cornell, he serves at the Beechtree Center for Rehabilitation & Nursing, where he spends time with senior residents.

Dean Zhang ’25 is majoring in biological sciences, computer science and music in the College of Arts and Sciences.

Is there an experience with the Elderly Partnership that was particularly rewarding? 

A particularly rewarding achievement was the success of our "Advancing Technology for Senior Citizens" project. This initiative aimed to improve technological literacy and diminish the isolation many elderly residents felt, especially highlighted during the pandemic, by providing them with technology to connect with their families and the outside world. Seeing their excitement and engagement with the technology was incredibly fulfilling and reinforced the importance of our work.  

Based on the work your program has done so far, what do you think the future holds? 

Looking ahead, I envision deeper ties between the elderly and the Ithaca community, reducing isolation and enhancing their sense of belonging. We aim to further empower seniors through technology, fostering connections with their families and the wider world. Our current efforts have laid a solid foundation, and I am excited about the future possibilities for enriching the lives of our elderly community members through greater inclusivity and connectivity. 

What advice would you give to someone who’s considering getting involved in community engagement at Cornell, either with this partnership or a different group? 

My advice is to stay true to yourself and embrace what makes you distinctive. For instance, my own connection with a resident over our shared enthusiasm for football and the Super Bowl highlighted how personal interests can forge unexpected bonds. Every interaction is a unique opportunity to learn, grow, and support a cause. Approach these experiences with an open heart and an eager mind.  

Wendy Lin ’25 is an industrial and labor relations major in the ILR School. She is a member of the Community Partnership Funding Board (CPFB), a grant funding board for undergraduate students. CPFB is dedicated to initiating and supporting grassroots, community action projects.

Wendy Lin ’25, left, is an industrial and labor relations major in the ILR School.

When and why did you join CPFB? 

I began volunteering with the Community Partnership Funding Board during my first semester at Cornell as a spring semester second-year student. I knew I wanted to get involved in the community, especially within Tompkins County, because I believe that one way to familiarize yourself with a community is to serve. 

Tell me about the other students or faculty involved in this program. 

I am beyond grateful to my fellow board members and our advisor, Joyce Muchan. I definitely found “community” within the Community Partnership Funding Board and the Einhorn Center for Community Engagement. I am always learning from my peers and value their opinions, perspectives, and experiences. Moreover, when I am surrounded by my peers, I know I am part of a larger community of student leaders. 

Is there anything you’d like to add about community engagement at Cornell and in the surrounding community? 

Students are involved in campus service at Cornell, but I don’t think they know just how much they can accomplish by partnering with local community members and organizations to enact social change. They may also underestimate the lens that local community members operate with to address prevailing injustices. 

Courtney Knight ’25 is a philosophy and government major in the College of Arts and Sciences. She is a member of Compass at Cornell, which supports activities for at-risk youth at George Junior Republic (GJR) High School.

Courtney Knight ’25, left, is a philosophy and government major in the College of Arts and Sciences.

What sort of work or activities have you done with GJR students? 

The first program I was involved with was a chess club, run by me and the former president of Compass during the spring of 2023. We would go to GJR once a week and teach the students chess strategy, play chess with them, and simply interact with the kids there and learn about their experiences or answer any questions they have about our experiences. During the fall of 2023, I ran an arts and creative writing club at GJR with three of our “G-Body” members. 

What’s the Compass program like? 

The Compass volunteer group consists of students with a passion for public service. We work with kids from rough pasts and are aware of how we interact with them, as to not exacerbate any hardship they have experienced in the past. This dedication to creating safe and healing spaces for kids that we interact with for an hour a week is a very unique and admirable quality in all of our volunteers. Programming in this air of gratitude has positively influenced my outlook on life and illuminated the hidden blessings that happen every day. 

Volunteering in the Tompkins County community has been a breath of fresh air for me while at Cornell. Being able to escape the barrage of obligations and rigorous academic responsibilities Cornell demands while giving back to a community that I have been a part of for almost three years reminds me to appreciate the greater beauties of humanity. 

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Student spotlight: Kelly Richmond

Graduate student research

By | Katya Hrichak , Cornell University Graduate School

Kelly Richmond is a doctoral candidate in performing and media arts from Toronto, Ontario, Canada. She earned her B.A. in English drama and theatre and psychology at McGill University and now studies the role of live performance in responding to the climate crisis under the guidance of Sara Warner at Cornell.

What is your area of research and why is it important?

My research considers the role of theatre and live performance in responding to the climate crisis. I look at how examples of queer, feminist, and Indigenous contemporary performance portray environmental and ecological issues, and how they effectively harness the medium of live performance to do so. Of particular interest to me is how theatre can help us re-imagine and re-present relationships between humans and non-human nature through co-presence with un/natural in/humans.

What are the larger implications of this research?

The problem of the climate crisis is no longer a problem of technology or policy; it is a problem of implementation. We have the renewable energy technologies developed; we have Green New Deal policies to decarbonize the economy drafted. What we don’t have is sufficient public refusal to continue current patterns of consumption, or mass enthusiasm for the vibrant connections that emerge when we turn towards circular sustainable community. What we need now is a cultural shift, a storytelling shift, one in which the climate crisis is imagined not dystopian apocalypse but a once-in-an-epoch opportunity to renew, restore(y), and repair. As a unique hub of localized ritual, gathering, and storytelling, theatre has a key role to play in this shift.

What does it mean to you to be a Bouchet Scholar? How do you exemplify the five pillars of the Bouchet Society—character, leadership, advocacy, scholarship, and service?

What I appreciate most about the Bouchet Society values is that they remind us that the role of the scholar is not limited to research and teaching within the confines of the academy; as scholars we exist within a greater cultural and intellectual ecosystem with which we must be response-able and care-full. For me, this has meant uniting my teaching, research, and artistry around community-based events that involve participants in environmental actions.  The Ithaca Department of Arts and Futures  (2023), a day-long performance action which brought together artists, agricultural workers, sustainability specialists, and students to envision a climate-resilient future in Tompkins County;  Haunted Natures Hidden Environments (2022), an immersive-environmental performance which brought together four PMA courses and over 90 artists to create a walk-through installation devised from queer, feminist, and Indigenous environmentalist plays; and eTRASH (2020), a six-week workshop series on devised theatre methods and their application for environmental activism. Each of these projects was designed to remove barriers to access; for example, no prior performance or environmentalist experience was required to participate, course credit or monetary honorariums were offered as compensation for labor, and food was provided at each meeting. I understand that taking care of my collaborators is a prerequisite to ensure that no one is barred from participating in my projects.

President Pollack has designated this academic year’s  theme  as freedom of expression. What does freedom of expression mean to you?

Freedom of expression requires freedom to physically/visually/aurally/temporally disrupt the public sphere. Freedom of expression requires freedom to become an inconvenience to systems of power and to business as usual. Freedom of expression requires freedom to create theatrical protest movements and experiment with new ways of being in public together. Freedom of expression on campus requires being allowed to make such experimentations a part of course work and extracurricular culture without fear of retribution from campus authorities or administrative policies. 

What are your hobbies or interests outside of your research or scholarship?

I volunteer with Brown Coat Cat Rescue where my partner and I specialize in bottle-feeding orphaned neo-natal kittens and caring for other special needs rescues; over the past 24 months we’ve successfully fostered 50 cats, all of whom are now vaccinated, spayed/neutered, and adopted. I take classes at Circus Culture where I’m training in lyra and handstands and I am a part of a wonderful fitness community at CrossFit Vertical. I also love baking, vegetarian cooking, hiking, and camping.

Why did you choose Cornell to pursue your degree?

I wanted to work with my incredible supervisor Prof. Sara Warner. Reading her book “Acts of Gaiety” during my undergraduate studies was transformative for my little lesbian life.

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• MyU : For Students, Faculty, and Staff

IMPORTANT BUILDING CLOSINGS: The following campus buildings on the Twin Cities campus will be closing at 2 p.m. on Monday, April 29, 2024: Coffman Union, Weisman Museum, Hasselmo Hall, Ford Hall, Vincent Hall, Murphy Hall, Tate Lab, Morrill Hall, Northrop, Johnston Hall, Walter Library, Smith Hall, and Kolthoff Hall . Please exit the buildings prior to 2 p.m. All other East Campus buildings will be on U Card access only.

NSF Graduate Research Fellowship Program honors nine Chemistry student community members

MINNEAPOLIS / ST. PAUL (4/26/2023) – Nine members of the Department of Chemistry student community were recently honored with recognition by the National Science Foundation Graduate Research Fellowship Program (NSF GRFP). Briana Krupinsky, Grace Murphy, Timmy Nguyen, and Ulises Perez were awarded fellowships, and Mrinalni Iyer, Killian MacFeely, Wallee Naimi, Miles Willis, and Ali Younis received honorable mentions.

Briana Krupinsky is a second-year graduate student in the Lamb group . She joined the UMN community after completing her undergraduate studies at the University of North Dakota. Briana investigates N-hetereocyclic carbene-carbodiimide (NHC-CDI) adducts for application as catalyst precursors in organocatalysis. At the moment, this includes working towards understanding the thermodynamic and kinetic properties of NHC-CDI adducts for well-controlled catalysis. One of Briana’s research goals is to develop a light-activated NHC-CDI catalyst precursor to achieve spatiotemporal control for the synthesis of polymers.

Grace Murphy , a member of the Hoover lab , came to UMN after completing her undergraduate studies at Saint Louis University. One of her long-term goals as a chemist is to study and develop transition metal catalyzed reactions that are used in organic chemistry. She is particularly interested in understanding the structure-reactivity relationships that make difficult reactions possible. Grace is currently working towards understanding the mechanism of nickel catalyzed/mediated decarbonylation, a reaction that has potential future applications to the synthesis of pharmaceuticals to polymer upcycling.

Timmy Nguyen first came to UMN for a summer research experience program in 2022, right before his senior year at California State Polytechnic University, Pomona. He officially joined the graduate program in 2023 as a member of the Haynes group. Timmy is interested in anisotropic nanoparticles as substrates and recently started working on a project to synthesize silica-coated gold nanorods for use in SERS sensors. He is also passionate about participating in outreach activities through Science for All, a student group that works to bring the excitement of science to Minnesota middle schools.

Ulises Perez , a Spring 2023 graduate from the UMN Chemistry undergraduate program and current PhD student at University of Washington, was also awarded a fellowship.

The NSF GRFP recognizes and supports outstanding graduate students in NSF-supported science, technology, engineering, and mathematics disciplines who are pursuing research-based master’s and doctoral degrees at accredited United States institutions. The program also seeks to support the participation of underrepresented groups in STEM graduate studies.

Mrinalni Iyer, Killian MacFeely, Wallee Naimi, Miles Willis, and Ali Younis received honorable mentions for their applications. The Department of Chemistry congratulates all nine students on this significant national academic achievement!

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Master of Science in Gerontology Readies Student for Career in Global Public Health and Research

Home » Master of Science in Gerontology Readies Student for Career in Global Public Health and Research

For Alexander Woodman, a Master of Science in Gerontology was an important step in becoming a leading health researcher in the Middle East.

Alexander Woodman, MPH, MSG, PhD and Fulbright research scholar, has traveled across the globe to study public health and aging. From Southeast Asia to France and the Middle East, his guiding values as an investigator have been equity, justice and care.

Woodman is currently a U.S. Fulbright Research Scholar at the Arabian Gulf University School of Medicine in Manama, Bahrain. He contributes to the complex obesity map of the Gulf Cooperation Council, an initiative launched in Saudi Arabia in collaboration with leading research experts at King Fahad Medical Complex, where Woodman previously worked as the Head of Research. For nearly ten years, he has collaborated with leading clinical researchers, doctors, medical educators and ethicists in the Gulf region and beyond. He credits his time at the USC Leonard Davis School for preparing him for this important work.

Developing a sense of service and compassion

When Woodman was an undergraduate at UCLA, he volunteered at Cedars Sinai Medical Center where he spent time with patients in the intensive care unit. At Cedars Sinai, he personally witnessed people experiencing physical and psychological pain. It became the core basis for his lifelong mission of service and social engagement.

“Volunteering, which I did with passion, helped me develop a deeper understanding of service and compassion for the elderly and vulnerable populations,” he says. “It cultivated within me the person I am today and led me to the fields of medical research, public health and gerontology.”

Finding purpose in research

Woodman was drawn to research that improves people’s quality of life. He focuses on the well-being of communities, from small neighborhoods to entire countries and regions of the world. Woodman’s research spans global reproductive health, clinical research, and medical education. Simultaneously, he continues to explore social and emotional aspects of aging, along with behaviors and attitudes that affect human lifespan in different populations and environments.

Before coming to the USC Leonard Davis School of Gerontology, Woodman completed a Master of Public Health at California State University, Fullerton. While there, he worked on a National Institutes of Health-funded project in Chiang Mai, Thailand. The goal of the project was to understand the attitudes of sex workers toward HIV, AIDS and HIV treatment.

Over the past few years, Woodman and his colleagues have published papers on a range of topics, including:

• Communicable and non-communicable diseases
• Women’s reproductive health and associated factors, such as genetic variations and coagulation factors
• Association of overweight and obesity with populations’ perceptions and knowledge about nutrition and health
• Medical education

Studying public health and aging in Saudi Arabia

One of Woodman’s current lines of research is obesity and the leptin gene. Leptin is a protein that helps maintain body weight. Some people have a variant of the gene that increases their risk for obesity. He studies the relationship between the prevalent leptin genetic variant and measurements such as body mass index (BMI) among Saudis in the Eastern Province.

As an undergraduate, Woodman minored in Near Eastern cultures, which inspired him to broaden his horizons and led him to the Middle East. He pursued health research in this region as part of his PhD at the University of Salford, Manchester (U.K.) There, leading public health experts provided valuable mentorship.

Woodman investigated and continues to study the Gulf region’s food guidelines, known as the Arab Food Dome. He wants to understand people’s attitudes towards the recommendations and how diet is related to longevity in the region.

USC Leonard Davis School: An important step

Woodman’s introduction to the concepts of diet and longevity occurred at the USC Leonard Davis School. As a Master of Science in Gerontology (MSG) student, he spent a summer conducting independent research for credit in Corsica, an island off the south of France. He documented food choices and other lifestyle factors in healthy older people.

Woodman’s MSG also prepared him to succeed at the doctoral level. Embarking on a PhD in the U.K. was challenging since the British educational system requires independent research and critical appraisal of evidence.

“I would not have been successful if not for the practice of reflexivity, which I consider the hallmark of excellent research. In this, the role and preparation by the leading experts of USC cannot and should not be underestimated,” Woodman says. “USC gave me the confidence to act as an independent researcher as part of my PhD study.”

Learning from the best

Woodman chose the USC Leonard Davis School to learn from passionate people who are leaders in their field. For Woodman, the “best” included:

• Mentor Aaron Hagedorn , PhD, who provided expert guidance
• Emeritus Dean of the Andrus Gerontology Center Edward Schneider, who is a pioneer in the aging field and an expert in medicine and biology
• Senior Associate Dean Maria Henke , whose leadership has led many to consider gerontology as a global, scientifically important undertaking

Woodman maintains connections with USC faculty. He recently published a study in the Journal of Community Health with Keck School of Medicine Associate Professor, Mellissa Withers . The study looked at factors of overweight and obesity among petrochemical company employees in Saudi Arabia.

Lifelong learning and achievement

Despite his achievements, Woodman frequently pursues additional educational and research opportunities. Recently, he completed a yearlong clinical research program at Harvard Medical School. He also attended a bioethics program at Yale University.

“My friends ask me, ‘When are you going to stop?’ but I cannot imagine my life without learning,” Woodman says. His hard work paid off with a Fulbright Research Fellowship supporting his work at Arabian Gulf University School of Medicine in Bahrain.

In addition to his research, as part of contribution to the local community, Woodman initiated and led a series of seminars and workshops aimed at training emerging Bahraini academicians in research methodology and scientific writing. He also began to explore how the concept “Nothing About Us Without Us” is perceived in Bahrain, one of the most inclusive countries in the world for people with disabilities, he adds.

As a researcher exploring diverse cultures, Woodman frequently asks himself, “Do I see and listen? Or do I just look and hear?” It seems clear that Woodman sees and listens to the people he studies — and cares deeply about their health and future.

To learn more about the Master of Science in Gerontology program at the USC Leonard Davis School of Gerontology, call us at (213) 740-5156.

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College names Outstanding Senior Award winners

Four College of Engineering seniors received 2024 Outstanding Senior Awards for their exceptional achievements and contributions and undergraduate students during a ceremony on campus on April 17.

Winners were chosen in the categories of Citizenship and Service, Humanities, Leadership, and Scholarly Achievement. Outstanding nominees were put forward by the College of Engineering’s nine academic departments and affiliated departments in three other NC State colleges.

Seniors are nominated by their respective academic departments in the College of Engineering, and winners are chosen by a selection committee made up of faculty and staff members in the College. Departmental nominees were honored during a ceremony held at the James B. Hunt Jr. Library on NC State’s Centennial Campus where the four COE winners were also announced.

Senior Award for Leadership

Rosie Fisher

Rosie Fisher has shown what it means to be a leader in her years at NC State. As an Accelerated Bachelor’s/Master’s Program student, she has maintained a 4.0 GPA while also acting as the president of the Textile Technology and Engineering Society (TTES) for the past two years.

Additionally, Fisher received the Textile Engineering Service and Dedication Award, which is awarded each year to a member of The Textile Engineering Society who has shown interest, effort and dedication above and beyond other members of the organization and who prioritizes the advancement and well-being of their fellow textile students.

Outside of textile engineering, Rosie was an event coordinator for Greater Good Textile Group her sophomore year, and she helped revitalize the campus-wide clothing swap when students returned to campus in 2021. In her leadership roles, Rosie not only aimed for and achieved more student involvement and inclusion, but she also worked to set incoming classes up for success by encouraging them to participate in leadership roles and supporting them when they did so.

In the Raleigh community more broadly, Rosie volunteered as a youth leader for 6th- and 7th-graders in Raleigh and was a summer staffer for Appalachia Service Project, where she led high school students and adult group leaders in completing home repair construction projects. She also led nightly educational and reflective programming.

For her tireless work with organizations on campus and in the surrounding community, we are proud to award Rosie Fisher the 2024 Senior Award for Leadership.

Senior Award for Humanities

Zane Shockley

As a double major in industrial and systems engineering and German studies, Zane Shockley stands out among his peers for his academic achievements and intellectual curiosity. Professors noted the drive with which he pursues his goals, his intrinsic motivation to learn and perform well and his flawless work discipline as reasons why he is the ideal candidate for the Senior Award for Humanities.

Zane is always deeply engaged with his work for his classes and with his research. In the summer of 2023, Zane completed an internship in Germany working for Krones AG, a

company that designs, builds and installs machines for packaging bottles and food items. In this capacity, he was able to build skills for both his humanities and engineering majors.

Additionally, Zane is always early for class and eager to practice his disciplines, both in the ISE and German departments. He is currently the vice president of Delta Phi Alpha, the national German honor society, and has helped many times over the years to organize their events. In the years prior to becoming vice president, Zane served as their secretary and their treasurer.

Zane noted that his time at NC State has helped him prepare to be a successful professional and said his professors and mentors from both departments helped him grow by challenging him and broadening his worldview through the lenses of humanities and engineering.

For his dedication to both of his chosen disciplines and his academic excellence, we are proud to award Zane Shockley the 2024 Senior Award for Humanities.

Senior Award for Citizenship and Service

Abigail Wucherer

Not only is mechanical engineering major Abigail Wucherer a dedicated student, but she is also a lifelong advocate for health and wellness.

As co-president and co-founder of the Active Minds student chapter at NC State, Abigail has created spaces for student advocacy around mental health and participated in public speaking engagements at campus events, during which she talks about her personal story of losing her friend to suicide in 2023. In doing so, Abigail hopes to make students feel less alone. Abigail and her co-founder recently made a presentation to the Dean of Engineering and the College’s Executive Committee about the need for more mental health resources for students in the College, which was extremely well received and supported by the dean.

Abigail is also the co-founder of Arin’s Good Girl Dog Treats, a nonprofit organization that provides career development opportunities for individuals with intellectual and developmental disabilities. Her older sister, Arin, was born with microcephaly, a rare intellectual and developmental disability. Abigail co-founded Arin’s Good Girl Dog Treats in honor of her sister, and the organization now has 11 talented employees with various intellectual and developmental disabilities.

In addition to leading these organizations, Abigail also leads the Women in Mechanical and Aerospace Engineering (MAE) Club and has maintained a 4.0 GPA throughout her time at NC State. In her capacity as an officer of MAE Club, Abigail organized several events, including a virtual women in engineering panel.

For her dedication to service to her community alongside her academic pursuits, we are proud to award Abigail Wucherer the 2024 Senior Award for Citizenship and Service.

Senior Award for Scholarly Achievement

Robert Kobrin

As a student in the Joint Department of Biomedical Engineering, Robert Kobrin is already working on important research in his field. Since 2021, Robert has worked in the ImmunoEngineering lab at NC State, where he created and parameterized a novel chitosan-glycerol gel to provide increased retention of injected immunotherapeutics compared to lab standard practices. He considers his time at the lab a cornerstone of his education.

Additionally, Robert has developed three cutting-edge flexible nanobiosensors and helped bridge two labs, leading to four other students working abroad. His research efforts led to a Goldwater nomination, co-authorship of manuscripts in Biosensors & Bioelectronics and Cryobiology, first-authorship in IEEE Open Journal of Engineering in Medicine and Biology and a second developing manuscript.

Outside of the lab, Robert has led the Helping Hands Project in developing 20 pediatric prosthetics and will graduate with highest honors for a thesis on developing a low-cost prosthetic that contains adjustable grip patterns, improving outcomes for pediatric patients.

Robert has held multiple teaching assistant appointments, interned at The Center for Bionic Medicine at the Shirley Ryan AbilityLab in Chicago and fostered an interdisciplinary background through his history and Spanish minors in his time at NC State. In the summer of 2022, he completed a 10-week research internship at the Universitat Autònoma de Barcelona, gaining experience and understanding of similarities and differences between research cultures. For his outstanding achievements as an engineering student and researcher, we are proud to award Robert Kobrin the 2024 Senior Award for Scholarly Achievement.

• Department of Mechanical and Aerospace Engineering
• Department of Textile Engineering Chemistry and Science
• Edward P. Fitts Department of Industrial and Systems Engineering
• Joint Department of Biomedical Engineering
• Outstanding Senior Awards

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