ABSTRACT

Student engagement is a fundamental concept in educational theory and practice. Research on student engagement has flourished in the last several years due to advances in more engaging teaching tools, technologies, methods, and psychological theories of learning. Yet many areas remain unexplored or somewhat underexplored. In this article, we use 756 research records from the Scopus database and employ a bibliometrics technique called co-occurrence of keywords to explore and map the intellectual architecture of research on student engagement. Our analysis reveals six domains of research. We discuss these domains and suggest several directions for future research.

RATIONALE AND RESEARCH QUESTION

One of the central goals of any educational activity or method is to nurture and maintain student engagement. Engaged students learn better and achieve higher performance (Bond, 2020). As a result, scholarly interest in understanding student engagement and researching its antecedents and consequences has flourished and continues to rise (Bond, 2020; Fredricks, Blumenfeld, & Paris, 2004; Trowler, 2010; Young, 2003). However, despite recent attempts, the concept of student engagement remains conceptually and semantically under-studied. Specifically, recent studies point to the importance of strategies that foster student engagement using latest technologies in the wake of trends such as COVID-19 (Chiu, 2022; Heilporn, Lakhal, & Bélisle, 2021).

To contribute to this stream, we seek to answer a fundamental yet still unanswered research question, that is: What are the building blocks of the intellectual architecture of research on student engagement? To address this question, we first define student engagement and shed light on its dimensions. We then carry out a bibliometric analysis and discuss its findings. 

DEFINING STUDENT ENGAGEMENT

In its broadest sense, student engagement refers to “how involved or interested students appear to be in their learning and how connected they are to their classes, their institutions, and each other” (Axelson & Flick, 2011, p. 38). This definition connotes a narrow behavioural view (Young, 2003; Zhang, Hu, & McNamara, 2015; Zhong, Busser, Shapoval, & Murphy, 2021) and needs to encompass why engagement matters. It is because developing and maintaining student engagement is at the heart of contemporary behavioural theories of teaching and learning. In fact, student engagement is considered to be highly correlated with students’ learning, performance, and overall academic achievements (Bond, 2020). Therefore, a more complete definition that addresses this issue is needed. Such a definition was suggested by Trowler (2010):

“Student engagement is concerned with the interaction between the time, effort and other relevant resources invested by both students and their institutions intended to optimise the student experience and enhance the learning outcomes and development of students and the performance, and reputation of the institution” (p6).

The above definition has merit but fails to clearly incorporate the psychological essence of engagement. So, for the purpose of this study, we adopt a more complete definition offered by Bond (2020). According to Bond, 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” (p3).

DIMENSIONS OF STUDENT ENGAGEMENT

Student engagement goes beyond simple involvement or class attendance and participation. It goes deeper by requiring and engaging students’ feelings, sensemaking, and active behaviours. Accordingly, engagement has been conceptualised as a multi-dimensional construct. For instance, Fredricks et al. (2004) proposed three dimensions for student engagement:

1. Behavioural engagement: Students who are behaviourally engaged would typically comply with behavioural norms, such as attendance and involvement, and would demonstrate the absence of disruptive or negative behaviour.

2. Emotional engagement: Students who engage emotionally would experience affective reactions such as interest, enjoyment, or a sense of belonging.

3. Cognitive engagement: Cognitively engaged students would be invested in their learning, would seek to go beyond the requirements, and would relish challenge.

This view has been widely adopted by others such as Trowler (2010), Axelson and Flick (2011), and more recently by Zhao and Ko (2020) and (Zhong et al., 2021). Furthermore, some authors have argued that these dimensions of engagement are not binary and follow a spectrum (low vs high) and have valence (positive vs negative) (Bond, 2020). In the next section we outline our methodology for mapping the intellectual architecture of research on student engagement. 

METHODOLOGY

Bibliometrics is the study of scientific fields and domains in order to explore their growth, evolution, dynamics, and formation (Donthu, Kumar, Mukherjee, Pandey, & Lim, 2021). Bibliometrics research encompasses a wide range of techniques and tools that allow researchers to explore scientific domains from different angles and for different purposes. In this article, we explore the intellectual architecture of research on student engagement. To do so, we adopt a technique called co-word analysis (Bhattacharya & Basu, 1998; Kostoff, 1993). Co-word analysis uses co-occurrence of keywords in scientific publications to identify clusters of related words that represent researchers’ common interests (He, 1999). It has become one of the most popular and widely used bibliometrics techniques (Nájera-Sánchez, 2019; Rojas-Lamorena, Del Barrio-García, and Alcántara-Pilar, 2022). To perform this technique, we:

1. Extracted all research records on the topic of student engagement published in English from the Scopus database. Scopus is widely considered to be the largest and most comprehensive academic database (Zhu & Liu, 2020).

2. Reviewed keywords, cleared ambiguous keywords, and removed duplicated keywords.

3. Exported the data to the VOSviewer software (Shah, Lei, Ali, Doronin, & Hussain, 2019) and created networks of co-occurred keywords with minimum of 10 occurrence. This setting has been suggested as a robust threshold to detect the most important networks of clusters in a domain (Rojas-Lamorena et al., 2022).

4. Reviewed, visualised, and labelled clusters that represent the intellectual architecture of research on student engagement.

In addition to these four steps, VOSviewer allows for a time overlay analysis of keyword co-occurrence. This analysis reveals how the significance of keywords and their clusters change overtime. As a further step we also looked at this aspect and mapped the more recent phase in the network of clusters. When put together, these steps portray an interesting picture of research on student engagement and illustrate how it has changed recently in response to global phenomena such as COVID-19. We discuss our findings in the following section.

FINDINGS

Figure 1 illustrates the network of co-occurred keyword clusters. It shows that six clusters jointly shape the intellectual architecture of research on student engagement. In what follows we elaborate on these clusters.

Figure 1: Clusters shaping the intellectual architecture of research on student engagement

Cluster 1: Student motivation and self-efficacy

It is a well-known and well-documented fact that student motivation, and specifically intrinsic motivation, drives student engagement and enhances self-reported learning. Students who demonstrate high degrees of self-efficacy are also highly motivated and generally outperform other students. Intrinsic motivation and self-efficacy played a significant role in maintaining student engagement in learning both during after the COVID-19 pandemic. Several recent studies are noteworthy in this cluster. For instance, Hernández González, and Blackford (2022) empirically supported this relationship in a sample of undergraduate business students. Similarly, Dabbour (2021) found that motivated engineering students exhibited higher satisfaction and grades in mid-term and end-of-term exams. Pasion, Dias-Oliveira, Camacho, Morais, and Campos Franco (2020) found no difference between learning levels of highly motivated students in Italy who participated in fully offline classes before COVID-19 and moved to online classes during the pandemic. Finally, Mozammel, Ahmed, Slade, and Zaman (2018) demonstrated that academic self-efficacy and academic resilience drove student engagement with their studies during the COVID-19 pandemic.

Cluster 2: Teaching methods and pedagogy

The second cluster pertains to the role of pedagogical issues at the centre of student engagement. For instance, Osgerby and Rush (2015) investigated the pedagogical potential of social media and in particular Twitter to enhance student engagement. They concluded that while Twitter has some communication and pedagogic potential, educators should plan its use carefully because of its limitations and operating dynamics. More recently, Singh, Doval, Kumar, and Khan (2022) discussed the gap between what is taught in class and what is required in the exam as a key detractor of managing student engagement and proposed experiential learning as a remedy. Analogously, DeLuca and Fornatora (2020) illustrated how experiential learning facilitated course engagement among sport management students. Fogarty (2021) offered an anecdotal account of the positive role of flipping classrooms on student engagement during COVID-19. Similarly, Altmann, Clauss, and Schoop (2021) found that professional pedagogical support, technical platform, authentic task design, and supporting staff on utilising learning analytics significantly improved student engagement during the pandemic. Zhao and Ko (2020) showed that vocational teaching practices that focus more on adjusting students' practical learning have a significantly better impact on vocational students’ engagement than traditional methods. Igwe, Rahman, Ohalehi, Amaugo, and Anigbo (2020) noted that, responsive educational approaches that create a sense of belonging in students tend to markedly improve student engagement.

Cluster 3: Innovative learning systems

The third cluster focuses on the applications of innovative learning systems in nurturing and maintaining student engagement. These systems use latest technologies and creative approaches to generate an atmosphere where engagement blossoms. Bressane, dos Santos Bardini, and Spalding (2021) demonstrated the use of the Fast-300 based learning system, which uses a set of cooperative methods to boost student engagement. They concluded that, due to its role in improving student engagement, Fast-300 is a promising alternative for improving both hard and soft-skills such as comprehension, communication, logical reasoning, and leadership. Educational gamification - defined as the use of gaming elements in non-game educational contexts - has become a key building block of this cluster. Gubic (2015) argued that gamifying education generates new types of data that can be used in the process of assessing a student's performance as well as effectively improving engagement. The key to implementing gamification is to understand the types of data that can be generated. Further to this, Craven (2015) asserted that gamification appears to be preoccupied with badging rather than seeking to overlay the learning processes of reflection, analysis, and insight over the gaming experience. They proposed an innovative simulation tool called PierSim, which addresses these deficiencies.

Filippou, Cheong, and Cheong (2018) identified ‘usefulness', 'preference for use', 'knowledge improvement', 'engagement', 'immersion' and 'enjoyment' as factors that influence students' preference for use of gamification when playing a gamified quiz, named Quick Quiz. This game features several gamification elements such as points, progress bars, leader boards, timers, and charts. More recently, Bucchiarone, Cicchetti, Bassanelli, and Marconi (2021) enumerated various gamifying techniques such as educational challenges, puzzles, rewards, and score boards and illustrated how they contribute to the engagement level of programming and modelling students. Lastly, Kaplan, Zhang, and Cole (2022) showed that an innovative gamified learning platform called Catch-the-Flag improved student engagement in a cybersecurity course. 

Cluster 4: Social and digital media

Social and digital media have contributed significantly to the practice of student engagement and numerous studies have documented this phenomenon. For instance, Hwang and Bowers (2012) argued that today’s students belong to the ‘net generation’ where social media such as Facebook can be innovatively integrated into learning management systems as a classroom support platform to keep students engaged. They contended that a shift away from traditional teaching-focused learning toward student-focused learning necessitate the use of more innovative learning systems that utilise the growing power and popularity of social media.

Holmes and Rasmussen (2018) presented a social media-based assignment that could increase student interest in and understanding of managerial accounting, particularly for English-as-a-second language (ESL) students. For the assignment, students used Pinterest to share Internet-based resources with their classmates. The students were also required to comment on Pinterest “pins” created by their classmates. The assignment encouraged students to be engaged and active learners in the course, build interest in the managerial accounting topics covered, and develop a deeper understanding of managerial accounting by utilising outside resources and real-world examples.

Similarly, Fujita, Harrigan, and Soutar (2017) found that students are likely to use their university’s social media as part of their acculturation and social identity construction strategy. Their engagement tends to be cognitive and emotional, being influenced by the instrumental value of the social media page, engagement with campus rituals and artefacts, social identity and bonds with other students, and perceptions of the page administrator. These students’ engagement influenced their identification with the university and its student community, manifested in a sense of belonging and pride and improved their course participation and academic performance.

More recently, De Aires Angelino, Loureiro, and Bilro (2021) revealed that students' engagement increased when they participated in tasks that require extensive information search from various media sources such as online forums and social media. Lastly, Alenezi and Brinthaupt (2022) studied Kiwanian students and found that social media facilitated their interaction with peers and faculty, engagement, and collaborative learning.

Cluster 5: Online teaching and e-learning

Online learning (or e-learning) refers to the use of information technology communications such as the Internet, computers, and mobile phones to enhance teaching and learning activities. Madar and Bin Ibrahim (2011) showed that e-learning systems improve students’ overall academic performance by enriching content and providing more opportunities to engage with learning materials. Ali and Al-Kaabi (2012) studied and empirically measured the role of Interactive Whiteboards (IWB) as e-learning tools that directly improve the learning environment by engaging students in the instruction. Similarly, Acosta-Tello (2013) outlined several simple yet effective e-learning tools including e-mails, online announcements, avatars, and an online office that, despite being simple, directly improved student engagement with the course.

Sahni (2018) discussed the widespread adoption of blended learning that combines face-to-face and online learning experiences and demonstrated empirically that students who are exposed to blended learning have higher engagement with class activities and outperform their peers who use only face to face learning. More interestingly, Dahleez, El-Saleh, Al Alawi, and Abdelmuniem Abdelfattah (2021) found that e-learning system usability influenced significantly and positively agentic, behavioural, and cognitive engagement. However, the link between the e-learning system usability and emotional engagement was not significant. Moreover, teacher behaviour mediated the relationship between e-learning system usability and the four types of engagement.

Lastly, Merkle, Ferrell, Ferrell, and Hair (2022) studied the role of e-books as an e-learning tool in students’ engagement and performance. Their findings suggested a diverse impact of e-books on student engagement. Some aspects of engagement were positively affected while other aspects of student engagement exhibited a neutral or negative leaning impact. The findings also reflected significant variation in e-book effectiveness depending on the course. In addition, they found that e-books moderated the relationship between textbook effectiveness and academic performance engagement. Highly effective e-books resulted in higher levels of academic performance engagement.

Cluster 6: Disciplinary factors

The last cluster encompasses various studies across engineering, computing, and other disciplines concerned with the pivotal role of student engagement in students’ success across disciplines. Clark and Andrews (2012) addressed the fundamental question of "How can University level Engineering Education be developed in such a way so as to enhance the quality of the student learning experience?" They proposed an approach to engineering education developed by a senior engineering educator working alongside a pedagogical researcher in an attempt to engage colleagues in contemporary debates about improving student’s engagement and overall performance. Ivala, Chigona, Gachago, and Condy (2012) pointed to the role of low engagement in students’ course completion in Africa and discussed the potential role of digital study-telling in improving universities’ completion rate across faculties and disciplines. Dabbour (2016) studied civil engineering students’ access to online student response systems (OSRS), which allow them to use wireless devices to respond to questions and quizzes posted by the instructors as a way of lifting engagement and overall student success. More recently, Ott, McCane, and Meek (2021) argued that active interviews with students in computing and programming courses help them strengthen their class engagement and adjustment to course requirements and assessments. Analogously, Anderson, Vasiliou, and Crick (2022) reported the results of a study related to the role of attention span on course engagement among computing and programming students. Using diaries and focus groups, they found that active listening to students and recommending professional ways to improve attention increase students’ course engagement. 

TIME OVERLAY: COVID-19

Having discussed core themes and some representative studies in each cluster, we took a further step and looked at the time overlay of co-occurred keywords. As Figure 2 suggests, COVID-19 is a watershed phenomenon in the evolution of research on student engagement. A survey of past research shows that numerous studies (Fogarty, 2021; Gangwani & Alfryan, 2020; Huang, Finsterwalder, Chen, & Crawford, 2022; Jusoh et al., 2021; Knox, 2022; Perera, Rainsbury, & Bandara, 2021; Sum, Chan, & Wong, 2021; Szopiński & Bachnik, 2022; Zaghloul & Bednar, 2022) explored methods and validating techniques aimed at improving and sustaining student engagement during the pandemic (when many classes were forced to become fully online) and after the pandemic (when the post-pandemic recovery required many classes to stay online and many more to became hybrid, offering blended learning). Innovative learning systems, social media, and e-learning tools and platforms are main keywords connected to COVID-19. This observation suggests that using social media, innovative platforms, and e-learning tools and technologies as discussed above have had and will continue to have important implications for managing students’ engagement in the current post-pandemic phase. The methods and studies surveyed in this article offer valuable insights into the mechanisms that can be used to create an atmosphere where students engage better with the content and learning systems. In the next session we discuss some of these implications 

Figure 2: Time overlay of keyword co-occurrence network in the field of research on student engagement

IMPLICATIONS AND CONCLUSION

In this study we outlined six clusters that jointly form the architecture of research on student engagement. We also showed that the COVID-19 pandemic represents the current focus of research on student engagement. Together, we can argue that student engagement has been and continues to be a complex, elusive, and multifaceted factor in teaching and learning research. Given its central role, one of the key functions of advanced teaching and learning technologies such as various gamifying tools, modern e-learning systems, and pedagogical frameworks on blended and flipped learning has been to initiate and sustain student engagement. Despite these advances, student engagement remains a highly debated concern.

Our study has three important implications. First, understanding factors that explain and improve student engagement in the post-pandemic recovery period requires a synthesis of insights from multiple clusters. These include past research on the role of social and digital media, e-learning systems, and new pedagogical views. Only by combining these sources cam we develop a new and more complete understanding of student engagement. Second, despite the significance, usefulness, and preponderance of past research on student engagement, there are numerous unexplored areas that require systematic research. These include the role of student demographics, psychological factors, and instructors’ socio-cultural factors on the level and dynamics of student engagement across disciplines.  Third, student engagement is not limited to a specific discipline, degree, course, country, or subject. It is a universal phenomenon that requires joint systematic and focused efforts. We hope that our study will provide educational researchers with a holistic view of this phenomenon and motivate them to conduct more progressive and ground-breaking research on the causes of low student engagement and solutions for increasing it.

REFERENCES

Acosta-Tello, E. (2013). Enhancing student engagement in the online classroom. Paper presented at the 3rd IEEE Global Humanitarian Technology Conference, GHTC 2013, San Jose, CA.

Alenezi, W., & Brinthaupt, T. M. (2022). The Use of Social Media as a Tool for Learning: Perspectives of Students in the Faculty of Education at Kuwait University. Contemporary Educational Technology, 14(1). doi:10.30935/cedtech/11476

Ali, H., & Al-Kaabi, R. (2012). Using an interactive whiteboard (IWB) to increase students' engagement in english and science classes : The Case of Creativity Private School in Kingdom of Bahrain. Paper presented at the 19th IBIMA Conference on Innovation Vision 2020: Sustainable Growth, Entrepreneurship and Economic Development, Barcelona.

Altmann, M., Clauss, A., & Schoop, E. (2021). Virtual collaborative learning during COVID19 - Lessons learned in practice. Paper presented at the 29th Interdisciplinary Information Management Talks - Pandemics: Impacts, Strategies and Responses, IDIMT 2021.

Anderson, E., Vasiliou, C., & Crick, T. (2022). Co-Designing Classroom Practice to Improve Student Attention and Engagement in Computer Science Degree Programmes. Paper presented at the 27th ACM Conference on Innovation and Technology in Computer Science Education, ITiCSE 2022.

Axelson, R. D., & Flick, A. (2011). Defining Student Engagement. Change, January/February, 38-43.

Bhattacharya, S., & Basu, P. (1998). Mapping a research area at the micro level using co-word analysis. Scientometrics, 43(3), 359-372.

Bond, M. (2020). Facilitating student engagement through the flipped learning approach in K-12: A systematic review. Computers & Education, 151, 103819.

Bressane, A., dos Santos Bardini, V. S., & Spalding, M. (2021). Active learning effects on students’ performance: A methodological proposal combining cooperative approaches towards improving hard and soft skills. International Journal of Innovation and Learning, 29(2), 154-165. doi:10.1504/IJIL.2021.112993.

Bucchiarone, A., Cicchetti, A., Bassanelli, S., & Marconi, A. (2021). How to merge gamification efforts for programming and modelling: A tool implementation perspective. Paper presented at the 24th International Conference on Model-Driven Engineering Languages and Systems, MODELS-C 2021.

Chiu, T. K. (2022). Applying the self-determination theory (SDT) to explain student engagement in online learning during the COVID-19 pandemic. Journal of Research on Technology in Education, 54(sup1), S14-S30.

Clark, R., & Andrews, J. (2012). Engineering education for the 21st century: Scholarship, synergy and student success. Paper presented at the International Conference on Innovation, Practice and Research in Engineering Education, EE 2012, Coventry.

Craven, D. (2015). Gamification in virtual worlds for learning: A case study of piersim for business education. In Gamification in Education and Business (pp. 385-401): Springer International Publishing.

Dabbour, E. (2016). Quantifying the Effects of Using Online Student Response Systems in an Engineering Ethics Course. Journal of Professional Issues in Engineering Education and Practice, 142(2). doi:10.1061/(ASCE)EI.1943-5541.0000260.

Dabbour, E. (2021). Motivating Engineering Students by Providing Two Midterm Exams and Dropping the Lower Mark. Journal of Civil Engineering Education, 147(1). doi:10.1061/(ASCE)EI.2643-9115.0000024.

Dahleez, K. A., El-Saleh, A. A., Al Alawi, A. M., & Abdelmuniem Abdelfattah, F. (2021). Higher education student engagement in times of pandemic: the role of e-learning system usability and teacher behavior. International Journal of Educational Management, 35(6), 1312-1329. doi:10.1108/IJEM-04-2021-0120.

Taylor & Francis; Wang, Y.S., Wu, M.C., Wang, H.Y. Investigating the determinants and age and gender differences in the acceptance of mobile learning (2009) British Journal of Educational Technology, 40 (1), pp. 92-118.

Young, M.R., Klemz, B.R., Murphy, J.W. Enhancing learning outcomes: the effects of instructional technology, learning styles, instructional methods, and student behavior (2003) Journal of Marketing Education, 25 (2), pp. 130-142.

Zepkea, N., Leacha, L., Butler, P. Student engagement: students' and teachers' perceptions (2014) Higher Education Research and Development, 33 (2), pp. 386-398,

De Aires Angelino, F. J., Loureiro, S. M. C., & Bilro, R. G. (2021). Analysing students' engagement in higher education through transmedia and learning management systems: A text mining approach. International Journal of Innovation and Learning, 30(4), 484-502. doi:10.1504/IJIL.2021.118875

DeLuca, J. R., & Fornatora, E. (2020). Experiential learning from a classroom desk: Exploring student perceptions of applied coursework. Sport Management Education Journal, 14(2), 142-150. doi:10.1123/SMEJ.2019-0015

Donthu, N., Kumar, S., Mukherjee, D., Pandey, N., & Lim, W. M. (2021). How to conduct a bibliometric analysis: An overview and guidelines. Journal of Business Research, 133, 285-296. doi:10.1016/j.jbusres.2021.04.070

Filippou, J., Cheong, C., & Cheong, F. (2018). A model to investigate preference for use of gamification in a learning activity. Australasian Journal of Information Systems, 22. doi:10.3127/ajis.v22i0.1397

Fogarty, T. J. (2021) Accounting education in the time of the coronavirus or how flipping the classroom heightened my appreciation for student behavior. In: Vol. 25. Advances in Accounting Education: Teaching and Curriculum Innovations (pp. 101-107): Emerald Group Holdings Ltd.

Fredricks, J. A., Blumenfeld, P. C., & Paris, A. H. (2004). School Engagement: Potential of the Concept, State of the Evidence. Review of Educational Research, 74(1), 59–109.

Fujita, M., Harrigan, P., & Soutar, G. N. (2017). International students’ engagement in their university’s social media: An exploratory study. International Journal of Educational Management, 31(7), 1119-1134. doi:10.1108/IJEM-12-2016-0260.

Gangwani, S., & Alfryan, L. H. (2020). Impact of online teaching strategies on student engagement in higher education during global lockdown in Riyadh. Academy of Strategic Management Journal, 19(6), 1-11. Retrieved from https://www.scopus.com/inward/record.uri?eid=2-s2.0-85098110728&partnerID=40&md5=4c1b2e03fa94b2a4982eeedf5825bcf9.

Gubic, A. (2015). Generating data by gamifying education. In Strategic Data-Based Wisdom in the Big Data Era (pp. 245-256): IGI Global.

He, Q. (1999). Knowledge discovery through co-word analysis, LIBRARY TRENDS, Vol. 48, No. 1 pp. 133-159.

Heilporn, G., Lakhal, S., & Bélisle, M. (2021). An examination of teachers’ strategies to foster student engagement in blended learning in higher education. International Journal of Educational Technology in Higher Education, 18(1), 1-25.

Hernández González, C. A., & Blackford, B. J. (2022). Engagement as antecedent of academic achievement and the moderating impact of work-family-school inter-role conflict for online graduate students. International Journal of Management Education, 20(3). doi:10.1016/j.ijme.2022.100676.

Holmes, A. F., & Rasmussen, S. J. (2018). Using Pinterest to stimulate student engagement, interest, and learning in managerial accounting courses. Journal of Accounting Education, 43, 43-56. doi:10.1016/j.jaccedu.2018.03.001

Huang, Y., Finsterwalder, J., Chen, N., & Crawford, F. R. L. (2022). Online student engagement and place attachment to campus in the new service marketplace: an exploratory study. Journal of Services Marketing, 36(4), 597-611. doi:10.1108/JSM-04-2021-0148

Hwang, E. J., & Bowers, F. (2012). The influence of U.S. and South Korean University students' involvement in social networking on teaching and learning. Paper presented at the 12th European Conference on e-Government, ECEG 2012, Barcelona.

Igwe, P. A., Rahman, M., Ohalehi, P., Amaugo, A., & Anigbo, J. A. (2020). Responsible education: what engages international postgraduate students – evidence from UK. Journal of Global Responsibility, 11(4), 363-376. doi:10.1108/JGR-03-2020-0036.

Ivala, E., Chigona, A., Gachago, D., & Condy, J. (2012). Digital storytelling and student engagement: A case of pre-service student teachers and their lecturers' at a university of technology. Paper presented at the 12th European Conference on e-Government, ECEG 2012, Barcelona.

Jusoh, J. A., Nazari Mohd Rose, A., Aledinat, L. S., Altrad, A., Ahmad Saany, S. I., Aseh, K., . . . Baker El-Ebiary, Y. A. (2021). Track student attendance at a time of the COVID-19 pandemic using location-finding technology. Paper presented at the 2nd International Conference on Smart Computing and Electronic Enterprise, ICSCEE 2021.

Kaplan, Z., Zhang, N., & Cole, S. V. (2022). A Capture The Flag (CTF) Platform and Exercises for an Intro to Computer Security Class. Paper presented at the 27th ACM Conference on Innovation and Technology in Computer Science Education, ITiCSE 2022.

Knox, S. (2022). Fostering student engagement in virtual entrepreneurship education environments. International Journal of Management Education, 20(3). doi:10.1016/j.ijme.2022.100705.

Kostoff, R. N. (1993). Co-word analysis. In Evaluating R&D impacts: Methods and practice (pp. 63-78): Springer.

Madar, M. J., & Bin Ibrahim, O. (2011). E-learning towards student academic performance. Paper presented at the 2011 International Conference on Research and Innovation in Information Systems, ICRIIS'11, Kuala Lumpur.

Merkle, A. C., Ferrell, L. K., Ferrell, O. C., & Hair, J. F., Jr. (2022). Evaluating E-Book Effectiveness and the Impact on Student Engagement. Journal of Marketing Education, 44(1), 54-71. doi:10.1177/02734753211035162.

Mozammel, S., Ahmed, U., Slade, H., & Zaman, F. (2018). Digging deep in students' engagement in Bahrain: Contributions from academic self-efficacy and academic resilience. International Journal of Management and Business Research, 8(1), 136-147. Retrieved from https://www.scopus.com/inward/record.uri?eid=2-s2.0-85058471028&partnerID=40&md5=812c690b741cc0f04f1d7b49a36143df.

Nájera-Sánchez, J. J. (2019). A systematic review of sustainable banking through a co-word analysis. Sustainability, 12(1), 278.

Osgerby, J., & Rush, D. (2015). An exploratory case study examining undergraduate accounting students' perceptions of using Twitter as a learning support tool. International Journal of Management Education, 13(3), 337-348. doi:10.1016/j.ijme.2015.10.002.

Ott, C., McCane, B., & Meek, N. (2021). Mastery Learning in CS1 - An Invitation to Procrastinate?: Reflecting on Six Years of Mastery Learning. Paper presented at the 26th ACM Conference on Innovation and Technology in Computer Science Education, ITiCSE 2021.

Pasion, R., Dias-Oliveira, E., Camacho, A., Morais, C., & Campos Franco, R. (2020). Impact of COVID-19 on undergraduate business students: a longitudinal study on academic motivation, engagement and attachment to university. Accounting Research Journal, 34(2), 246-257. doi:10.1108/ARJ-09-2020-0286

Perera, A., Rainsbury, L., & Bandara, S. (2021). Face-to-face delivery this week; online the next: a reflection. Accounting Research Journal, 34(3), 270-278. doi:10.1108/ARJ-09-2020-0294.

Rojas-Lamorena, Á. J., Del Barrio-García, S., & Alcántara-Pilar, J. M. (2022). A review of three decades of academic research on brand equity: A bibliometric approach using co-word analysis and bibliographic coupling. Journal of Business Research, 139, 1067-1083.

Sahni, J. (2018). Blended learning in higher education: Evidence from private women university. Paper presented at the 32nd International Business Information Management Association Conference, IBIMA 2018.

Shah, S. H. H., Lei, S., Ali, M., Doronin, D., & Hussain, S. T. (2019). Presumption: bibliometric analysis using HistCite and VOSviewer. Kybernetes.

Singh, E. P., Doval, J., Kumar, S., & Khan, M. M. S. (2022). Investigating the impact of full-term experiential learning project on management graduates: an emerging economy perspective. Review of International Business and Strategy, 32(4), 677-694. doi:10.1108/RIBS-03-2021-0049

Sum, C., Chan, I., & Wong, H. (2021). Ready to learn in an uncertain future: ways to support student engagement. Accounting Research Journal, 34(2), 169-183. doi:10.1108/ARJ-08-2020-0220

Szopiński, T., & Bachnik, K. (2022). Student evaluation of online learning during the COVID-19 pandemic. Technological Forecasting and Social Change, 174. doi:10.1016/j.techfore.2021.121203

Trowler, V. (2010). Student engagement literature review. The Higher Education Academy, 1(1), 1-75.

Young, J. R. (2003). Student 'Engagement' in Learning Varies Significantly by Major, Survey Finds. Chronicle of Higher Education, 50(12). Retrieved from https://www.scopus.com/inward/record.uri?eid=2-s2.0-17444449729&partnerID=40&md5=2e904311fb46e64c0531b6dee65fcb61.

Zaghloul, F., & Bednar, P. (2022). Blended and Online Learning Environments: Motivations, Contradictions, and Influencing Factors. Paper presented at the 18th Annual Conference of the Italian Chapter of AIS, ItAIS 2021.

Zhang, Z., Hu, W., & McNamara, O. (2015). Undergraduate student engagement at a Chinese university: a case study. Educational Assessment, Evaluation and Accountability, 27(2), 105-127. doi:10.1007/s11092-015-9213-x.

Zhao, Y., & Ko, J. (2020). How do teaching quality and pedagogical practice enhance vocational student engagement? A mixed-method classroom observation approach. International Journal of Educational Management, 34(6), 987-1000. doi:10.1108/IJEM-11-2019-0393.

Zhong, Y., Busser, J., Shapoval, V., & Murphy, K. (2021). Hospitality and Tourism Student Engagement and Hope During the COVID-19 Pandemic. Journal of Hospitality and Tourism Education, 33(3), 194-206. doi:10.1080/10963758.2021.1907197.

Zhu, J., & Liu, W. (2020). A tale of two databases: The use of Web of Science and Scopus in academic papers. Scientometrics, 123(1), 321-335.

BIOGRAPHIES

Dr Arash Najmaei holds a PhD in strategic management and entrepreneurship from Macquarie University. He is currently working as an associate director of analytics and teaching at various universities. His teaching interests include business research methods, strategic management, entrepreneurship, organizational change, and media management. Dr Najmaei’s research has been published in several journals and research books and presented at international conferences. He has also received three best-paper awards for his research in entrepreneurship and research methods.

Dr Zahra Sadeghinejad graduated with a PhD in management from Macquarie University. She is an active researcher and an award-winning lecturer. Her areas of teaching expertise include marketing, media management, entrepreneurship, and quantitative methods. Dr Sadeghinejad’s research has been published as book chapters and journal articles and has been presented at prestigious international conferences for which she has received multiple best-paper awards. Dr Sadeghinejad is currently a lecturer at the Universal Business School Sydney (UBSS), Central Queensland University (CQU), and the International College of Management Sydney (ICMS).