1 Introduction

The use of digital educational technology is not a new phenomenon in higher education and gained traction in the early ‘70s in the form of telecourses and the ‘80s in the form of computer-assisted learning and online learning (Garrison, 1985). In recent years, technology has received significant attention as a means to support distance education during the COVID-19 pandemic (Abu Talib et al., 2021) and as a disruptor of traditional teaching, learning, and assessment forms with the advent of generative artificial intelligence (GenAI) tools such as ChatGPT, Google Gemini, and Dall-E (Farrelly & Baker, 2023; Godsk & Elving, 2024). Studies show that educational technology has the potential for improving learning outcomes, motivation, engagement, and pass rates (Garrison & Kanuka, 2004; Price & Kirkwood, 2011; Schindler et al., 2017), as well as the business potential for reducing costs, increasing intakes, and increasing student retention (Daniel et al., 2009). In higher education in Europe and English-speaking countries, student engagement is often linked to the students’ experience, satisfaction, and learning outcomes, which is why there is a widespread desire to benefit from the technology’s potential to engage students (Payne, 2019; Schindler et al., 2017). Despite the evidence and interest, universities are struggling to make effective and systematic use of technology to support student engagement (Henrie et al., 2015b). This may be due to limited systematic evidence on how to engage students with specific educational technologies in terms of practical, concrete recommendations or guidelines, which can be directly applied by educators in their lesson planning or connection with Learning Design processes (Henrie et al., 2015b; Schindler et al., 2017).

1.1 The concept of student engagement and educational technology

The concept of “student engagement” has significantly evolved and expanded within educational research and higher education. Unlike traditional views that mainly focus on observable behaviours and indicators of involvement in educational activities, such as attendance and participation, recent studies adopt broader conceptualisations, analysing how students behave, feel, and think in the context of teaching and learning (Bond et al., 2020; Fredricks et al., 2004; Henrie et al., 2015a). This includes aspects like the general student experience and the resultant institutional reputation (Trowler, 2010; Wimpenny & Savin-Baden, 2013), viewing student engagement as an interconnected and psychosocial process influenced by personal and contextual factors (Kahu, 2013), “force-fields” (i.e., driving/resisting forces) for and against intrinsic and extrinsic motivation (Payne, 2019), or as described through three dimensions of engagement: behavioural, affective/emotional, and cognitive (Fredricks et al., 2004; Newmann et al., 1992). These dimensions are sometimes supplemented by additional dimensions such as “the will to succeed” (Kahu, 2013), social-behavioural engagement in the context of group work (Linnenbrink-Garcia et al., 2011), and student agency (Reeve & Tseng, 2011), which other researchers consider unnecessary, as they believe the three existing dimensions already adequately capture these aspects of student engagement (Kahu, 2013). These varied conceptualisations also reflect a broader debate between narrow, “mainstream”, and broad, holistic views of student engagement. The narrow view often restricts engagement to specific, measurable behaviours within classroom settings related to an effective learning process (Henrie et al., 2015b; Zepke, 2015), whereas the more broad and holistic view considers engagement as encompassing a wide range of student activities, interactions, and emotions both within and beyond academic environments that contribute to a richer learning experience (Bond et al., 2020; Fredricks et al., 2004; Henrie et al., 2015b; Zepke, 2015).

In addition, educational technology can involve students in teaching activities that were previously inconceivable (e.g., in virtual reality, simulations, online self-test quizzes, and GenAI-based formative feedback) (Kirkwood & Price, 2014; Puentedura, 2010; Godsk & Elving, 2024) and engagement can be expressed in ways and with indicators that could not previously be observed without technology (Bond & Bedenlier, 2019; Fredricks et al., 2004). This underscores the importance of not limiting focus to readily observable indicators of student engagement or confining the understanding to just one indicator, as both approaches risk overly simplifying the potential for student engagement. Such narrow focus may also overlook other forms of engagement that are indirectly related or cannot be observed without technology.

In other words, both the general desire to improve students’ learning experiences by engaging them with educational technology and the potential of the technology to engage in numerous ways that are not necessarily observable but interconnected (Payne, 2019) advocate for a need to adopt a broad conceptualisation of student engagement. One of the broad and widely used conceptualisations is based on Fredricks et al. (2004) and Newmann et al.’s (1992) three perspectives on student engagement and defined by Bond et al. (2020) in the context of higher education as: “The energy and effort that students employ within their learning community, observable via any number of behavioural, cognitive or affective indicators across a continuum.” (Bond et al., 2020, p. 2). This conceptualisation extends the narrow behavioural perspective by adding affective and cognitive dimensions, including how students feel and think about their learning experiences, which may significantly affect their engagement (Fredricks et al., 2004). “Behavioural engagement” is typically indicated by participation, interaction, involvement, achievement, confidence, and study habits; “affective engagement” or “emotional engagement” is often indicated by positive interaction, enjoyment, attitude, motivation, and enthusiasm; and “cognitive engagement” is typically indicated by peer learning, deep learning, self-regulated learning, positive self-perception, and critical thinking (Bond et al., 2020; Fredricks et al., 2004). This breadth of Fredricks et al. (2004)’s conceptualisation of student engagement, along with Bond et al. (2020)’s extensive and thorough list of indicators based on a large-scale review related to the three dimensions of engagement, therefore provides a coherent and practical framework for mapping studies of educational technologies and their use to actual engagement types, including the broader, holistic views of student engagement. Although no direct relationship between introducing specific educational technologies and student engagement in higher education has been established (Schindler et al., 2017; see also Pickering & Swinnerton, 2019), studies show that technology in education does influence student engagement and that more research is needed to understand the potential of specific educational technologies and how to benefit from them (Bond & Bedenlier, 2019; Clark, 1994; Lillejord et al., 2018) and thus ultimately meet the widespread desire to promote student engagement with educational technology. This leads to the following research question:

  • How to engage students with educational technology in higher education?

2 Method

A systematic literature review guided by the PRISMA process and utilising the inclusion and exclusion criteria in Table 1 was conducted to answer this question. The PRISMA process involved four steps: (1) searching for, screening and identifying relevant studies based on abstracts; (2) screening of and excluding studies that were not relevant based on full-text; (3) assessment of eligibility based on full-text; and (4) selection (‘inclusion’), coding, and analysis of the relevant studies in the final synthesis (see 6. for details). The analysis was based on a deductive and inductive coding of the studies according to educational technology, subject area, educational level, modality, type of student engagement, research method, and aim (Khan et al., 2003; Littell et al., 2008; Moher et al., 2009); and supplemented with follow-up searches (“Round 2”) on the identified types of educational technologies in step 4 (see details in 6. and Godsk et al, 2021). Fredricks et al.’s (2004) conceptualisation of student engagement as comprising three perspectives — behavioural, affective (emotional), and cognitive engagement — as well as Bond et al. (2020)’s identification of 55 specific indicators related to these dimensions, served as the basis for the coding of the engagement type (see Bond et al., 2020, Additional file 2). In Round 1, the searches were limited to empirical studies from OECD countries from 2013 onwards for maximum comparability of the educational contexts regarding teaching tradition, educational regulations, including GDPR, and the available technologies. In Round 2, there were no exclusion criteria related to country or resource type as long as the resource was scientifically robust and directly or indirectly based on empirical data. However, only resources that included firsthand empirical data were included as the basis of the synthesis and recommendations, while, for example, systematic reviews and reports were used for perspective and discussion.

Table 1 Inclusion and exclusion criteria
Full size table

In the first round, 2,154 articles were screened, and 112 empirical studies were included in the synthesis. The 112 studies document a positive or negative engagement potential of educational technology related to eight major clusters of educational technologies, hereafter referred to as “types”: (1) learning management systems, (2) discussion forums and weblogs, (3) audience response systems and tablets, (4) online quizzes, (5) social media, (6) video and audio, (7) games and gamification, and (8) virtual reality and simulation. In addition, only eight eligible studies addressed diverse technologies that did not fall within these eight types of technologies (i.e., digital curation tools, e-portfolios, peer feedback tools, haptic devices (except virtual and augmented reality), digital magazines, open badges, word clouds, and diverse or non-specified mobile technologies), thereby constituting an insufficient basis to conclude on their engagement potential and thus excluded from the article. In the second round, the eight identified types of technology were used to search more specifically for the engagement potential of each respective technology. This resulted in screening 618 new articles, of which 60 ended up being added, bringing the total number of studies and other publications included in the article to 196 (see Table 2 and Appendix for details). The second round of searches validated and expanded the already identified recommendations, but only eight new recommendations were identified, suggesting that the list was already saturated.

The coding revealed that a wide range of subject areas were represented, including the social sciences, comprising psychology and business; natural and technical sciences; humanities; and health sciences, as well as a representation of first-year, other undergraduate, and postgraduate teaching. The coding also revealed that most included studies were based on qualitative case studies or quantitative quasi-experimental research methods involving pre- and post-studies or a control group receiving conventional teaching, analysing differences in students’ test results, activity level, perceived engagement, or attitude. However, despite the wide representation of subject areas and levels and the thorough research, it is difficult to generalise findings from these kinds of studies from various contexts. Thus, the findings and recommendations in this article build on the heterogeneity principle (Patton, 2015) that any common finding that emerges from a great variation suggests a potentially more general pattern and forms the basis for the recommendations for each technology collected in Table 2.

3 Results

The included studies show that educational technology can engage students in higher education behaviourally, affectively, and cognitively. However, the studies also show that this potential depends on the context, how the technology is pedagogically and didactically integrated into teaching practice, and that the potential type of engagement varies between the specific educational technologies (Vercellotti, 2018). The findings for actualising the engagement potential of the eight types of educational technologies are further unfolded in the following sections and Table 2.

Table 2 Educational technologies, engagement potential, and recommendations
Full size table

3.1 Learning Management Systems

Learning Management Systems (LMS) is a collective term for web-based learning platforms for developing, distributing, delivering, and administrating educational materials and activities via the Internet (Weller, 2007). 99% of higher education institutions have at least one platform available, of which Canvas, Blackboard, Brightspace, and Moodle are currently the most widespread (Dahlstrom & Bichsel, 2014). Clark et al. (2016) demonstrate that an LMS can lead to increased engagement, better student-educator interaction, and improved learning when used to structure flipped classrooms with online video lessons supplemented by face-to-face activities. Zanjani et al. (2017) also note that engagement is generally strengthened by simple structure and navigation and a manageable number of links and tools that students can customise according to their needs and preferences. Furthermore, Karaksha et al. (2013) highlight that it is relevant to remind students of the available digital tools to increase their use and engagement potential. Vercellotti (2018) compares students’ learning outcomes in online and face-to-face teaching and finds that how the technology is utilised to support an active learning pedagogy plays a crucial role, while Osman (2022) finds that combining synchronous and asynchronous activities in the LMS enhances students’ interaction and engagement and ultimately their satisfaction. Orcutt and Dringus (2017) highlight how educators’ online presence and passion for teaching influence the students’ intellectual curiosity. Wdowik (2014) highlights the opportunities to support more interaction and collaboration between educator and students, as well as among students, using the video conferencing tool in the LMS.

Another potential of LMSs is linked to their tools for tracking students’ activities, progress, and submissions (Veluvali & Surisetti, 2022). Lawrence et al. (2019) point out how learning analytics can promote desired study behaviour and increase behavioural engagement by identifying and assisting students at a low academic level or close to dropping out through reminders, links to resources, or other support for task completion. The study also emphasises the need to explicitly communicate expectations for online students and prepare them for online activities (Pepple, 2022). The tools to monitor students’ progression also influence their retention through continuous summative assessment and peer feedback, and students can monitor their learning. This can be done, for example, through the educator’s feedback on activities and tasks submitted on the e-learning platform (Holmes, 2018) or via peer assessment activities, where students anonymously assess each other’s activities and assignments (Mirmotahari et al., 2019; Sullivan & Watson, 2015).

3.2 Discussion forums and weblogs

Discussion forums and weblogs are typically used for asynchronous activities in which students and the educator discuss and develop ideas related to the course content and form using threaded discussions, text, and possibly multimedia independently of time and place. Most LMSs have a built-in discussion forum that the educator typically manages, whereas weblogs are often managed by the students individually. Research on this technology, in general, focuses primarily on how the technology can be used to train writing, critical thinking, reflection, and argumentation, social constructivist online teaching and peer learning, “scaffolding” (Arend, 2009; Dalsgaard & Paulsen, 2009; MacKnight, 2000; Salmon, 2000; Szabo & Schwartz, 2011), and how students can be activated in their learning processes (Balaji & Chakrabarti, 2010; Dennen, 2005). The included studies show that it is essential that the educator outlines the code of conduct as well as provides short, precise instructions. Additionally, open questions at an appropriate academic level that can encourage all students to participate and discussions where students can apply existing experiences or relate them to their lives can be stimulating. Likewise, the peer aspect of online discussions can contribute to developing students’ professional identity and sense of belonging, thereby increasing their participation (Willis et al, 2013). In addition, audiovisual media can make discussions more authentic for the students (Douglas et al., 2020; Harvey et al., 2018; Kebble, 2017; Page et al., 2020). Stimulating questions can, for example, be formulated based on Bloom’s taxonomy (Badenhorst & Mather, 2014; Shaw & Irwin, 2017), and students’ participation can be strengthened by providing exemplars of “quality discussions” (Kebble, 2017). It is also effective to let the discussion be based on questions and topics that are engaging for students, such as relevant cases and real situations, and that invite students to share different opinions and develop personal perspectives through reflection questions (Buelow et al., 2018; Fukuzawa & Boyd, 2016). Another important factor is the educator’s visible and active participation in the discussion forum, which can consist of relevant contributions related to the issues the students are discussing (Collins et al., 2019; Mokoena, 2013; Mooney et al., 2014) or guide and point out relevant teaching materials that students can work with (Fukuzawa & Boyd, 2016). It also has a positive effect on engagement if students are assigned roles that frame their active participation in the discussion (Mooney et al., 2014; Truhlar et al., 2018), there is a requirement to use a specific argumentation model (Oh & Kim, 2016), or the students’ participation is assessed according to well-defined criteria (Kebble, 2017; Wyatt, 2021). Truhlar et al. (2018) highlight that activities in which students summarise discussions stimulate higher-order thinking. Discussions with many participants and repetitive and extensive posts are experienced as frustrating, so large groups should consider this (Fukuzawa & Boyd, 2016; Kebble, 2017). Concerning weblogs in formal settings, Sharma and Tietjen (2016) demonstrate a similar effect on education, indicating that the technology is viable for supporting both students’ collaboration and meaning-making.

3.3 Audience response systems

Audience response systems and devices (ARS) are a collective term for a range of software and hardware-based technologies that allow students to participate in activities such as polls or ask questions and provide answers interactively during lectures using their computer, tablet, mobile phone, or a so-called clicker. The majority of studies find that activities involving audience response systems enhance student engagement (Çakir, 2020; Fischer et al., 2015; Funnell, 2017; Habel & Stubbs, 2014; Han & Finkelstein, 2013; Jozwiak, 2015; Kay & LeSage, 2009; Remón et al., 2017; Sawang et al., 2017; Shaw et al., 2015; Sun et al., 2014), and a comprehensive literature review from 2009 highlights the technology’s potential to particularly increase behavioural and cognitive engagement (Kay & LeSage, 2009). Shaw et al. (2015) and Lim’s (2017) studies demonstrate that digital polls with questions and answers foster a sense of cohesion between the educator and students, which is not typically experienced in large classes. The technology also provides educators with insights into students’ learning outcomes for continuous feedback and addressing their questions (McKenzie & Ziemann, 2020; Remón et al., 2017; Robson & Basse, 2018; Yilmaz, 2017) and allows students to pause the classroom if they needed more time (Dong et al., 2017). Polls should ideally be academically challenging (Sawang et al., 2017), preferably combined with group activities (Jozwiak, 2015) or plenary discussions in the class (Robson & Basse, 2018; Sawang et al., 2017), and ideally allow students to respond anonymously (Heaslip et al., 2014: Remón et al., 2017). Notably, the opportunity to discuss the reasoning behind poll responses is crucial (Habel & Stubbs, 2014; Steadman, 2015; see also “Peer Instruction,” Crouch & Mazur, 2001, and Thomas et al., 2017). It can also enhance engagement if students formulate questions themselves (Song et al., 2017) or if the question is open-ended, controversial, or requires ethical consideration or higher-order thinking (Campbell & Monk, 2015; Steadman, 2015; Wood & Shirazi, 2020). Finally, the technology can support students’ mutual dialogue through a “backchannel,” where students can discuss ongoing teaching, leading to higher student satisfaction, higher grades, and more frequent use of class content (Neustifter et al., 2016).

3.4 Online quizzes

In online quizzes, students can answer questions related to the subject matter. Online quizzes differ from audience response stems by being fully online and, typically, asynchronous so that they can be used and reused regardless of time and place. The activities contribute to students’ understanding and deep learning and consolidate what has been learned (Argyriou et al., 2022; Browne, 2019; Russell et al., 2016). Students appreciate the flexible access, the options to revisit the quizzes, and the ability to do the quizzes at their own pace (Browne, 2019). When quizzes are used regularly for providing feedback, it promotes students’ engagement (Browne, 2019; Holmes, 2015; Lee & Harris, 2018; McKenzie et al., 2013) and is an effective mechanism for incentivising student completion of preparatory work (Cann, 2016; Cook & Babon, 2017; Cossu et al., 2022). It is important to use various quiz question types (Browne, 2019) and provide the students with specific feedback so that they can monitor and self-regulate their studying and progression (Evans et al., 2021; Thomas et al., 2017). Combining quizzes with group activities promotes students’ engagement and learning outcomes (Balta & Awedh, 2017) and supports collaborative learning.

3.5 Social media

Social media is a collective term for web-based social networks where users can socialise, communicate, and share files and other information. Social media is typically not an institutionalised learning technology but often plays a role in students’ social interaction and their informal digital learning environment (frequently referred to as “personal learning environment,” PLE, see also Caviglia et al., 2018) or as part of the curriculum (see Delello et al., 2015; Megele, 2015). Overall, studies indicate that increased interaction and collaboration opportunities offered by the social media in terms of their flexibility and the ability to incorporate external resources contribute to enhanced motivation and interest in teaching (Camus et al., 2016; Cooper & Naatus, 2014; Chugh & Ruhi, 2018; Delello et al., 2015; Evans, 2014; Glowatz & Bofin, 2014; Graham, 2014; Gregory et al., 2016; Kent, 2013; Northey et al., 2015; Scott & Stanway, 2015; Sharma & Tietjen, 2016). Students prefer Facebook and Twitter (now “X”) over discussion forums in LMSs, as they are perceived as more accessible than the LMSs’ discussion forums (Kent, 2013) and are more familiar (Clements, 2015). However, other studies suggest that familiarity with Facebook does not guarantee its use for study purposes (Dyson et al., 2015; Gregory et al., 2016). Similarly, Cooke (2017) points out a risk that students may lose interest in the specific social media and, as a result, its value as a supplementary tool for supporting discussions if the platform is their primary learning platform and its use is mandatory (Cooke, 2017). Both Camus et al. (2016) and Kent (2013) note that the use of Facebook resulted in more dialogue compared to the institutionalised LMS, and Evans (2014), Tiernan (2014), and Pallas et al. (2019) find that social media can also contribute to increasing student collaboration, creating an inclusive atmosphere that increases the participation of “quiet” students and supporting deep learning (Megele, 2015). However, if assessment is involved, it is important to be explicit about expectations and criteria (O’Brien & Freund, 2018). Similarly, Barber et al. (2015) show that a “Digital Moments” course helped create meaningful online learning communities among the students. Kent (2013) also points to a different perception and use of social media and LMS. LMS is associated with formal learning, while social media is more often used for practical questions and informal collaboration. Several studies describe different ways Twitter has been used: as a channel for questions to the instructor during class (Kunka, 2020; Tiernan, 2014; Prestridge, 2014), as a discussion forum between students and possibly external participants (Bender, 2021; Dragseth, 2020; Megele, 2015), and as a channel for students to share academic examples (Prestridge, 2014). Diug et al. (2016) demonstrate that Twitter gave students a sense of increased access to their educators while supporting their collaboration.

3.6 Video, audio, and multimedia

Video, audio, and multimedia are used here as a broad term for synchronous and asynchronous, audiovisual and digital multimedia, such as video presentations of course content and feedback on assignments, video recordings from field trips, and video assignments, produced by both the educator, students, or external providers. Video can be used, for example, to “flip” the teaching, allowing students to watch video lectures at home, creating more time for in-class dialogue (Noetel et al., 2021; Willis et al., 2018), appealing to multiple sensory channels simultaneously (Mayer, 2008), and supporting more authentic communication compared to written communication (Henderson & Phillips, 2015; McCarthy, 2015; Noetel et al., 2021; Oh & Kim, 2016). Activities where students produce audio can enhance their engagement, provided they have the equipment and skills to create them (Bolliger & Armier, 2013). In addition, student-produced audio materials can have a socialising effect on teaching due to their authenticity and personal touch, offering variation compared to traditional written assignments (Barber et al., 2015; Bolliger & Armier, 2013). Similarly, audio and video feedback from the educator is perceived as more personal and information-rich than written feedback (Cavaleri et al., 2019; Pearson, 2018; Rasi & Vuojärvi, 2018; Seery, 2015; Zhan, 2023) as well as video conferences can make the educator more visible and “accessible” than in face-to-face teaching (Gleason & Greenhow, 2017; Ng, 2018; Wdowik, 2014), thus creating a closer connection and being perceived as more personal (Steele et al., 2018). Educator feedback on video is often revisited and used in later assignments (Speicher & Stollhans, 2015). Several studies document a generally positive attitude towards video lectures and instructions among students, providing greater flexibility and allowing more independence in the learning process compared to face-to-face teaching (O’Callaghan et al., 2017; Gnaur & Hüttel, 2014; Lin et al., 2017; Lupinski & Kaufman, 2023; Scagnoli et al., 2019; Seery, 2015; Speicher & Stollhans, 2015). Scagnoli et al. (2019) conclude that the more video lectures students watch, the more positively they perceive the medium. However, they also emphasise the importance of familiarity with and experience using video for learning purposes, students’ academic level (postgraduate students are more positive than undergraduates), and how well the video lectures are integrated into the course. In addition, Brame (2016) stresses the importance of minimising students’ cognitive load when watching the videos — a parallel theme to research on “attention span,” which ambiguously indicates various durations students can maintain concentration depending on the context, teaching format, subject matter, and the students’ characteristics (Bradbury, 2016; Hartley & Davies, 1978). However, there are also studies highlighting the risk of a more superficial learning approach (Francescucci & Rohani, 2019; Trenholm et al., 2019), lower learning outcomes (Roberts, 2015), lower attendance in class (O’Callaghan et al., 2017), and lower engagement with video lectures where in particular the low-performing students are at risk (Murphy & Stewart, 2015). Lin et al. (2017) point out that students found concrete, instructional videos for laboratory work more useful and essential for their learning than video lectures of a generally more conceptual nature. However, the longer the videos are, the fewer students will watch them to the end (Lin et al., 2017). Video combined with other activities such as quizzes, small assignments, group work, or individual feedback positively impacts student engagement (Brame, 2016; Gnaur & Hüttel, 2014; Jozwiak, 2015; Paiva et al., 2017). In addition, student-produced video and audio for learning and assessment purposes may also positively impact students’ learning experience and contribute to the development of their communication, knowledge construction, and teamwork skills (Arsenis et al., 2022; Mathany & Dodd, 2018; Morena et al., 2019), for example, in the form of digital storytelling, which can also contribute to developing social and cultural competencies (Grant & Bolin, 2016; Ribiero, 2016; Yousuf & Conlan, 2018).

3.7 Games and gamification

Games and gamification as educational technology involve activities with various forms of game elements such as leaderboards, points, badges, or other forms of rewards or competition. The technology distinguishes itself from online quizzes by extensively using entertainment and possible competitive elements to motivate students’ participation and learning (Educause Learning Initiative, 2011). Subhash and Cudney (2018) find in their review that the elements mentioned above increase, in particular, the students’ attitude, level of participation, motivation, and performance. However, several studies also highlight the importance of authenticity and its relation to reality. Edmonds and Smith (2017) find that mobile learning games can engage students if they involve interactive investigations of phenomena with fellow students and involve them as designers of similar games. Similarly, Buckley and Doyle (2016) find that involving games with real-world dilemmas and decisions increases student engagement. However, it is important to note that students who are already gamers are more positive towards games in education than other students (Davis et al., 2018). Bawa (2019), Plump and LaRosa (2017), and Holbrey (2020) find that the game-inspired polling tool Kahoot can increase student engagement and participation in education if used for students to play together in groups against other groups, collaboratively create quizzes for other groups based on the curriculum, and this subsequently forms the basis for discussion among the students. Viswanathan and Radhakrishnan (2018) document in this context that students find it engaging to be co-developers of a game and that it supports their critical thinking. The combination of games and collaboration is also highlighted by Christopoulos et al. (2018), who, in their study, emphasise the importance of both the interaction among students and the function of the game. For example, individual games that test students’ knowledge will only be engaging for a few students (Christopoulos et al., 2018).

3.8 Virtual reality and simulation

Virtual reality (VR) and simulation are computer-generated simulations of an environment where educators and students can interact via a computer or, for example, through a dedicated headset (Makransky & Petersen, 2019). Studies indicate a general increase in engagement, especially due to the sense of presence (Cavanaugh et al., 2023; Chulkov & Wang, 2020; Papanastasiou et al., 2019; Rafiq et al., 2022), the simulated first-hand experiences that would have been impossible in the real world (Di Natale et al., 2020) for instance, interacting with three-dimensional virtual molecular phenomena (Elford et al., 2021), doing virtual field trips in Google Earth (McDaniel, 2022), use virtual microscopes for manipulation of online images (Herodotou et al., 2020) and provide variation for the students in the learning process (Hayes et al., 2021). However, opinions on the technology may be divided, and reservations among students often stem from a lack of experience and comfort in participating and interacting in VR (Francescucci & Foster, 2013). Francescucci and Foster (2013) and Makransky and Lilleholt (2018) find it essential to ensure that students have a high level of autonomy through a sense of control and active learning when using the technology, while others find it important that educators have the qualifications to use VR for learning purposes, give time for students to get familiar with the technology and have access to support in initial phases (Nesenbergs et al., 2020; Pellas et al., 2021). Luo et al. (2021) find that activities in VR can benefit from being combined with non-VR activities, including group or educator debriefings related to the VR activities. Pellas and Kazanidis (2015) found significantly positive learning outcomes and engagement results for teaching conducted solely in Second Life, compared to combined Second Life and face-to-face teaching. Matthew & Butler’s (2017) study showed that video from Second Life was suitable for simulating authentic problems, positively influencing students’ engagement and learning outcomes. Similarly, Sobocan and Klemenc-Ketis (2017) document that virtual patients in teaching for diagnosis and medical practice are perceived as beneficial due to the increased opportunities for skill training. Likewise, a positive effect on student engagement is demonstrated in simulations. Pallas et al. (2019) identify how simulations can increase students’ online interaction and reflection, including involving otherwise quiet students. Irby et al. (2018) and Marques et al. (2014) point out that virtual laboratories can be just as engaging as working in a physical laboratory and, in some situations, primarily introductory modules, completely replace face-to-face laboratory work.

4 Discussion

Overall, the included studies document the potential of educational technology to engage students in higher education behaviourally, affectively, and cognitively, which is dependent on the context, integration, and the specific educational technology as well as the specific technology’s support for structure, active learning, communication, and interaction between students and/or educators (Fig. 1, further developed from Schindler et al., 2017). Furthermore, the synthesis indicates that each of the eight technologies has the potential to support all three forms of engagement, of which some are more well-documented than others and that they are interconnected.

Fig. 1
figure 1

Overview of the potential of educational technology for student engagement

Full size image

Across 64 studies, the impact on students’ behavioural engagement is documented, particularly in the context of LMSs, discussion forums, audience response systems, online quizzes, social media, video and audio, and virtual reality and simulations. The studies document that technologies suitable for conveying curriculum content, creating structure, providing assessment tasks, and facilitating interaction and active learning effectively support students’ behavioural engagement. The interaction between students and content, educators, and peers is crucial for behavioural engagement (McCallum et al., 2015) as well as a course organisation with clear learning goals, logical course structures, recurring activities, and regular interactions with peers and educators contribute to behavioural engagement, satisfaction, and learning (Gray & DiLoreto, 2016; Gross et al., 2015; Porcaro et al., 2016; Ravenscroft & Luhanga, 2018; Ravishankar et al., 2018). Thus, this also shows how structure influences students’ affective engagement. Muir et al. (2019) highlight the importance of assessment tasks, workload, work-life balance, assignment quality, and educator presence. While online activities can enhance retention and engagement (Callahan, 2016), Dumford & Miller, (2018) note a link between students’ preferences and experience with online learning. Studies emphasise the flexibility of access to online teaching materials, with video lectures freeing up time for more engaging in-class activities (Steen-Utheim & Foldnes, 2018).

The impact of technology on students’ affective engagement is highly linked to how it influences the communication and interaction between students and educators, as documented in 59 studies. Communication tools within the LMS, discussion forums for peer learning, social media, competitive game elements, VR and simulations, and other audiovisual media can play a key role in this context. In general, technologies facilitating multi-faceted communication and interaction and educator involvement are often effective for affective engagement (Vayre & Vonthron, 2017). Educator presence, social support, figurative language, and effective facilitation are pivotal factors in online settings (Dixson et al., 2017; O’Shea et al., 2015; Orcutt & Dringus, 2017; Yates et al., 2014). Nevertheless, students’ low technological skills can negatively impact their affective engagement (Butz et al., 2016; Vayre & Vonthron, 2017), and some students may prefer using technologies they are already familiar with (del Barrio-Garcia et al., 2015). While students generally have experience with and a positive attitude towards technology in education, they may lack the skills to use technology in their academic work (Kim et al., 2019). Technology and online teaching can also hinder students’ involvement in the informal, implicit aspects of academic work (Selwyn, 2016).

The cognitive engagement is documented in 46 studies and notably supported by technologies such as audio and video, virtual reality and simulations, and audience response systems used to facilitate active and flexible student involvement in high taxonomic learning activities, such as collaboration, problem-solving, reflection, authentic exploration, and hypothesis testing. Flexible technology access supports self-directed learning, motivating students to engage actively (Mello, 2016; Mihret et al., 2017). McGuinness and Fulton (2019) illustrate the value of online tutorials as a flexible supplement to in-class teaching, aiding students in self-paced learning. Mihret et al.’s (2017) case-based teaching, combined with online discussions and ongoing e-portfolio assessment, enhances self-directed learning compared to face-to-face participation. However, high flexibility may negatively impact affective engagement due to the self-discipline required (McCallum et al., 2015). The technology may also support adaptive learning involving diagnostic quizzes, individual materials, formative tests, lectures, and summative tests that enhance satisfaction, performance, and cognitive engagement, as McKenzie et al. (2013) and Pourdana (2022) demonstrated. Technology supporting pedagogical strategies, like Baum’s (2013) guided inquiry, blends short video lectures and self-organised problem-solving, proving less confusing than traditional teaching. Gibbings et al. (2015) highlight the role of technology in providing authentic online activities and fostering communication, collaboration, and personal development despite geographical distances. Activities that challenge students’ understanding of societal issues, entertaining elements, and connections to past experiences also enhance cognitive and affective engagement (Buelow et al., 2018; O’Shea et al., 2015).

4.1 Breadth and Interconnectedness

When looking across the three types of engagement, there is no clear pattern in which technologies that engage students in more than one way. However, as also stressed by Payne (2019) and Fredricks et al. (2004), engagement is often interconnected, and indicators can be ambiguous. This interconnectedness is notably evident from the 25 included studies on specific technologies and background studies that document the technology’s potential to engage students in multiple ways simultaneously as well as from the studies that investigate the impact of technology-enhanced learning designs in education (e.g., Gray & DiLoreto, 2016; Gross et al., 2015; Porcaro et al., 2016; Ravenscroft & Luhanga, 2018; Ravishankar et al., 2018). Audience response systems and video, audio, and multimedia appeared most frequently in the studies of specific technologies, with seven and five studies, respectively, and three studies demonstrated a potential to support all three types of engagement simultaneously (Chulkov & Wang, 2020, on VR, and Christopoulos et al., 2018, on games and gamification; and Neustifter et al., 2016, on audience response systems). The varying documented breadth may be due to a narrow focus of the individual studies, but it may also suggest a diverse potential to support student engagement more broadly. Furthermore, it may indicate that it often does not make sense to talk about a specific type of engagement potential as they are often interconnected and/or prerequisites for each other, just as important indicators can be overlooked. For example, Bond et al. (2020) categorise “confidence” as a (direct) indicator of affective engagement as well as an (indirect) indicator of behavioural engagement. The rationale is that the students’ confidence with the technology is manifested in their constructive behaviour. Likewise, cognitive engagement can manifest as self-regulated behaviour or simple memorisation (Fredricks et al., 2004).

Overall, the conceptual framework of student engagement by Fredricks et al. (2004) and the indicators provided by Bond et al. (2020) are useful for capturing a broad spectrum of the concept. This includes both observable behaviours, traditionally associated with narrow understandings of student engagement and the broader understandings, where student engagement is linked to experience, satisfaction, learning outcomes, and various affective and cognitive factors. This broader conceptualisation also addresses the additional dimensions proposed by Kahu (2013), Linnenbrink-Garcia et al. (2011), and Reeve and Tseng (2011). Furthermore, these frameworks accommodate indicators that may be overlooked without technology. For example, the use of technology allows for the observation of student engagement in online peer feedback activities (Mirmotahari et al., 2019) and supports self-regulated behaviours through online quizzes that enable students to monitor their progress and receive automated feedback (Evans et al., 2021; McKenzie et al., 2013; Thomas et al., 2017). However, the results suggest that one should place little importance on the actual classification but rather consider whether a given indicator may point to multiple types of engagement and be connected to other indicators.

4.2 How to Engage Students with Educational Technology in Higher Education?

The answer to the research question depends on the type of engagement one wishes to support, available technologies, and the specific context and educator competencies. For instance, to increase students’ behavioural engagement, educators may utilise technologies that provide structure and support active content delivery, such as LMSs, ARSs, and online quizzes, and follow the provided recommendations (Table 2). Those aiming to increase students’ affective engagement can benefit from technologies supporting student interaction, like discussion forums, social media, and games. Educators wanting to support students’ cognitive engagement can use simulations to aid students in authentic exploration of a given topic, have the students produce their own video, or facilitate structured online discussions. If there is a need to engage students behaviourally, affectively, and/or cognitively at the same time, it is relevant to consider technologies with a documented, broad engagement potential. However, if the educational technology is already provided, the recommendations provided (Table 2) can increase the chances of engaging students with the respective technology.

4.3 Limitations

The study in this article has revealed limitations related to the concept of student engagement and an inherent limitation associated with the research methods of the available studies.

The term “engagement” is ambiguous in English and may refer to attending something in a broad sense (Payne, 2019) or, in a narrow sense, referring to student behaviour in class (Zepke, 2015). Conversely, studies may deal with student engagement without necessarily using the term. A similar limitation is seen in the naming of educational technologies, which are often referred to by the name of the software or hardware and not necessarily by the type of technology, which is why it is easy to overlook relevant studies with a traditional protocol-driven search strategy based on keywords.

In addition, the study confirmed that engagement can be interconnected and indicators can be ambiguous. This is not a problem per se in realising the technology’s engagement potential but rather a problem in analysing studies that investigate and document a narrow engagement potential. Thus, further validation and mapping of the interconnectedness of Bond et al. (2020)'s indicators would be useful.

Finally, there is a limitation that relates to the nature of the available studies, which are often characterised by qualitative case and quasi-experimental studies and other research methodologies in which it is difficult to distinguish the cause of the effect from other factors such as the novelty effect (McKechnie, 2008), the redesign of teaching that the introduction of technology entails (Kirkwood & Price, 2014), and the context (Schindler et al., 2017) and thus also to generalise findings. This calls for more research on the significance of the teaching context, including course design, course delivery, and other contextual factors.

5 Conclusion and implications

The article has identified the potential of educational technology to support students’ behavioural, affective, and cognitive engagement, along with a series of specific recommendations on how to realise this potential. These recommendations can be used, for example, by educators to incorporate specific, available educational technologies into their teaching or as an educational development method to enhance particular forms of student engagement. Educators and educational developers can use these recommendations to qualify the use of educational technology for student engagement in higher education. While the studies highlight various engagement potentials of educational technology, the synthesis also revealed that whether this potential is realised is dependent on the context, integration, the specific technology, and the educator’s competencies in teaching with technology (see also Orcutt & Dringus, 2017, and Schindler et al., 2017). Furthermore, the synthesis also shows that all included technologies can support all three kinds of engagement, that engagement is often interconnected, and that technologies may vary in how broad their engagement potential is. Therefore, the recommendations should be viewed for what they are — practical guidelines derived from what was effective in another context — and should always be adjusted based on what is possible and relevant in the given situation. One cannot expect a specific effect on learning outcomes or student engagement simply by introducing a specific educational technology, and only few studies investigate aspects such as the importance of context, the role of the educator, how students interact, and what happens in the actual learning process (e.g., Bertheussen & Myrland, 2016; Butz et al., 2016; Evans, 2014; Steen-Utheim & Foldnes, 2018; Vercellotti, 2018). This calls for more research on the influence of course context and delivery on student engagement.

The synthesis also revealed that many aspects that determine whether the potential is realised are recognisable from traditional face-to-face teaching. For example, the educator’s active role as a facilitator of learning, active involvement of students, and consideration for students and their needs are crucial, as well as the technical support, feedback, authenticity, and learning environment. This is not surprising but important to remember when designing and delivering technology-enhanced, blended, and online learning. Careful considerations should be made for both the design and delivery of teaching: What is the purpose of educational technology, what potential does this technology hold for student engagement, and what determines whether this potential is realised? Thus, if all forms of engagement are to be supported by technology, the educator must have competencies in structuring, developing, and delivering technology-enhanced teaching, as well as taking the possibilities, engagement broadness, and limitations of the technology into account. Furthermore, the educator must be able to communicate and involve students in an activating way in high-taxonomic learning activities, as well as support students’ communication and interaction through suitable technology.