virtual learning research paper

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• Published: 25 January 2021

Online education in the post-COVID era

• Barbara B. Lockee 1  

Nature Electronics volume  4 ,  pages 5–6 ( 2021 ) Cite this article

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The coronavirus pandemic has forced students and educators across all levels of education to rapidly adapt to online learning. The impact of this — and the developments required to make it work — could permanently change how education is delivered.

The COVID-19 pandemic has forced the world to engage in the ubiquitous use of virtual learning. And while online and distance learning has been used before to maintain continuity in education, such as in the aftermath of earthquakes 1 , the scale of the current crisis is unprecedented. Speculation has now also begun about what the lasting effects of this will be and what education may look like in the post-COVID era. For some, an immediate retreat to the traditions of the physical classroom is required. But for others, the forced shift to online education is a moment of change and a time to reimagine how education could be delivered 2 .

Looking back

Online education has traditionally been viewed as an alternative pathway, one that is particularly well suited to adult learners seeking higher education opportunities. However, the emergence of the COVID-19 pandemic has required educators and students across all levels of education to adapt quickly to virtual courses. (The term ‘emergency remote teaching’ was coined in the early stages of the pandemic to describe the temporary nature of this transition 3 .) In some cases, instruction shifted online, then returned to the physical classroom, and then shifted back online due to further surges in the rate of infection. In other cases, instruction was offered using a combination of remote delivery and face-to-face: that is, students can attend online or in person (referred to as the HyFlex model 4 ). In either case, instructors just had to figure out how to make it work, considering the affordances and constraints of the specific learning environment to create learning experiences that were feasible and effective.

The use of varied delivery modes does, in fact, have a long history in education. Mechanical (and then later electronic) teaching machines have provided individualized learning programmes since the 1950s and the work of B. F. Skinner 5 , who proposed using technology to walk individual learners through carefully designed sequences of instruction with immediate feedback indicating the accuracy of their response. Skinner’s notions formed the first formalized representations of programmed learning, or ‘designed’ learning experiences. Then, in the 1960s, Fred Keller developed a personalized system of instruction 6 , in which students first read assigned course materials on their own, followed by one-on-one assessment sessions with a tutor, gaining permission to move ahead only after demonstrating mastery of the instructional material. Occasional class meetings were held to discuss concepts, answer questions and provide opportunities for social interaction. A personalized system of instruction was designed on the premise that initial engagement with content could be done independently, then discussed and applied in the social context of a classroom.

These predecessors to contemporary online education leveraged key principles of instructional design — the systematic process of applying psychological principles of human learning to the creation of effective instructional solutions — to consider which methods (and their corresponding learning environments) would effectively engage students to attain the targeted learning outcomes. In other words, they considered what choices about the planning and implementation of the learning experience can lead to student success. Such early educational innovations laid the groundwork for contemporary virtual learning, which itself incorporates a variety of instructional approaches and combinations of delivery modes.

Online learning and the pandemic

Fast forward to 2020, and various further educational innovations have occurred to make the universal adoption of remote learning a possibility. One key challenge is access. Here, extensive problems remain, including the lack of Internet connectivity in some locations, especially rural ones, and the competing needs among family members for the use of home technology. However, creative solutions have emerged to provide students and families with the facilities and resources needed to engage in and successfully complete coursework 7 . For example, school buses have been used to provide mobile hotspots, and class packets have been sent by mail and instructional presentations aired on local public broadcasting stations. The year 2020 has also seen increased availability and adoption of electronic resources and activities that can now be integrated into online learning experiences. Synchronous online conferencing systems, such as Zoom and Google Meet, have allowed experts from anywhere in the world to join online classrooms 8 and have allowed presentations to be recorded for individual learners to watch at a time most convenient for them. Furthermore, the importance of hands-on, experiential learning has led to innovations such as virtual field trips and virtual labs 9 . A capacity to serve learners of all ages has thus now been effectively established, and the next generation of online education can move from an enterprise that largely serves adult learners and higher education to one that increasingly serves younger learners, in primary and secondary education and from ages 5 to 18.

The COVID-19 pandemic is also likely to have a lasting effect on lesson design. The constraints of the pandemic provided an opportunity for educators to consider new strategies to teach targeted concepts. Though rethinking of instructional approaches was forced and hurried, the experience has served as a rare chance to reconsider strategies that best facilitate learning within the affordances and constraints of the online context. In particular, greater variance in teaching and learning activities will continue to question the importance of ‘seat time’ as the standard on which educational credits are based 10 — lengthy Zoom sessions are seldom instructionally necessary and are not aligned with the psychological principles of how humans learn. Interaction is important for learning but forced interactions among students for the sake of interaction is neither motivating nor beneficial.

While the blurring of the lines between traditional and distance education has been noted for several decades 11 , the pandemic has quickly advanced the erasure of these boundaries. Less single mode, more multi-mode (and thus more educator choices) is becoming the norm due to enhanced infrastructure and developed skill sets that allow people to move across different delivery systems 12 . The well-established best practices of hybrid or blended teaching and learning 13 have served as a guide for new combinations of instructional delivery that have developed in response to the shift to virtual learning. The use of multiple delivery modes is likely to remain, and will be a feature employed with learners of all ages 14 , 15 . Future iterations of online education will no longer be bound to the traditions of single teaching modes, as educators can support pedagogical approaches from a menu of instructional delivery options, a mix that has been supported by previous generations of online educators 16 .

Also significant are the changes to how learning outcomes are determined in online settings. Many educators have altered the ways in which student achievement is measured, eliminating assignments and changing assessment strategies altogether 17 . Such alterations include determining learning through strategies that leverage the online delivery mode, such as interactive discussions, student-led teaching and the use of games to increase motivation and attention. Specific changes that are likely to continue include flexible or extended deadlines for assignment completion 18 , more student choice regarding measures of learning, and more authentic experiences that involve the meaningful application of newly learned skills and knowledge 19 , for example, team-based projects that involve multiple creative and social media tools in support of collaborative problem solving.

In response to the COVID-19 pandemic, technological and administrative systems for implementing online learning, and the infrastructure that supports its access and delivery, had to adapt quickly. While access remains a significant issue for many, extensive resources have been allocated and processes developed to connect learners with course activities and materials, to facilitate communication between instructors and students, and to manage the administration of online learning. Paths for greater access and opportunities to online education have now been forged, and there is a clear route for the next generation of adopters of online education.

Before the pandemic, the primary purpose of distance and online education was providing access to instruction for those otherwise unable to participate in a traditional, place-based academic programme. As its purpose has shifted to supporting continuity of instruction, its audience, as well as the wider learning ecosystem, has changed. It will be interesting to see which aspects of emergency remote teaching remain in the next generation of education, when the threat of COVID-19 is no longer a factor. But online education will undoubtedly find new audiences. And the flexibility and learning possibilities that have emerged from necessity are likely to shift the expectations of students and educators, diminishing further the line between classroom-based instruction and virtual learning.

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Lockee, B.B. Online education in the post-COVID era. Nat Electron 4 , 5–6 (2021). https://doi.org/10.1038/s41928-020-00534-0

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The Research Alliance for New York City Schools

Exploring the Evidence on Virtual and Blended Learning

Chelsea farley (2020).

The Research Alliance has developed an overview of research and practical guidance on strategies to implement remote teaching and learning, as well as strategies that combine virtual and in-class instruction. While not a complete summary of the relevant literature, our overview provides links to a variety of useful articles, resources, and reports. We hope this material can inform school and district leaders’ planning and support their ongoing assessment of what has and has not been effective, for whom, and under what conditions.

Key Takeaways from the Research Alliance’s Review

• Eight months into the COVID-19 pandemic, there is still an enormous need for data and evidence to understand how the school closures that took place in NYC and around the country—and how the various approaches to reopening—have affected students’ academic, social/emotional, and health outcomes. New research is needed to inform critical policy and practice decisions. (Below we highlight specific kinds of data that would help answer the most pressing questions.)
• Past research about online learning is limited and mostly focused on post-secondary and adult education. The studies that do exist in K-12 education find that students participating in online learning generally perform similarly to or worse than peers who have access to traditional face-to-face instruction (with programs that are 100% online faring worse than blended learning approaches). It is important to note that this research typically compares online learning with regular classroom instruction—rather than comparing it to no instruction at all—and that these studies took place under dramatically different conditions than those resulting from COVID-19.
• Studies of blended learning, personalized learning, and specific technology-based tools and programs provide hints about successful approaches, but also underscore substantial “fuzziness” around the definition of these terms; major challenges to high-quality implementation; and a lack of rigorous impact research.
• Teaching quality is more important than how lessons are delivered  (e.g., “clear explanations, scaffolding and feedback”);
• Ensuring access to technology is key , particularly for disadvantaged students and families;
• Peer interactions can provide motivation and improve learning outcomes  (e.g., “peer marking and feedback, sharing models of good work,” and opportunities for collaboration and live discussions of content);
• Supporting students to work independently can improve learning outcomes  (e.g., “prompting pupils to reflect on their work or to consider the strategies they will use if they get stuck”, checklists or daily plans); and
• Different approaches to remote learning suit different tasks and types of content.

Our overview highlights these and other lessons from dozens of relevant studies. It also underscores the need for more rigorous evidence about the implementation and impact of different approaches to remote and blended learning, particularly in the context of the current pandemic. To begin to fill these knowledge gaps,  the Research Alliance strongly encourages schools and districts—including the NYC Department of Education—to collect, analyze, and share data about :

• COVID-19 testing results,
• Professional development aimed at helping teachers implement remote and blended learning,
• Students’ attendance and engagement (online and in person),
• Students’ social and emotional wellbeing,
• Students’ and families’ experiences with remote and blended instruction,
• Teachers’ experiences with remote and blended instruction, and—critically—
• What students are learning, over time.

All of this should be done with an eye toward pre-existing inequalities—especially differences related to race/ethnicity, poverty, home language, and disability. These data are crucial for understanding how COVID-19 has affected the educational trajectories of different groups of students and for developing strong policy and practice responses. 

Read our full overview here . This document was initially released in May and updated in November of 2020.

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Effects of virtual learning environments: A scoping review of literature

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• Volume 27 , pages 3683–3722, ( 2022 )

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The purpose of this scoping review is to isolate and investigate the existing data and research that identifies if the synchronous face-to-face visual presence of a teacher in a virtual learning environment (VLE) is a significant factor in a student’s ability to maintain good mental health. While the present research on this explicit interaction among VLE implementation and student mental health is limited, the material suggests a framework for strong utilization of VLEs. Overall, our research has shown that authentic, high quality VLEs are ones that have as their primary focus the communication between students and their teachers and between students and their peers. This communication is best generated through synchronous connections where there exists the ability to convey the student’s immediate needs in real-time. Our research results and discussion will outline how a team approach that brings together teachers, students, administration, counsellors, mental health support staff, instructional designers, and ICT specialists is necessary to create a genuinely enriching VLE where both learning and social-emotional needs can be met. The authors present a case for further study in order to reveal the nature of the interaction among VLEs and student mental health.

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1 Introduction

Increasingly educators are turning to digital online tools and resources to supplement and enhance their teaching in the classroom. Independent of the move to online learning as a result of the COVID-19 pandemic, there is a clear upward trend to the prevalence and variety of online learning opportunities available to students of all ages. In their meta-analysis, Means et al. ( 2009 ) show that the rise of online learning is for a variety of reasons:

Online learning has become popular because of its potential for providing more flexible access to content and instruction at any time, from any place. Frequently, the focus entails (a) increasing the availability of learning experiences for learners who cannot or choose not to attend traditional face-to-face offerings, (b) assembling and disseminating instructional content more cost efficiently, or (c) enabling instructors to handle more students while maintaining learning outcome quality that is equivalent to that of comparable face-to-face instruction. (p. 22)

In Ontario, online learning can be accessed through traditional school boards using eLearning Ontario and Brightspace or through available online private schools such as VirtualHighSchool.com. These virtual learning environments (VLEs), online spaces that facilitate the delivery of curriculum content, assessment, and evaluation activities – deliver said curriculum in an asynchronous manner, that is “learning that is not delivered in real time. Asynchronous learning may involve students watching pre-recorded video lessons, completing assigned tasks, or contributing to online discussion boards” (Ontario Ministry of Education, 2020 , PPM 164). This is different than synchronous learning which is defined as “Learning that happens in real time. Synchronous learning involves using text, video, or voice communication in a way that enables educators…to instruct and connect with students in real time” (Ontario Ministry of Education, 2020 , PPM 164). It is believed that “synchronous learning supports the well-being and academic achievement of all students…by providing educators and students with an interactive and engaging way to learn. It helps teachers provide immediate feedback to students and enables students to interact with one another” (Ontario Ministry of Education, 2020 , PPM 164). Despite this fulsome definition of synchronous learning, little research-driven policy or infrastructure exists whereby educators can be trained and supported in creating an ideal VLE for their students’ educational and social-emotional needs (Jones, 2015 ; Kent et al., 2018 ). This was certainly the case for all Ontario educators when, in March 2020, schools were closed due to the SARS-COVID-19 global pandemic and learning shifted to online-only instruction as contextualised in the following section.

1.1 Background

On Thursday, March 12, 2020 at 4:00p.m. we tuned into the local news channel to hear that the Ontario Provincial Government was putting the province into lockdown to avert the upcoming threat of the novel Coronavirus, SARS-COVID-19. In this same news conference, we learned that Friday, March 13, 2020 would be our last day in schools; schools would be closed for two weeks after the March Break which began the following Monday. We spent the day photocopying, distributing, and explaining work packages for our students while ourselves wondering how we would negotiate the transition to distance learning and the new expectation to work – teach – from home. After these two weeks, a continuation of the lockdown was announced and Ontario educators were tasked with connecting with students and families procedure to gauge student’s communications technology readiness, to monitor their social-emotional needs arising from the lockdown and to explain that basic emergency learning was now in place.

At the same time, educators were told to create virtual learning environments (VLEs) to deliver this emergency instruction. Hours of instruction guidelines were provided by the Ontario Ministry of Education (April 2020): for JK-Grade 6, no more than six hours per week; for Grades 7–8, no more than eleven hours per week and for Grades 9–12, no more than three hours per course per week. During this time, normal attendance procedures were paused and replaced with anecdotal teacher monitoring and the mode and delivery of teaching was not standardized via a specific virtual platform. Guidance from Ontario teacher unions was to avoid videoconferencing and webcasting (OECTA, 2020 ) to maintain the privacy of both teacher and student.

The unprecedented scope and unforeseen turmoil caused by the COVID-19 crisis resulted in a delayed and mixed response for how educators could meet the daily social-emotional needs of their students who were now being taught in ways that were not conducive to learning such as having no face-to-face communication. Part of this mixed response included the ministry directive that students’ marks and grades would be frozen to the date prior to the initial closure (Miller, 2020 ), though opportunities for improvement would be provided. While the intentions of policymakers were rooted in compassion and wishing not to amplify any feelings of stress, anxiety, or hardship already being caused by the pandemic, it did not serve to inspire students to be continually engaged in their educational community or to feel wholly supported by said community. Overwhelmingly, students in both of our schools stopped engaging in course content; knowing that their grades could not change truly impacted their motivation, diligence, and general commitment to learning. When this disconnect occurred, teachers were advised to continue to call the student and encourage them to login to their VLEs, but this consistent check-in by four teachers at least once a week to each family began to feel at best like nagging and, at worst, an intrusion. This disconnect had another negative effect; now that students were not explicitly required to complete work for school, businesses were free to demand that they work throughout the day. Many students shared that they had taken on full-time hours at their part-time jobs. Had the student–teacher-classroom relationship continued throughout the closure somehow through routine, synchronous, live-video or live-voice communication, perhaps students and families would have felt a social and emotional pull to preserve their work habits and would have benefitted from helping to maintain the class community.

Essentially, our students went from daily, face-to-face interaction that built relationships and promoted positive social-emotional skills to the bare minimum interaction of a posting or an email a few times a week. Compounding this growing detachment was the social isolation that being in lockdown necessitated both from friends and family members who were at work. Students were at home, cut off from teachers, school, and friends, alone with the computer screens.

When schools reopened toward the end of September 2020, protocols and safety standards had been improved and implemented in ways that would encourage some families to opt for in-person learning while for others, the risk was still too great. For these families, school boards in Ontario created whole virtual schools or adopted a form of hybrid learning that would accommodate student from Junior Kindergarten through to Grade 12.

Our personal experience of in-person learning after the initial lockdown was atypical: though there was slight excitement the first day, this waned quickly and by the end of the week students were behaving very differently than they had been in March. Classes were silent; students were not speaking to each other, and group discussion or student–teacher conferences were very difficult to craft and nurture. In cases where students were priorly familiar with one another and the teacher, as was the case in one author’s classroom, students remained quiet, distanced, and generally withdrawn. These personal observations were similarly attested by several colleagues. For one author who had been assigned to the new virtual school, similar observations were made. Here, students from all over the city were being placed in VLEs together, disconnected from their home schools. Every attempt was made to create positive community bonds but still, save a handful of eager students, online classrooms were quiet both auditorily and in the chat box.

Unfortunately, COVID-19 continued to spread rapidly through the province and by the winter of 2021, the threat of the new, more infectious variants of COVID-19 forced the hand of the Ontario Ministry of Education to close schools once again for the remainder of the school year. The most marked difference in this lockdown is the continuation of the collection of attendance – students are at least driven to login to their VLEs to have their attendance recorded to avoid the consequences of truancy. Many students have shared with the authors that this second lockdown had evaporated their hopes of any social-emotional normalcy for their educational experience. They have attested that they are tired of feeling alone, overly challenged at the thought of having to continue their learning experiences solely through their computer screen.

2 The path to inquiry

In the summer of 2020, The Hospital for Sick Children ( 2019 ) released a report that detailed how online learning and increased screen time could result in an increase of negative mental health effects. In addition, the prevailing advice of Public Health Ontario (2015) for the total number of hours of screen-time children and youth should be engaged in had not moved from the recommended “no more than 2 hours per day”. Instead of heeding the warning and advice of these public institutions, the Ontario Ministry of Education Policy/Program Memorandum No. 164 ( 2020 ) defining synchronous learning as “using text, video, or voice communication” in real time, set in place the following expectations for daily synchronous learning: 180 min for Kindergarten and 225 min for Grades 1–12.

Our desire to study the mental health effects of online learning takes its root in the experience of students and their guardians who have directly voiced difficulty with engagement in online schooling. Specifically, our students shared that their experiences in courses facilitated by teachers who did not utilize real-time video for the delivery of their synchronous lessons were difficult, hard to manage, and at times felt alienating.

Certainly, the few noted anecdotes above cannot constitute a direct cause for this review. Nonetheless, it is the view of the authors that such comments appearing from within the unique context of the mandated and sweeping switch to online learning warrant an investigation of what research might exist to establish acceptable standards of VLE implementation. In the case of the COVID-19 context, it must be understood that the changes to educational architectures were motivated by the physical requirement of stopping the spread of disease rather than what research has shown to be the best mode of education for the learner and their mental health needs. Further, even within the confines of mass VLE implementation, deeming the use of real-time video as strictly optional instead of prescribing a definitive modality for engaging in best practice displays a gap in an understanding of VLE significance. Further still, as online learning continues to be a the only viable option for many families, educators in Ontario must ensure that the creation of a VLE and the curriculum delivered through it is guided by the Ontario College of Teachers (OCT) Ethical Standards of Care , where it is understood that Ontario Teachers ought to avoid practices that may not support the welfare of all students placed in their care.

The Ontario College of Teachers ( 2020 ) defines the ethical standard of care as follows: “Members express their commitment to students' well-being and learning through positive influence, professional judgment and empathy in practice.” Further, the Ontario Education Act, 1990 Sect. 264(1c) clarifies that part of a teacher’s duty is to impress the “highest regard for…humanity…and all other virtues” to their students. In addition to the duty of a Principal to provide “assiduous attention to the health and comfort of the pupils” (Ontario Education Act, 1990 , Sect. 265(1j)) they must do so in such a way that they set the standard for the teacher. Without daily, live interaction with students, how is it possible to wholly meet these expectations?

Interestingly, the concept of all persons having to remain at home during the new COVID-19 online education paradigm seemed to create a vague understanding of whether it was even possible for the professional liability of care to take place. In consideration of the area of attendance, it must be understood that the traditional taking of attendance has a layered purpose: to ensure the “safe arrival” of students, to give proof of student attendance for funding allocation, and to link a student to the legal liability of a teacher as well as to allow teachers to exercise interventions for students who are truant. In the first stage of lockdown, teachers were asked to not take attendance and so were unable to ensure regular student contact. In this way, it became much more difficult to distinguish which students were not able to keep up with their studies and who required appropriate interventions for their learning, development, and wellness.

Tronick et al. ( 1978 ) showed that when the face-to-face interaction between infants and their mothers becomes distorted in such a way whereby the mother is unresponsive and still, “infants reacted with intense wariness and eventual withdrawal” (p. 1). They concluded that it was vital to have “interactional reciprocity” (Tronick et al., 1978 , p. 1) to learn how to regulate their own emotional reactions. In their meta-analysis of this Still-Face Paradigm, Mesman et al. ( 2009 ) found that not only had the paradigm been consistent through multiple studies in infants, but its negative effects could also be found in both youth and adults. Experiments with adults resulted in “quite severe disrupting effects on social interactions, making people angry, confused, or upset…[where] [t]he perceived necessity for following the ground rules of social interactions is likely to stem from the evolutionary roots of human social life” (Mesman et al., 2009 , p. 156). These results would suggest that part of the educator’s duty of care to a student is to maintain this quintessentially human behaviour of consistent face-to-face interaction. Without this fundamental interaction, it seems that humans are unable to properly regulate their emotional expression. Certainly social-emotional learning and the formation and maintenance of positive relationships with others is a core part of the care mandate of teachers.

3 Purpose of scoping review

While it could be surmised that the guiding education authorities’ combined lack of clarity in declaring best practice directives and providing systems of care contributed to negative student experiences, the notion of how these organizational failures might have impacted student mental health must be drawn into focus. Indeed, the most notable shift in education has been toward the use of the online classroom and it is foreseeable that the continuation of this paradigm will be unavoidable. However, with the ambiguous messaging that surrounds that which defines quality implementation of a VLE, there lies a discernible gap in gauging the importance of teachers using live video in the delivery of synchronous instruction and if significant mental health implications are present in the decision to do so.

The purpose of our scoping review is to isolate and investigate the existing data and research that identifies if the real-time visual presence of a teacher in a VLE is a significant factor in a student’s ability to maintain good mental health. Such research might reconcile the void of definitive directives educational authorities have offered concerning teachers’ utilization of real-time video in a VLE and, more importantly, provide students with a higher standard of care while enduring the context of increased vulnerability the pandemic has introduced.

In short, this scoping review aims to answer two questions:

Does the synchronous face-to-face and interactive presence of a teacher in a VLE contribute positively to student learning and mental health and well-being?

What are the characteristics of a VLE that meets the social-emotional care requirements and needs of the student?

4 Method and parameters

This scoping review works to capture the current “size and location of the literature” (Anderson et al., 2008 , p.7) to better define the state of understanding of our research questions. Further, it is our hope that this scoping review might work to ascertain common threads among the research and note any gaps to inspire further inquiry in the topic. All of the material will be charted so it can be viewed in tandem for the above purposes.

4.1 Boundaries of the review

Our search was limited to English-only peer-reviewed research literature that investigated the efficacy of online teaching and VLEs for educating the whole student. More specifically, we completed a search of all research articles published between 1 January 2010 and 31 January 2021 (an 11-year period). The rationale for focusing on this span of just over a decade was to draw our attention to the most recent research evidence related to our question. This date range also reflects the time span in which we had secured permanent work as educators; tracing the development and evolution of online learning since that point is of great personal interest.

Our search utilized the following terms: virtual learning environment (in all text) OR online instruction (in all text) AND mental health (in all text). These search terms were selected as they provided the widest catchment for the screening process required for this scoping review; in particular, the search term virtual learning environment was selected to identify research that was specific to an interactive context of online learning, as opposed to, for instance, online learning structured as a correspondence course. Databases that we utilized include ProQuest, ProQuest/ERIC, APA PsychINFO, and SAGE Journals Online because together they encompass a comprehensive and diverse catalogue of education-related literature as well as research regarding the psychological aspects of education. These database searches occurred between January 31, 2021, and February 7, 2021.

4.1.1 Special considerations of the topic search: defining a student

Ideally, the search terms could have been narrowed to refer to students specifically in a high school setting, using common platforms such as Google Classroom, but these terms did not yield any results. In an effort to compile the most useful sources for our research, a broadening rather than narrowing approach to our search terms was employed as it was discovered that limited resources existed in the field we were exploring. Considering this, the limits we set required special considerations of demarcation. For instance, many sources pertaining to the student mental health in a VLE context dealt exclusively with nursing students (e.g., Shea & Rovera, 2021 ). These studies offered excellent data sets but were omitted because the data could not be understood as congruous with a universalized definition of what constitutes a student. That is, the specialized nature of these studies involved students who had the responsibility of dealing with patient care, using VLEs to interact with their patients. In this way, the nursing paradigm muddied the demarcation between the VLE experience of a student and that of an authority.

4.2 Inclusion criteria

In initially assessing and screening the results of the database searches, we ventured to remove sources that did not pertain to the contexts of VLEs and the student’s experience of those VLEs. Here, a primary list of 268 articles was redacted to a sum of 63 articles via an evaluation on the content of the title and abstract of each article. The inclusion of the remaining 63 articles was based on their titles and abstracts having expressed: (1) the use of an online modality that would implicitly involve the visual presence of an educator, and (2) the interest in the contexts of the learners’ experiences being a result of the instruction provided. Sources that were concerned with the experience of post-secondary students were only included if they involved Year 1 students as part of their purview. This limit was imposed on the research as we perceived this domain of data as relevant to the prospect of constructing an information-set surrounding the transitional stage of Grade 12 into post-secondary. Further, this inclusion might allow for a greater basis of insight into the still emerging reality of widespread online education. Notable exclusions from the research that might have helped develop such a picture were many articles that focused on the VLE experiences of graduate and post-graduate level students (e.g., Chugani & Houtrow, 2020 ; Gardner, 2020 ; Shawaqfeh et al., 2020 ) and articles concerned with the mental health of educators who utilized VLEs (e.g., Watermeyer et al., 2021 ; Rowe et al., 2020 ; Ault et al., 2020 ; Alkarani & Thobaity, 2020 ; Schlesselman, 2020 ). The subsequent 63 articles were read in their entirety to determine whether their content would be appropriate for this scoping review. This stage of assessment resulted in the inclusion of 38 articles.

4.3 Manual inclusions

Additionally, a reference check of all 38 articles yielded the inclusion of a meta-analysis authored by Cavanaugh et al. ( 2004 ) we deemed relevant insofar as it provided an overview of the effects of online learning in a strictly academic sense allowing a possible comparison between achievement and positive mental health. A manual search conducted prior to the exercise of prescribed database search-terms warranted the inclusion of one other meta-analysis authored by Mesman et al. ( 2009 ) which is noted in the introduction section of this scoping review. The specific data and information presented in the work of Mesman et al. ( 2009 ) serves as a correlational tool in observing the trends apparent in the included articles. As a final article addition, a second manual journal search was conducted where two studies were found to include data that encompassed student perceptions of their experiences with VLEs, thus, resulting in a total sum of 42 articles to be included in this scoping review. The total review process is noted visually in Fig.  1 .

Review process and results

5 Summary of research findings

The purpose of this scoping review is to isolate and investigate the existing data and research that identifies if the real-time visual presence of a teacher in a virtual learning environment (VLE) is a significant factor in a student’s ability to maintain good mental health. Overall, our research has shown that authentic, high quality VLEs are ones that have as their primary focus the communication between students and their teachers and between students and their peers. This communication is best generated through synchronous connections where there exists the ability to convey the student’s immediate needs in real-time. The demands placed upon an individual educator to facilitate an online education for students, inclusive of the creation and maintenance of an effectual and engaging VLE often serving as a proxy to a face-to-face (F2F) base-school, “require[s] extensive time commitments” (Wingo et al., 2016 , p. 437) which are far beyond the standard workload formulas calculated by many institutions (Wingo et al., 2016 ). Indeed, the complexity of this task while also providing sufficient social-emotional services for students and families is not fully understood in terms of student and teacher equity. Our research results and discussion will outline how a team approach that brings together teachers, students, administration, counsellors, mental health support staff, instructional designers, and ICT specialists is necessary to create a genuinely enriching VLE where both learning and social-emotional needs can be met.

Summaries of the 42 articles pertaining to this research are provided in Table 1 . In addition to information related to authors, years of publication, countries (i.e., where the research was conducted), participant profiles, intervention programs and timelines (where available) and data sources, we have also summarised the researchers’ aims, research designs, findings, and conclusions.

Our research provided studies that had been conducted from nearly every continent of Earth, save South America and Antarctica. Notably, many of the articles were completed by researchers in the United States ( n  = 16). Fifteen were completed by researchers in Europe (Belgium, Cyprus, Georgia, Germany, Greece, Poland, Portugal, Slovenia, The Netherlands, Turkey, Ukraine, and the United Kingdom), and six were completed by researchers in Asia (India, South Korea, Taiwan, and Vietnam). The remaining research was conducted in Australia, Canada, Jordan, and Kenya. The research designs employed by the various researchers in our summary are quite varied and sometimes particularly nuanced and range from case study to meta-analysis. However, a preference for experimental, quasi-experimental, qualitative analysis, and mixed-method correlational analysis did emerge. Overall, few articles had a singular focus, which can be attributed to the expansive field of VLEs and their many intricate pieces. Some central concepts were highlighted, however, in the research: fifteen observed the effectiveness, benefits or challenges of some aspect of VLE (i.e., using virtual reality simulations or synchronous video); ten investigated student readiness, either on a social-emotional basis, technology know-how, or academic; six focused on the mental health of the learners in VLEs; six gathered data on the experiences and perceptions of either students, students’ families, or faculty, and four researched the current infrastructure and available policies for online learning. The participant profiles of our research were also quite varied, identifying elementary and secondary school-aged children, both undergraduate and graduate students, school faculty, and student family members, sometimes all within the same study.

Our research results have been organized in Table 1 below. It has been constructed in such a manner whereby an informational narrative that reflects the essential themes found within the research can be revealed.

6 Results and discussion of themes

The articles included in Table 1 represent the most current and relevant research in considering the embedded inquiry of this scoping review which involves uncovering the nature, implications, and best iterations of practice within VLE contexts. In our reading and review of the data therein, the themes of insufficient data surrounding VLEs, VLE benefits, the challenge of VLE readiness, and that which constitutes the ideal VLE emerged as pivotal. The objective of this section is to elucidate these themes, thereby, providing a modest basis for recommendations regarding VLE implementations and, perhaps, a view to offer directionality for future research.

6.1 Insufficient data

A key note thread found within many of articles was the self-admission of insufficient data. This theme of insufficient data is expressed in varying capacities that range from claims of there being a limited or even non-existent body of research, to more systemic causes for the insufficiencies. While the lack of data is often presented as a cautionary device for the demarcation of limits to implementation outside the context of the studies and provide exhortation for further research to be conducted, the admissions of insufficient data also point to the novel nature of the area of inquiry in question. Kumar and Owston (2015) begin their study on e-learning accessibility by stating that their field of inquiry had “not been explored, nor have methods to generate data” (p. 264) expressing that there is “a dearth of studies'' (p. 268) in the literature, and concluding that “[c]ontinued work in the area of developing methods to evaluate e-learning accessibility is thus urgently needed” (p. 280). Archambault et al. ( 2013 ) also identified their research scope of basic virtual school policies as being novel in nature, having no representation in the existing literature. Many researchers make note of the existing data as being too insufficient to draw more universal conclusions (Barbour & LaBronte, 2019 ; Cavanaugh et al., 2004 ; Engelbertink et al., 2020 ; Gillis & Krull, 2020 ; Ho et al., 2014; Jena, 2016 ; Zhu & van Winkel, 2016 ). In addition to this paucity of research, the attrition of study participants is noted as being a barrier to gathering full data sets (Manthey et al., 2016 ).

Some systemic issues which led to shortages in the available data are noted in Johnston et al. ( 2014 ) where school districts are slow to institute policy. Cavanaugh et al. ( 2004 ) mentioned a similar dynamic in considering that common goals are needed in policy making to identify the effectiveness of an intervention and policy makers and evaluators are exhorted to work together in partnership to ameliorate this. A further systemic barrier to data production that is noted is the problem of implementation of programming without conducting research (Cavanaugh et al., 2004 ).

6.2 Benefits of VLEs

In response to our first research question regarding the benefits of a wholly synchronous VLE experience, the research is generally favourable toward academic achievement with some degree of attestation to its social-emotional benefits. The benefits to VLEs and their implementation are assumed among most of our research in how they can be potential vehicles delivering some form of meaningful intervention or program within a given context. Further, some of the articles underline fundamental goods that can be uniquely exploited via VLEs. Driscoll et al. ( 2012 ) cites VLEs as an opportunity to better promote a constructivist framework for learning in saying that it inherently “creates a structural impetus for this style of learning that is not automatically present in F2F classrooms” (p. 314). Cavanaugh et al. ( 2004 ) provides multiple examples of how the institutional advantages of virtual schools “represent the best hope for bringing high school reform quickly to large numbers of students” (p. 22). Building upon the pervasive benefits to VLEs as a concept, Roblek et al. ( 2019 ) frame VLE dynamics as an essential component of human advancement where “social relations will be formed through the building of collective intelligence” (p. 96). Similarly, VLEs and their relation to ICT literacy as a global objective is observed throughout the research (Blayone et al., 2018 ; Cavanaugh et al., 2004 ; Crea & Sparnon, 2017 ; Davies, 2014 ; Gibson & Smith, 2018 ; Huang et al., 2011 ; Hursen, 2019 ; Jena, 2016 ; Mallya et al., 2019 ).

The strengths of specifically synchronous VLEs emerge in the research with highlighting synchronous learning as an essential component to student engagement with technology, peers, and educators. Concerning technology fluency, even in a blended learning context, synchronous VLEs offered a unique opportunity to implement technology in a meaningful way (Ho et al., 2016 ). Using a device in a synchronous context meant that students felt more engaged with material, subsequently feeling more confident with presenting work using technology, and students enjoyed being able to revisit an interactive lesson digitally after the synchronous session was over (Davies, 2014 ; Driscoll et al., 2012 ; Kumar & Owston, 2016 ). In terms of supporting engagement among classmates, synchronous learning was seen to offer increased avenues for peer-to-peer learning while allowing for teacher involvement throughout, thus increasing effectiveness (Crea & Sparnon, 2017 ; Johnston et al, 2014 ). Synchronous VLEs that include video also offer opportunities to be present to a class setting in a way that attends to learning retention, academic engagement, resiliency, and self-regulation (Archambault et al., 2013 ; Driscoll et al., 2012 ). When VLEs employ best-possible real-time communication, education processes can be more active, constructive, cooperative, and more attentive to a student’s meta-cognitive abilities than the traditional classroom (Cavanaugh et al., 2004 ). These latter points concerning real-time visual instruction potentially align with a foundational dynamic noted by Mesman et al. ( 2009 ) where it is stated that an “infant needs an external regulator to achieve optimal arousal levels and will show disorganization of emotion and behaviour when the regulator is absent or non-optimal” (p.122). Such a relationship becomes apparent in the work of.

Baker et al. ( 2019 ) which observed quiz results decrease among those students whose instructor withdrew communication and synchronous availability after originally being quite attendant to their needs and in the work of Engelbertink et al. ( 2020 ) where student motivation dropped significantly when the teacher no longer demonstrated an interest in the student’s homework. Throughout the research, it is evident that student engagement and achievement is well-supported in a synchronous VLE.

6.3 The barrier to a VLE: the challenge of readiness

Across all our research, it became clear that one of the primary factors curtailing the effectiveness of any VLE or LMS was the various states of readiness of the institution, the teacher, and the student.

At an institutional level it can be said that most schools are not equipped to create VLEs where students can thrive, even those schools that are virtual by design. The infrastructure required to create a holistic learning experience for the student, and one that embodies fair and equitable working conditions for the online educator, requires a considerable investiture of human resources and technological tools (Archambault et al., 2013 ; Cairns et al., 2020 ; Jones, 2015 ). Many LMSs that institutions use for online learning are bulky and inefficient (Gillis & Krull, 2020 ; Jones, 2015 ; Kumar & Owston, 2016 ; Lee et al., 2016 ) which can lead to their being used as places where information is simply disseminated, rather than genuine VLEs where the design and curriculum content can come together to connect students with each other for interaction and collaboration (Jones, 2015 ; Stone, 2019 ). Elementary schools, for instance, can be said to provide many opportunities for families to increase their informal social capital and high schools, colleges and universities often provide a student with guidance and counseling services not easily accessible elsewhere. In moving to online learning, these institutions must not forget their “organizational brokerage” (Domina et al., 2021 , p. 4) in facilitating and maintaining these social connections lest their students suffer in isolation (Crea & Sparnon, 2017 ). Ultimately, the VLE experience begins with the institution; if there is no commitment to ensuring the use of a high-quality LMS and no focus on securing and maintaining the human resource social supports that students and families have come to rely on the school to provide, then the mental health and academic achievement of its students can deteriorate (Cairns et al., 2020 ; Cavanaugh et al., 2004 ; Domina et al., 2021 ; Gillis & Krull, 2020 ; Jones, 2015 ; Lee & Oh, 2017 ; Merlin-Knoblich et al., 2019 ; Rogowska et al., 2020 ; Stone, 2019 ; Xavier et al., 2020 ; Zhu & van Winkel, 2016 ).

As Blayone et al. ( 2018 ) points out, vital to the VLE experience is “high quality activity design, strong environmental and motivational supports, and competent online facilitators” (p.15). Teacher readiness for both the technological scope of VLEs and for the new expectations that they are the sole social-emotional support for students and families (at the very least a proxy to such supports) is generally low. Training is essential for educators who are navigating new technologies and creating resources that provide meaningful opportunities for knowledge construction, reflection, and practice (Davies, 2014 ; Gibson & Smith, 2018 ). Teachers must also be taught how to “adjust and find their own rhythm, providing sufficient presence while avoiding feeling perpetually ‘on call’” (Jones, 2015 , p. 227). Teachers lacked access to suitable training and felt ill-prepared to offer and provide to students with special needs or disabilities the appropriate accommodations within the VLE (Kent et al., 2018 ). Substantial professional development is needed to ensure that teachers know how to provide social opportunities in the VLE that encourages group work, formal and informal interactions, and peer-to-peer cooperative learning (Cavanaugh et al., 2004 ; Johnston et al., 2014 ; Zhu & Van Winkel, 2016 ). Cultivating this social-emotional component is an essential task of the online educator; when a student can trust their teacher and their classmates, their self-efficacy and motivation increases and generally so does their performance and progress (Johnston et al., 2014 ). To accomplish this, institutions must increase their efforts in training and supporting their faculty to be ready for online instruction (Crea & Sparnon, 2017 ).

Jena ( 2016 ) defines student learning readiness as “the body of skills needed by learners to learn” (p. 950). This body of skills and aptitudes includes, but is not limited to, motivation, self-regulation, perceived usefulness, confidence with using various technology, attitude, self-efficacy, computational abilities, communication skills, and research and critical thinking competence (Baker et al., 2019 ; Blayone et al., 2018 ; Du et al., 2019 ; Hursen, 2019 ; Johnston et al., 2014 ; Jones, 2015 ; Mallya et al., 2019 ). Beyond these attributes of learning readiness is also a strong necessity for a certain level of social-emotional maturity, most especially if the online learning was a result of the COVID-19 pandemic or of illness (i.e., not a free choice). Soft qualities such as resilience, flexibility, and positivity (Lee & Oh, 2017 ) made it more possible for students to survive the transition from the routine and collaboration of a physical classroom to the more solitary and independent learning space of the VLE (Crea & Sparnon, 2017 ; Gibson & Smith, 2018 ; Jena, 2016 ). In addition to these crucial factors, is the technology-readiness of students. Students may not have access to their own personal device to do their schoolwork, and if they do, there is no guarantee that it is a device equipped with the sufficient technological specifications to handle the resource heavy online tools or that the student has access to high-speed internet to allow full and equal participation in the lesson and VLE (Domina et al., 2021 ; Gillis & Krull, 2020 ; Hursen, 2019 ). It cannot be assumed that because students use technology at very high rates for personal relationships and entertainment that they can directly transfer those skills to the sophisticated and critical digital literacy necessary and conducive to learning in a VLE (Blayone et al., 2018 ; Roblek et al., 2019 ). Indeed, the various online tools that are familiar to institutions and educators are rarely in the purview of students, though when the need arises, students do want to be taught how to use the many programs and LMSs available to them effectively (Stone, 2019 ) and thus system readiness, student readiness, student inclusion, student achievement and teacher readiness are inseparable (Huang et al., 2011 ; Kumar & Owston, 2016 ; Pryjmachuk et al., 2012 ; Yilmaz, 2019 ).

6.4 The ideal VLE

Among the reviewed articles, the answer to our second research question concerning the criteria of an ideal VLE emerged. VLEs which supported students both academically and emotionally and whereby online educators were engaged and motivated were highly organized and inventive, and if given that no barriers of readiness existed, could be implemented in every school system willing to pivot to this necessary focus. Firstly, policies and procedures that focus on the progress and social-emotional needs of the student must be in place (Archambault et al., 2013 ). This can only be achieved if a full set of human resources such as guidance teachers, attendance officers, counsellors and special education resource teachers are available both on a central campus and online (Johnston et al., 2014 ) offering “inclusion, communication, connection with others and proactive institutional support” (Stone, 2019 , p. 7) by way of a school-home mentorship model (Barbour & LaBonte, 2019 ). In this way, the student’s isolation is lessened and, united with the educational team, the VLE teacher can focus on lending their subject and pedagogical expertise to their students (Driscoll et al., 2012 ; Du et al., 2019 ; Engelbertink et al., 2020 ; Wingo et al., 2016 ; Zhu & van Winkel, 2016 ). Secondly, the VLE must be easy to use, accessible, flexible, and innovative. Institutions must select uncomplicated LMSs for teachers to use to deliver their program. The expectations of use must also be communicated to all faculty to ensure a seamless experience for students (Jones, 2015 ). As well, in either a synchronous VLE or BLM, having easy access to recorded lessons is crucial, especially for students with disabilities or who are still learning the language (Davies, 2014 ; Dommett et al., 2019 ; Kumar & Owston, 2016 ). Investment in innovative tools and technologies is necessary to keep the VLE from becoming stagnant for students and, depending on the technology, can promote healthy, rich, and meaningful student interactions (Du et al., 2019 ). There is promising research in the use of tools such as AR, VR, 3DVR and 3DVE to create experiences and spaces that allow students to attend to one another virtually. These tools help to cultivate positive relationships, academic and personal confidence, and good mental health (Huang et al., 2019 ; Lan et al., 2018 ; Papanastasiou et al., 2019 ; Stone, 2019 ). Thirdly, there must be, at best, a live-video synchronous component to the VLE, or at minimum, the availability of synchronous office-hours (Stone, 2019 ; Wingo et al., 2016 ; Zeren, 2015 ; Zhu & van Winkel, 2016 ). When students and teachers were engaged face-to-face, body language and tone could be better understood and relationship markers such as trust and care could be better perceived (Driscoll et al., 2012 ; Johnston et al., 2014 ; Wingo et al., 2016 ). Finally, the VLE must engage students in becoming digital citizens together. VLEs that provide opportunities for students to engage formally and informally enable students to increase their academic self-efficacy, increase their learning outcomes, and mitigate any mental health issues that may result from the perceived isolation of online learning (Driscoll et al., 2012 ; Du et al., 2019 ; Engelbertink et al., 2020 ; Johnston et al., 2014 ; Stone, 2019 ; Yilmaz, 2019 ; Zhu & van Winkel, 2016 ).

6.5 Discussion of gaps and limitations in the research and suggestions for further inquiry

The attempt to study any observable intersection of VLE implementation and student mental health presents unique logistical and philosophical queries that remain unquelled. Such wonderings involve the state of how participant numbers are determined, the founding modalities in which self-reported qualitative data is obtained, the rationale, or lack thereof, of why specific LMS platforms were used in the existing studies, and the generally perceived evolving nature of VLEs. Taken together, the various streams of inadequate information fret deeply and, perhaps, create quite significant gaps. In the following discussion of these gaps, we will humbly aim to make moderate suggestions for further inquiry that could enrich the current available research.

Concerning the limitations in obtaining meaningful participation, a key area that remained challenging among the research was ensuring that participant profiles were not assembled out of simply convenient contexts of implementation. Indeed, quality research is exhorted to communicate, as narrowly as possible, the contexts in which they are situated. However, our search yielded a number of studies that were isolated case studies (e.g., Archambault et al., 2013 ; Johnston et al., 2014 ; Jones, 2015 ; Kumar & Owston, 2016 ) or were relegated to being singularly quasi-experimental (e.g., Blayone et al., 2018 ; Davies, 2018; Driscoll et al., 2012 ; Ho et al., 2016 ; Huang et al., 2011 ; Lee et al., 2016 ) in nature due to the fact that their implementation was imposed upon pre-existing participant groupings – those who happened to be enrolled in the class that was chosen for intervention. In extension to this, adequate control conditions were not always apparent, especially those which considered many factors that were changed in the experience of intervention groups. That is all to say that the interventions themselves were multifaceted, and one could surmise a possible inability to distinguish which key facet or combination was pivotal in the intervention. This issue may be considered a specific function of the sheer complexity of studying VLE implementations themselves. It is further compounded in the noting of pre-existing intervention groupings as it is perhaps the result of simple pragmatism in observing VLE implementations where they are available to be observed. This point recognizes that VLEs require specific access to resources that may be limited, making widespread and universally approachable studies a challenge. Here, it is possible that an underlying dynamic exists in the research where actioning any opportunity for study, however limited, is better than conducting no study at all. In our view, further inquiry into VLE efficacy and its relation to the mental health of students, should endeavour to include randomized trials, whereby there is no observed previous relationship between the intervention group and the researcher.

Another limitation to this scoping review related to participant selection is the scale and size of many of the studies. Several studies combined the type of participant, blending the experiences of students, faculty, and education support staff, thus limiting a focus on the unique perspective of the student as the end-user (e.g., Crea & Sparnon, 2017 ; Dommett et al., 2019 ; Engelbertink et al., 2020 ; Johnston et al., 2014 ; Stone, 2019 ; Wingo et al., 2016 ). Additionally, some studies that reported findings concerning students directly were of an extremely small student sample size of thirty or less (e.g., Cairns et al., 2020 ; Dommett et al., 2019 ; Engebertink et al., 2020 ; Hursen, 2019 ; Johnston et al., 2014 ; Kumar & Owston, 2016 ; Lan et al., 2018 ; Wingo et al., 2016 ; Zeren, 2015 ; Zhu & van Winkel, 2016 ). We note this small sample size in order to frame the perceived usefulness of these studies in the Ontario education context noting that Ontario Regulation 484/20, s. 4(14.1) states that “the average size in a school year of a board's online learning classes shall not exceed 30”. It is our view that findings of studies with a less than thirty sample size should be interpreted cautiously, as the dynamics and pressures conspicuous in an average sized class cannot be accurately measured. For further inquiry, we would suggest research that included groups of whole divisions across multiple school boards allowing for parallel interpretation and consistency.

A further limitation of this scoping review is the lack of consistency in LMS research. In as many facets as teachers differ so too do the online VLE tools that may be utilized to deliver programming and the effects of each LMS’s nuances can be difficult to account for and isolate as non-contributing factors within the studies. Several studies looked specifically at the Blackboard LMS (e.g., Crea & Sparnon, 2017 ; Davies, 2014 ; Du et al., 2019 ; Engebertink et al., 2020 ; Kent et al., 2018 ; Lee et al., 2016 ; Pryjmachuk et al., 2012 ) noting that in most cases its use was pragmatically chosen as it was already in use by the hosting institution. As well, multiple studies looked at either outdated programs, such as Facebook and RSS feeds (e.g., Huang et al., 2011 ; Hursen, 2018; Yilmaz, 2019 ) or expensive and new technology, such as 3DVR and iPads (e.g., Davies, 2014 ; Huang et al., 2019 ; Lan et al., 2018 ), that would be quite financially out of reach for most Ontario school boards to implement in any widespread and equitable fashion. Overall, researchers instead focused on studying only the perceptions of online learning in general or one specific piece of the online learning experience (for instance, the posting of recorded lectures or an asynchronous discussion board section) without giving any precise attention to the LMS used to create the VLE. In fact, it can be seen that the largest gap in the research is ignoring the LMS as a true, unto itself environment. We find this to be crucial to our research focus of determining how the perceived humanity of the VLE affects the mental health of the student; after all, it is the space in which the student will be spending most of their learning hours.

Many of the studies relied upon qualitative data gathered via surveys during the intervention which required participants to self-report on their perceived well-being and mental health. This paradigm of understanding reads as akin to consumer-based research where one who is satisfied with a product is more likely to repeat consumption regardless of whether the product ultimately increases quality of life. Just as the terms pleasure and contentment are not interchangeable, in the context of the research, it remains unclear if a participant’s self-reporting on perceived levels of anxiety is congruent with clinical definitions of the terms. While studies which utilized this form of data collection are free of equivocation by way of maintaining qualifying language such as “perceived level of anxiety” and not simply state “anxiety”, the question of the results being meaningful still remains. A suggestion for further inquiry may entail the implementation of standardized data sources such as the PSS-10 , the CES-D , and the MBI-SS utilized by Lee and Oh ( 2017 ). Caution must be employed when developing questionnaires, interview questions, and surveys that provide opportunities to participants for open-ended self-reporting.

A final point on the limits of VLE research rests in the concern that LMSs may evolve at rates that do not allow for consistent implementation and ample research to be conducted in a timely manner. In an earlier section of this scoping review, we referenced the lack of research as a product of the field being relatively novel in nature. However, for several years now, LMSs have offered functionalities that extend into the realm of real-time collaboration that is inclusive of visual presence with teachers as well as being fully capable of allowing students fluid peer-to-peer real-time engagement. In considering this it is astonishing that our research showed such a vast range in how VLE operated in relation to the available technology. This has led us to wonder if the technology is available, why is it not being utilized and studied in a way that reflects its full capabilities? Aside from the concepts of readiness that have been earlier itemized and discussed, the level of investment that an institution is willing to make of a platform and the rate of making that LMS available with a fully optioned suite, becomes the pivoting element. Further, if a gradual investment model is employed, where not all features of the LMS are available at the onset, each time a new feature is doled out, it becomes a potential point of relearning and creates inefficiencies if there are not ample professional development opportunities for educators.

While determining our final research terms for this scoping review, we had initially searched for studies that were exclusively for the K-12 sphere and unsuspectingly this did not prove to be fruitful. We noted earlier that a peculiar number of studies had been published using nursing students as the study participants, reporting on their role as student and practitioner. We believe that the near exclusivity of undergraduate students in the research limited our ability to present a complete picture of the current state of VLEs for all students. In The Human Face of Mental Health and Mental Illness in Canada , the Government of Canada ( 2006 ) reported that “two-thirds (68.8%) of young adults aged 15–24 years with a mood or anxiety disorder reported that their symptoms had started before the age of 15” (p. 34). Boak et al. ( 2016 ) support these conclusions, showing in their report that “one-third (34%) of students indicate a moderate-to-serious level of psychological distress (symptoms of anxiety and depression)” (p. iv) and “one-in-eight (12%) students had serious thoughts about suicide in the past year” (p. iv), a statistic that has remained consistent over the past fifteen years of reporting. Given these unsettling statistics, we would have thought that the rich and varied K-12 arena would have been a sphere where there would be a surfeit of mental health research related to VLE utilization. Instead, our search results yielded studies that disproportionately represented participant groups outside of our desired range of inquiry; just six included the experiences of K-12 students, and in each of those studies, the students’ caregivers were the primary data source. K-12 students often spend more than half their waking hours in school environments and VLEs; it is notably unclear why most of the studies observed in this scoping review are unilaterally disinterested in exploring an identified area of need for mental health support. We believe it would be prudent to prioritize research on the mental health of K-12 students engaged in VLEs which Domina et al. ( 2021 ) has shown can be isolating, psychologically disruptive, and exhausting experiences.

7 Suggestions for educator practice

Though there is variety in the identified gaps detailed above, our research maintained a consistent thread that related to the criteria of the ideal VLE for both the success of the educator but also the well-being and dignity of the student. From this work, we endeavour to make a few moderate suggestions for online educators:

Where possible and where privacy concerns can be mitigated, conduct lessons and office hours using live videoconferencing. Whether the VLE is a secondary component in a BLM arrangement, or it is the primary mode of program delivery, maintaining a personal face-to-face connection is an essential component to a student’s feelings of connectedness and motivation.

Avoid viewing the VLE as merely a space to bank work packages and collect evaluations. Rather, aim to create a space where both formal and informal interactions can occur, synchronously and asynchronously. Many LMSs have the capability to incorporate a variety of third-party online teaching and learning tools to aid educators in creating a multi-modal experience for students.

Be vulnerable and take advantage of learning opportunities when they become available. It takes an enormous amount of energy and resources to run stimulating programs that speak honestly to curriculum content, allow for individual learning needs, and that are cognizant to the social-emotional well-being of students. The responsibilities and conditions of an online educator are well-primed for strain; be mindful of the added pressure and allow the professional development that is available to inform practice but not make hurried demands of that practice.

8 Conclusion

If educational research involves an ethical component, it would be incumbent on institutions to see that research reflects areas of need within communities. It is our hope that this scoping review might provide modest insight into the current state of research that concerns student mental health in VLE contexts, while casting light on the need for new research initiatives to be undertaken in the K-12 sphere. As it stands, there lies the strong possibility that K-12 students are experiencing VLE implementations that do not actively partake in the qualities of a VLE that soundly offer best practices, working to support the mental health needs of students. To build strong VLE’s for K-12 students, research campaigns ought to offer architectures that are universalized in their implementation and fundamentally repeatable. This requires a commitment beyond that of the researchers involved, but also a willingness of the institutions who serve the participants of such a sweeping study to abide by the research. Without such research, institutions which utilize VLEs can only continue on sometimes arbitrary perceptions of how best to serve student wellness. Persisting in the status quo as such leaves students vulnerable to practices that might institutionally under-serve them and have potential generational implications. Interestingly, one might argue that, without such research, institutions who offer VLEs might garner the ability to omit themselves of the direct responsibility to provide those qualities of VLEs that would be found to support mental health and exclude those qualities that are found to diminish mental health.

As a closing thought and to return to the experiential modus and inquiry of this review, we adjure future research to be guided by the question of how the student encounters their teacher within the VLE. Emmanuel Levinas, a philosopher who wrote extensively on the innate ethical experience that is garnered through face-to-face interaction, took a rare moment in his writing to offer insight on the dynamics of education. In Levinas’ Totality and Infinity, he notes that in being called to respond to the Other, “[teaching] designates an interior being that is capable of a relation with the exterior and does not take its own interiority for the totality of being” (Levinas, 1969, as cited in Zhao, 2016 , p. 324). Here, Levinas may appear to point to the disposition of the educator as one that offers the presence of self for the sake of the students’ being. This sentiment, taken along with the intriguing meta-analysis offered by Mesman et al. ( 2009 ) may do little to establish the “how” of education as conveyed through this inquiry, but certainly makes a tremendous stride in the realm of the “why” that institutions ought to work to expound among the current VLE modalities that they are imposing upon learning communities.

Data availability

Not Applicable.

Code availability

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The authors would like to thank Dr. Christopher Gilham for his comments and guidance on a previous version of this manuscript.

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Caprara, L., Caprara, C. Effects of virtual learning environments: A scoping review of literature. Educ Inf Technol 27 , 3683–3722 (2022). https://doi.org/10.1007/s10639-021-10768-w

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Making virtual learning engaging and interactive

David s. sweetman.

1 University of Michigan, Ann Arbor MI, USA

This article provides practical perspective and guidance for transitioning from in‐person to virtual learning. Student engagement is emphasized through providing synchronous and highly interactive virtual learning sessions. This approach not only improves student outcomes related to class, but also is related to strong student mental health. Technological considerations are explored surrounding how to select a video conferencing platform that will enable engagement. Key functionality recommended includes the ability to share audio and video of both instructor and students, virtual hand raising and other signaling capabilities, hosting of small group discussions within the larger class, concurrent chat capabilities, and the crowd‐sourcing of questions. The implications of this functionality from a learning perspective are discussed. Empathy and flexibility in accommodating diverse and evolving student scenarios is also discussed. The importance of setting norms and expectations provides a foundation for the class, both during class sessions as well as in providing a framework within which students conceptualize group work. This article ends by looking ahead at near‐term implications of teaching during a global pandemic.

1. INTRODUCTION

In a recent analysis of successful life science research exemplars, all of the most important characteristics identified by these exemplars related to the human dimension of research: relationships, passion, and resilience were the top three characteristics. 1 A key part of the learning experience of these exemplars began in the same place it does for all of us: in the classroom. Therefore, teaching the human dimension of work is an important part of our pedagogy. This is best made possible through interactive learning experiences. 2

As a faculty member at a large, resident‐based public university, I have always placed value on the importance of in‐person education. A key part of that value has been in the interaction. Interaction between students and professor, and interaction between students. This is true whether a small seminar class of 15, a large survey course of 150, or anywhere in‐between. Last spring as we began life with the COVID‐19 pandemic, I was suddenly forced from the familiarity of teaching those engaging classes in‐person to transitioning to an all remote interaction. That semester, I was teaching a 150 person introductory course on organizational leadership and a 30 person capstone seminar course on strategic change through human resource management. Both were taught in‐person for the first half of the semester before transitioning, and both were undergraduate courses, with the introductory course being largely sophomores and juniors while the seminar course was largely seniors. Additionally, at the end of the term, I virtually hosted a traditionally 3‐day in‐person summer program for over 250 high school students. Like so many peers around the world, I feverishly worked to transition those well‐planned in‐person learning interactions to a format where I found myself physically in a room by myself. It took roughly one hour for every hour of class time to thoughtfully plan and update materials to transition the course from in‐person to virtual formats. While this experience is not what I planned, nor what I thought I would have wanted, I went all‐in to make the best of the situation. As expressed in end‐of‐semester evaluations, a number of students actually found the virtual experience more engaging than in‐person class. What a pleasant surprise. I too had found unexpected positives in the transition to virtual interaction. In this article, I share my perspectives and experiences of what worked (and what didn’t) and end with specific recommendations for you to consider when moving forward with your own online teaching. This article focuses specifically on the experience of interpersonal interaction within the classroom, especially in fostering interactive dialog among learners. The present article does not address tools specific to STEM education, such as interactive anatomy tools, which are covered elsewhere in this special issue. 3

2. AGAINST COMMON WISDOM: SYNCHRONOUS LEARNING

The first major decision I faced: do I continue to offer the course at the same scheduled time and expect students to attend at that time (synchronous), or do I simply record lectures and structure assignments such that there is the flexibility of viewing and completing coursework when convenient (asynchronous)? Guidance and resources emerged and the common wisdom I largely saw was to go asynchronous—to video record lectures and perhaps offer chat board interaction via the institution’s Learning Management System. However, I chose not to do that. I chose to go the synchronous route, because the learning of the subject matter I teach is maximized through synchronous discussion. Students retain and apply materials better through the process of discussion. 4 When my university ran usage data on video conferencing a few weeks into the virtual transition, I in fact found my magnitude of adopting synchronous learning was among the highest in the university—one of the top five greatest volumes of usage of almost 13,000 users of the video conferencing platform at the university.

Additionally, as we consider the undergraduate residential college experience, it is largely defined by a regular sense of structure, and regular social interaction with peers. This has been well‐established in decades of meta‐analyses on achievement in higher education. 5 The cancelling of in‐person learning and extracurricular experiences served to take away both of those pillars of the educational experience. While our collective concerns and attention were largely toward the physical health of students, I was already considering the mental health impacts of an abrupt transition from the regular structure and interactions of college life to being relatively isolated with relatively little structure to individual schedules. Recent survey data has shown a 40% increase rates of mental health impairing academic performance when compared to pre‐pandemic levels. 6 This underscores the importance of efforts to keep students engaged and mentally healthy.

The argument in favor of synchronous learning is in some ways parallel to the argument for active learning and “flipping the classroom” in biomedical education. “Flipping the classroom” refers to providing pre‐reads and recorded lectures ahead of class time so that class time is focused on interactive pedagogy to solidify and synthesize student learning. Systematic review has found inconclusive results in “flipping the classroom.” 7 However, there is an important distinction to be made. Increased outcomes are not always associated with a “flipped” delivery per se, but rather through the active learning that is often enabled in this approach. 8 The considerations related to synchronous learning discussed in this paper relate to enabling an active learning approach, which may or may not include a “flipped classroom” instructional design. Additionally, synchronous classroom experience helps ensure appropriate pacing of the delivery of materials, rather than students binging multiple lectures in a short period of time. While some students may desire the autonomy of asynchronous learning, the benefits of active learning can best be realized by both undergraduate and graduate student populations through actively engaged synchronous learning experiences. 9

I’m glad I made the decision to maintain synchronous learning. Throughout the semester, I received unsolicited positive feedback from over 75% of students for that decision. This feedback took two main forms. First, from the perspective of educational experience, students had been worried. They had been worried they would “lose out” or otherwise have a “subpar” learning experience due to the transition. As the semester went on, this feedback became even more pointed in comparison with other classes they had that had gone asynchronous, and how the video recordings and asynchronous discussion boards simply weren’t providing them a meaningful level of engagement and learning. As an example of the feedback: “Thank you for not letting the class experience be lost just because we could no longer meet in‐person. I still learned so much meeting together on Zoom.” Synchronous class facilitated both engagement with the material as well as their peers to produce a high‐quality learning experience. 10

The second form of positive feedback from students on synchronous classes was very personal. I was moved by the number of students who told me my class was something that they looked forward to each week, to a much greater degree than when it was in‐person. Some went as far as to call it the highlight of each week and something that helped them to stay mentally healthy. This excitement for the class was grounded in it being one of the only regular times they had the opportunity for real, meaningful interaction with people outside of their immediate household.

To be clear, both in‐person and virtually, class is structured in a highly interactive form. I strive to limit my guidance through course materials to no more than 10 minutes at a time, and less than half of the total class time. The balance of class time is spent in large‐group discussion, small group breakout discussions, or other activities designed to apply and solidify the learning objectives for the day’s class. While I did not fundamentally change this interactive approach from in‐person to remote learning, I realize that perhaps student attention spans are shorter and more readily distractible when attending virtually rather than in‐person. As such, the focus on a highly interactive and engaging experience is even more important to the success of the virtual class setting.

3. TEACHING GLOBALLY ACROSS DIFFERENT TIME ZONES

A factor that could have limited my decision to teach synchronously was that I teach to a global audience. Students had returned to their homes and were now scattered across ten time zones throughout the world. Fortunately, the timing of the classes and the time differences largely worked. I teach in the Eastern time zone, and my only complaints came for my 10am class from a few students on the west coast of the United States, where class started at 7am for them. While perhaps not ideal for their learning experience, I held firm that this was still doable and expected.

On the other extreme, the other course I taught was an evening course that ended at 7pm, which meant 1am for a few students joining from Europe. While they were used to late‐nights anyways, their parents were not. So, while these students joined the class, their vocal contributions to discussions ended mid‐way through class once their parents went to bed for the night so as to not disturb them. As we look ahead to continued online teaching experiences, factoring in student time zones of residence should be a key component of scheduling synchronous learning experiences. We need to know time zones where students will be living and find a time that best accommodates the greatest number of learners.

4. TECHNOLOGY PLATFORM CONSIDERATIONS

I reviewed and experimented with a number of different video conferencing platforms for the transition to online education. Some of the largest platforms in the market include Adobe Connect, BlueJeans, Cisco WebEx, Google Hangouts, Microsoft Teams, and Zoom. Check with your institution’s information technology group, as many have purchased a site license for one or more products and integrated it with other student technology. One of the most important factors in deciding what platform to use is what platform(s) are licensed and supported by your institution, which also includes the benefit of student familiarity and integration with other tools, such as the Learning Management System. Additionally, as you consider which platform(s) to use, it is important to have clear requirements in mind of what you do, and don’t, need a virtual platform to do to achieve your pedagogical goals. For me, factors that were especially important were: ability to share video and audio, virtual hand raising, small group discussions, chat, crowd‐sourced questions. The software I ended up using to accomplish all of this was Zoom. This section is neither an advertisement for or against any particular video conferencing platform, but I use the example of Zoom as it is something that worked for me.

4.1. Audio and video

This is standard fare for any video conferencing platform. However, of specific interest to me was the ability to see many students all at once. When we teach in‐person, consciously or not, we will often “read the room” of student non‐verbal reactions to understand student engagement, the degree to which they understand materials, or their general energy level. We then adapt to the needs of the students to maximize their learning. Being able to see students during class was therefore an important piece for me of any technology tool.

While not related to the technology specifically, this also implies the expectation that students have their video turned on and shared so you and others in the class can see them. Meta‐analysis has shown this shared social presence to positively impact learning outcomes. 11 It is important to establish this standard expectation early in the semester. Otherwise, you run the risk of it becoming the habit of students to not turn on their video, and that habit is much harder to break later. That said, I do not recommend requiring video be turned on, but only strongly encouraging it. Depending on student circumstances, living environment, and Internet availability, not all students may be able to share video.

As for audio, to minimize distraction and maximize focus on learning, students should only turn on their audio when they are talking, and mute their audio when they are not. This avoids background noises and the annoying audio feedback that can sometimes plague video conferencing calls. I found I would need to occasionally remind a student to unmute themselves so the class can hear them, but students are largely used to the technology and self‐regulating their muting and unmuting.

4.2. Screen sharing

This is also standard fare for any video conferencing platform. The ability to share all or a portion of one’s screen can be used for presentations and other materials. Students have the flexibility to view both the presentation and instructor when the screen is shared. Additionally, sometimes sharing materials on screen and sometimes focusing on video sharing is a good way to provide variety for students during the presentation.

Instructors and students both have the option to share their screen. This is configurable in most video platforms so that you can choose to enable or disable it. However, I found it presented flexibility and another tool in our toolkit to use to achieve our learning goals. While student‐led presentations are perhaps an obvious example of where this was useful, a less‐obvious example is as a way to visually share output or summary from small group discussions, or for students to be able to showcase learning objects they have uncovered that other students may find useful.

4.3. Virtual hand raising

Again compared to the in‐person class experience, something as simple as raising one’s hand needs thoughtful translation to a digital context. While physically raising one’s hand in a video conference can work for smaller class sizes, it becomes more difficult to see that in larger classes, not to mention not all students can participate via video. The virtual platform needed to include the equivalent of hand raising, where students click a “raise hand” button and the instructor and peers see a visual indicator.

Unexpectedly, this is one of the areas I found worked out better virtually than the in‐person classroom. There were two reasons for this. First of all, there was no mistaking whether or not the person was raising their hand. In‐person, there can be the timid hand raise that barely looks like a hand raise, or even the adjusting of hair that can be mistaken for a hand raise. However, with the software, the indicator is clear: either the hand is raised or not. Additionally, the software keeps track of the order in which hands are raised and students were listed to me in that order. While hand raising was a key feature to facilitating large‐group discussion, a solution is also needed for small group discussion.

4.4. Small group discussions

This is similar to the idea of having small tables in a physical classroom and asking students to have a discussion at their small tables. This was perhaps the feature that made the most difference to the learning experience, as it is a learning tool I use regularly in the physical classroom, especially to benefit conceptual learning. 12 This functionality put students into separate groups, each of which was an independent discussion. Both myself and teaching team members could each then “visit” those individual discussion groups, much like walking around an in‐person classroom and talking with each of the groups. Students can also effectively “raise their hand” to signal if they’d like the attention of a member of the teaching team.

The small group discussions functionality also had great flexibility—I chose the number of students per group and could pre‐assign or randomly assign specific students to specific groups. Especially for my larger class of 150, this was even better than the physical classroom as I could readily configure differing discussion group sizes with differing amounts of people in a way that would have been impossible—or taken up huge amounts of time to logistically move students ‐ in a lecture hall of 150 students. For example, in the course of one 90 minute class session, I had students begin the class session by connecting in a small group, mimicking time for chit‐chat when students sit at the same table together before class begins. At two later points in the session, I had them in breakout groups of seven, each one of these groups having different members. Finally, I had a couple quick back‐to‐back small groups of three, each with different peers. I would have never tried to accomplish that many changes in the course of one in‐person class session.

Additionally, as each breakout room was independent, small group discussions did not have to deal with the background noise of other groups talking at the same time. However, with that great benefit came a great limitation as an instructor: it was not possible to “read the room” to decide whether to extend or reduce the amount of discussion time. When multiple small groups are talking concurrently in a large room, you can sense the level and intensity of discussion and whether to continue or cut short. However, this insight is not possible in the virtual small group discussions. As such, I had to be much more intentional in planning discussion content and the amount of time it would take.

When I started on this journey, I did not expect to use the chat function much, however, it filled an additive role to the overall experience. On a simple level, if I had a question that quickly elicited short answers from many people, chat was the best tool. For example, when I’m looking for many people to provide examples of a specific phenomenon. On a more complex level, it supplemented discussion and in some ways took the place of “sidebar conversations” students may have had one‐on‐one during a discussion, except now those conversations were with the entire class. For example, students would sometimes add sub‐points or personal examples while another student was talking in order to facilitate peer learning. 13 Additionally, after individual or group presentations by students, chat was a way peers could affirm each other’s presenting or ask follow‐up questions.

Chat can also be implemented privately with a given student. Chat made it easy for me to converse with students individually if needed during class. This was sometimes used for technical issues, such as helping a student who was having microphone difficulties. Related to learning, individual chat also provides an opportunity to address students individually. For example, if I noticed a student who looked puzzled on video, or made a discussion comment that showed they weren’t quite understanding the material, I could offer a personalized follow‐up.

4.6. Crowd‐sourced questions

I will routinely host guest speakers during the semester. When I do, I provide students the opportunity to ask questions of the guests. I’ve often had concerns with that format, wondering if the students who were speaking were asking questions representative of the class as a whole, or relatively more unique and one‐off to their personal interests. That concern has now disappeared for me in the virtual format. In this format, students have the ability to pose a question for fellow students to see it and then “vote up” questions that also interest them. Within a few short minutes of the start of the session, I quickly had a list of questions organized by the number of students who were interested in each question being asked. Students also experience an increased sense of engagement through working collaboratively to determine questions for guests. This functionality is something I plan to introduce back into my in‐person classes, as standalone functionality like this is also available through tools such as Slido.

While it could have been simple for me to then ask those questions of guest speakers, I took different approaches to more directly engage students and speakers. In the case of the smaller class of 30, I would invite the person who originally posed the vote‐up question to then ask the question to the speaker. In the case of the larger class of 150, I asked for a few volunteer student moderators who would take turns asking the questions on behalf of the larger class. In both these scenarios, I also made clear the expectation and norm that students could then ask follow‐up questions if so inclined based on the speaker’s response.

While I highlight the use of this feature as related to guest speakers, one can envision other uses. For example, to elicit questions about readings or the day’s lecture to help guide discussion, or to crowd‐source examples or idea generation related to a concept being studied. Additionally, these tools can be configured such that students can respond to other student questions. In this way, there are two related discussions occurring simultaneously in class: the one where students are answering the questions of peers, and the other where you are discussing the larger and more complex questions as a class.

4.7. Annotation

This is a functionality of Zoom I had initially disabled, but later came to appreciate its value. Annotation provides students the ability to write on slides you are presenting. Like it was for me, that may be an idea that initially causes you some hesitation. However, it proved to be another source of interaction that is not readily possible in the in‐person classroom. Specifically, Zoom annotation provides a tool called “Stamps” where students can place a predefined stamp (for example, “X”, “O”, etc) somewhere on the screen. Interactive slides can then be crafted where students would put their stamp somewhere on the screen. For example, providing a continuum and asking students to denote where they are in understanding a topic, or providing a handful of options on the screen and asking each student to select one by putting their stamp in the appropriate place. This enables sudents to quickly see where they stand relative to their peers for a given discussion prompt.

Table ​ Table1 1 summarizes all the key features of video conferencing platforms. All of these technology tools that have the potential to enhance the student virtual learning experience. However, we must also remember that not all students may have access to the underlying technology or environment needed to leverage these tools for the benefit of learning. Flexibility is needed as we consider implementation.

Key features of a video conferencing platforms

5. FLEXIBILITY AND EQUITABLE ACCESS

A strong argument against the synchronous virtual learning discussed in this article is that it could exclude students from participating due to socioeconomic or other reasons. The technology and Internet connectivity needed for video conferencing may not be something all students have access to in their homes, and could disadvantage those who do not have that access. As such, the approach needs to be flexible to ensure equitable access. For example, I set the expectation that sharing your video was strongly encouraged, but was not required. For any given class session, a small percentage of the class would not share video at all, and their grade was not adversely affected.

In the event that a student does not have the necessary technology tools to fully participate in virtual classes, what are the options? Fortunately, this is not an issue for us to solve individually as professors. Most institutions are grappling with this question and many have limited programs available to provide assistance to students who may need it. 14 Be aware of your institution’s approach, including knowing how to refer students who may benefit from such a program.

In addition to potential socioeconomic differences for students, we are living in an especially uncertain time. We need to be empathetic and flexible in providing asynchronous learning options when life events occur that make a particular synchronous learning experience not possible. For example, a student who is in quarantine, a student who is caring for infected relatives, or a student who has tested positive for COVID‐19 each require different forms of flexibility.

6. INVITED INTO STUDENT HOMES

As a professor, it is not expected or encouraged that we would go to a student’s home. In decades of teaching, I have never once set foot in a student’s home, nor has one of my students set foot in my home. Yet in a virtual environment, week after week we are invited into each other's homes during our shared learning experience. I believe this has led to a learning experience that feels much more personally connected for students. Photos, paintings, collections, bold colors, neutral colors, or even features like a back‐of‐the‐door basketball hoop, virtual background choice, or choice of headphones bring a level of seeing personality we don’t experience in the classroom. Kitchen tables, living rooms, bedrooms, basements, closets, garages, and even bathrooms and cars are all places students have joined from for my classes. With each of those, the key is to remind students that learning best occurs in a distraction‐free environment, and to ask them to thoughtfully select a location that will minimize the chance for distractions during class time. I respect that location is different for each student depending on their individual circumstances.

However, despite their best attempts at a distraction‐free location, the realities of their living environment sometimes came out during class: the 4‐year‐old niece excitedly barging into the room during a student’s final presentation, the thunderstorm that disrupted wireless Internet in the middle of class, or the loud party at the neighbor’s that could be clearly heard every time the student unmuted to speak. Sharing these virtual experiences week after week gave students a level of insight and appreciation into each other that seemed to help them build community in a way greater than a shared in‐person classroom experience. However, we also need to expect the unexpected and be ready to respond by facilitating classroom discussion with flexibility in focusing students on our learning objectives for the class.

7. MY TECHNOLOGY FOR TEACHING

This section provides just a brief mention of my own use of technology during class, and what I would recommend to others as a result. First, multiple computer monitors worked extremely well for me. This could be either one computer with two monitors, or two different computers. This enabled me to arrange video, chat, class list, slides, and all other materials in a way I could easily see them all. However, the more spread out your screen, the more important it is to remember where the video camera is on your device, and where you are looking relative to the camera.

It is relatively easy to look directly at students in an in‐person classroom. However, if you are looking at students on your screen, it will not appear to the students as though you are looking at them. Rather, know where the camera is on your device, and know students will experience you looking at them when you look at the camera. I found it highly unnatural to look directly at students in the virtual setting (which meant looking directly at the camera). However, as the weeks went on, it is something I became more comfortable in doing. Also, if you are working with others in the delivery of the class, be sure to have some form of communication, such as text message or group chat, that you can use to keep in contact with each other during a class session to exchange logistical and other time‐sensitive messages. This separate channel of communication ensures communication isn’t missed among student communication and keeps communication open in the event of technical issues where the connection to Zoom is lost by any member of the instructional team. Finally, private chat in Zoom is only between two individuals, so where a teaching team consists of three or more people, Zoom isn’t even an option.

The basic camera and microphone in most computers is generally sufficient for teaching. However, upgrading to a higher quality microphone can make you even clearer to hear and give your presence a more “professional” ambiance. Additionally, a higher quality camera can result in clearer and crisper video quality. Another consideration is lighting. Even the highest quality camera cannot compensate for poor lighting. Your face should be lit from the front to clearly show your facial features and expressions. Also, you should avoid having a direct source of light behind you (for example, don’t sit in front of a window), as it will cast you in shadow and make you hard to see. For advanced lighting configuration, consider a classic three point lighting set up which places one light in front of you on either side and a third light illuminating you from behind. The combined impact of the three lights is to minimize shadows and maximize a natural, well‐lit appearance. There are many online tutorial videos that elaborate on this technique. Figure ​ Figure1 1 shows the difference between standard and upgraded lighting and camera configurations, and Figure ​ Figure2 2 shows the entire setup from behind.

Camera and lighting these two pictures illustrate the difference between standard and upgraded camera and lighting configuration. Configuration (A) leverages standard video camera and lighting while (B) leverages upgraded camera and three point lighting configuration. Either is perfectly acceptable, but configuration (B) provides greater clarity and professionalism

Behind the scenes setup. Where the previous figure showed the student view, this is my view, including the setup of three point lighting.

Some video platforms also provide the option to use a virtual background, that is, replacing your natural video background with an image from your computer. While technology of yesteryear required advanced lighting and backgrounds (eg, a “green screen”), modern technology no longer has those requirements, making it accessible to both us and our students. However, just because it is accessible, doesn’t mean we need to use it. This is somewhat a matter of personal choice: what message or impression do you want to convey to your students? Does your natural background convey that, or would you rather have another background? Additionally, while the technology is much‐improved, virtual backgrounds can sometimes be distracting as the software is still limited in its accuracy of determining the boundary between someone’s face and the background, and may not display a portion of the face, or may display a portion of the background. Virtual backgrounds can also be used to provide variety or help augment the context of a given lecture (for example, I once used a factory background on a unit involving manufacturing). However, to be very clear: virtual background or not, the lighting concerns discussed in the previous paragraphs still apply. Even when using a virtual background, good lighting is still needed for your personal appearance.

The technology described in this section alone will not ensure a great online learning experience for students. Rather, these serve to help minimize the distraction technology could otherwise be so that students can focus on learning.

8. GROUP WORK

My courses involve a significant amount of group work. One of the most surprising aspects of the transition to online learning for me was the difficulty many students felt in completing group work when they weren’t together physically. Understanding how to work together for group work was easily the most frequent process‐related question I received about the transition from in‐person to online learning. I realized an important lesson in this: as I was adapting to our new online realities, the students were too. They needed greater guidance and structure around how to think about interacting with group members and completing assignments together virtually. 15

I recommend explicitly taking class time to discuss how to work effectively in groups virtually. Leverage the wisdom of the room and ask for students to share their own ideas of what has and hasn’t worked for them. Students need to develop self‐efficacy in effective online learning and group work behaviors. 16 The discussion should in some ways parallel themes in this article ‐ setting common expectations, having synchronous meetings for at least some of the work, and understanding and leveraging technical tools to enable their collaboration. In particular, on setting common expectations, expectations of communication are especially important. Each group needs to establish their expected shared methods of communicating, whether email, group text, or other platform, as well as a high level timeline of milestones and expectations in working together.

9. CULTURE AND EXPECTATIONS

As instructors, we set a classroom culture beginning with our first interaction with students. This culture includes what is “normal” or expected in our classroom, how students interact with the professor, how students interact with each other, and the entire classroom experience. We set this culture whether we are intentional about it or not. In a virtual environment, especially with the relative newness of the virtual environment for many of our students, it is important that we are intentional in setting the culture we desire, and in reinforcing that throughout the semester through explicit reminders as well as more implicit recognizing and encouraging of the types of behaviors we want to see. Organizations such as Quality Matters 17 provide comprehensive guidance for faculty and institutions in consideration what expectations to set and how in order to deliver high‐quality online learning.

At a foundational level, this includes expectations for the course as clearly outlined in the syllabus. It now includes the added layer of using all of the technology we’ve discussed, from having video turned on, to using the hand raise function, to what to write in chat, to how to use annotation, to how often to take breaks to avoid fatigue from looking too long at their computer screens. Additionally, setting expectations and building culture includes how students are expected to carry themselves in class and the amount of interaction and contribution. In my case, I begin this conversation with a simple question prompt: think of your best class experience ever, what made it such a great experience? Inevitably, this draws out answers related to how interactive the class is, and how connected students feel to their peers. Further, this helps draw out differences in learning styles and how to best accommodate those styles. 18 Exploring with students what they then want that interaction to look like in your classroom helps establish a great foundation for an expected culture of engagement. Table ​ Table2 2 summarizes overall recommendations for establishing an effective online learning environment.

Recommendations for effective online learning

10. LOOKING AHEAD

We are in a time of experimentation. There are many articles around the experiences of individuals and institutions, what has worked, and what has not worked in the transition to remote learning. Just as we each bring our experiences and personalities to our classrooms in a way that creates our unique brand of experience for students, so too in the online environment. However, where the online environment perhaps diverges dramatically from the in‐person environment is the volume and configurability of the tools available to us. A goal of this article was to provide thoughtful exposure to some of those key tools so that you can more fully consider how they may integrate, or not, with your own teaching philosophies and approach.

Until a COVID‐19 vaccine is developed and administered, we will likely continue to live with at least some forms of social distancing. As we consider this short‐term reality, envision the socially‐distanced in‐person classroom. This involves a much larger classroom for a comparable number of students, so that we can maintain safe levels of physical distancing. This also involves wearing masks. Being spaced far apart and wearing masks. Envision small group discussions in this sort of setting. Envision trying to read non‐verbal cues, or even being able to know if someone is talking by looking at them. I can’t help but think the online learning environment in some ways enables a better learning environment for larger classes, where students are not wearing masks while online and where our cameras provide a clear view of each other. Perhaps a combination of in‐person and virtual should be considered in the interim. Systematic literature review suggests a blended approach including in‐person and online components may afford the balance of benefits needed. 19

Many of us, myself included, having a longing to return to “the way it was.” However, we are in our new COVID‐19 pandemic reality and will be here for some time. Rather than resist this reality, and try to go back to “the way it was”, how can we dive‐in and truly make the most of online learning for our students? Instead of striving only to make online learning as good as in‐person instruction, what if we used this as an opportunity to make it better? I’m convinced that the opportunity of the present time is not only related to online learning, but that our designing for interactive online learning can also directly translate into improving in‐person interactive learning into the future. The experiences and insights in this article are not meant to be limited only to improving online learning, but are focused on improving learning for our students regardless of in‐person or virtual instruction. I hope my experiences and reflections have provided you with at least some form of inspiration or insight.

CONFLICT OF INTEREST

The author has no conflict of interest to declare.

This article is part of the Biomedical Education Special Collection.

• Open access
• Published: 14 July 2022

The impact of virtual learning on students’ educational behavior and pervasiveness of depression among university students due to the COVID-19 pandemic

• Fatima M. Azmi   ORCID: orcid.org/0000-0001-9275-0965 1 ,
• Habib Nawaz Khan   ORCID: orcid.org/0000-0003-3519-264X 2 &
• Aqil M. Azmi   ORCID: orcid.org/0000-0002-0983-2861 3  

Globalization and Health volume  18 , Article number:  70 ( 2022 ) Cite this article

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One of the worst pandemics of recent memory, COVID-19, severely impacted the public. In particular, students were physically and mentally affected by the lockdown and the shift from physical person-to-person classrooms to virtual learning (online classes). This increased the prevalence of psychological stress, anxiety, and depression among university students. In this study, we investigated the depression levels in Saudi Arabian university students who were learning virtually because of the COVID-19 pandemic and examined its impact on their educational proficiency.

The study focused on two points: first, examining the depression levels among undergraduate students in Saudi Arabia, by adapting the Zung (Self-Rating Depression Scale) questionnaire. Second, whether there is an association between the levels of depression and various distress factors associated with virtual (online) learning resulting from the COVID-19 pandemic and its impact on students’ educational behaviors. The questionnaire was prepared using a monkey survey and shared online, via email, and on WhatsApp groups, with participants in two universities, a public and private university in the largest city of Saudi Arabia. A total of 157 complete responses were received. Data were analyzed using SPSS-24, the chi-square test, descriptive statistics, and multilinear regression.

The results indicated that three-fourths of the university students suffered from different depressive symptoms, half of which had moderate to extreme levels of depression. Our study confirmed that a boring virtual (online) learning method, stress, fear of examinations, and decreased productivity were significantly associated with increased depression. In addition, 75% and 79% of the students suffered from stress and fear of examinations, respectively. About half of the students were associated with increased depression. The outcome also indicated that female students experienced extreme depression, stress, and fear of examinations more than males.

These findings can inform government agencies and representatives of the importance of making swift, effective decisions to address students’ depression levels. It is essential to provide training for students to change their educational experience mindset, which might help decrease "depression and stress-related growth." There is also a need to search for a better virtual teaching delivery method to lessen students' stress and fear of examinations.

On March 11, 2020, the World Health Organization (WHO) declared the highly contagious coronavirus (COVID-19) a global pandemic [ 1 ]. As the cases of COVID-19 increased, China, and many other countries practiced partial or complete lockdowns. It is estimated that this drastic measure helped save 3 million lives across 11 European nations [ 2 ]. Toward the end of January 2022, the total number of confirmed COVID-19 cases worldwide was 360,578,392, and 5,620,865 confirmed deaths. The number of people who received vaccination doses globally was 9,679,721,754 [ 3 ].

To contain the virus, the lockdown caused academic disruptions. This resulted in the indeterminate closure of schools, universities, various institutes, shopping malls, and centers of economic activities [ 4 , 5 ]. Repetitive activities, transfer of educational mode to distance (virtual) learning, and change in social life amplified the prevalence of psychological stress, anxiety, depression, and acute stress reactions among university students [ 6 ]. Sociodemographic factors associated with low mental health include financial constraints, old age, infection risk, and fear of losing a relative or friend. In addition, COVID-19 pandemic-related educational stress may be attributed to (in no particular order): (a) transformed teaching and assessment methods; (b) skepticism about university education; (c) technological worries about online courses [ 7 , 8 ]; (d) uncertainty about the future because of academic disruptions; (e) fear of failing examinations; (f) inability to concentrate during lectures, and many more factors. All these factors have been detected in universities across the world [ 9 , 10 ]. A global study that inspected students’ experiences in about 62 different countries, including a university in the United States, found that students expressed worries about their academic achievements and professional careers and feelings of dullness, anxiety, and frustration [ 9 ]. Students in China also reported increased sadness, anger, anxiety, and fear [ 11 ]. The occurrence of depression, psychological distress, and anxiety from pandemics differed from country to country. A study in Italy reported that 15.4% of Italians suffered from extremely high levels of depression, 12.6% were highly stressed, and 11.5% were highly anxious [ 12 ]. In Malaysia, it was reported that severe to extremely severe levels of depression and anxiety were found in 9.2% and 13.2% of the subjects, respectively. Moderate stress was found in 9.5% of subjects, and severe to highly severe stress was found in 6.6% of subjects [ 13 ]. Furthermore, students in Switzerland manifested a decrease in social interface and higher levels of stress, anxiety, and loneliness [ 14 ]. Adults have also reported declining physical activity, while food eating increased during pandemic quarantine periods compared to previous times [ 15 ].

The first COVID-19 case appeared in the Kingdom of Saudi Arabia (KSA) on March 2, 2020 [ 16 ], while the lockdown was imposed on March 8, 2020. To keep students on track due to the pandemic, the education delivery mode was switched to virtual learning. It has been over one year since teaching was transferred online, and many countries worldwide have tried to revert to the standard path of education by opening schools and universities. Although the COVID-19 vaccine is available worldwide, some countries are still practicing lockdown because of the appearance of several more contiguous variants of the coronavirus, such as Delta, a SARS-CoV-2 strain that was first spotted in India [ 17 ]. The spread of COVID-19 presents a serious risk; in mid-April 2022, the confirmed cases in KSA were 751,717, out of which 736,910 had recovered, and 9,055 deaths were recorded [ 18 ].

The psychological consequences of COVID-19 have been observed and described in KSA. Al-Hanawi et al. [ 19 ] reported different levels of distress in 40% of the general Saudi population because of COVID-19. Moreover, Alkhamees et al. [ 20 ] reported moderate to severe psychological effects in 23.6% of the general Saudi population. In another study of the influence of the COVID-19 pandemic on Saudi Arabian residents, Alyami et al. [ 21 ] stated that the percentages of mild, moderate, moderately severe, and severe levels of depression were 41%, 20%, 6.2%, and 3.2%, respectively. Furthermore, Khoshaim et al. [ 22 ] reported that about 35% of students experienced moderate to extreme anxiety levels. Azmi et al. [ 23 ] observed that 75% of students suffered from various levels of depression, while 41% suffered from low levels of self-esteem.

Likewise, another study found that 35% of students in the western and northern regions of KSA had high rates of distress [ 24 ]. Following the observed rise of psychological disorders, the authorities posted health messages and distributed procedures to the public. For example, during the pandemic, the Saudi Center for Disease Control and Prevention (CDC) [ 25 ] supplied a precautionary manual for mental and social health focused on prevention, pressure, and fear control. From the foregoing, the COVID-19 pandemic has had a severe impact on the physical and mental health of the public in general and students in particular, as university students are among those most severely affected by the COVID-19 pandemic.

In this study, we investigated the depression levels of university students in Riyadh, the capital and largest city in KSA, who were learning virtually because of the COVID-19 pandemic. We also assessed the impact of virtual learning on their educational behaviors. The following questions were explored during the investigation:

What are the levels of depression among university students?

What is the impact of virtual learning on students’ educational behaviors and what are the relationship between depressive symptoms they exhibited and virtual learning?

To answer the second question, we explored the relationship between the levels of depression and various distress factors associated with virtual learning because of the COVID-19 pandemic and its impact on students’ educational behaviors. These factors were divided into two main categories: Category 1 dealt with factors relating to how virtual learning has affected students’ feelings from an educational perspective. Category 2: dealt with factors relating to how virtual learning affected students’ understanding of subjects/learning materials.

Once we ascertain the current levels of depression and their impact on students’ educational behavior, we may embark on helping them cope with the extraordinary situation. Hopefully, this will help lower their elevated depression levels. Furthermore, we hope our study will guide policymakers in searching for innovative ways of online teaching to make learning less stressful and more productive.

Design and sampling procedure

This study examines depression levels and investigates virtual learning-related distress factors, which might predict the increased level of depressive symptoms among university students in Riyadh City during the COVID-19 pandemic.

Research design

We conducted a descriptive survey-based study to obtain responses from students at large universities in Riyadh, the capital of KSA. The total size of the target population of the city of Riyadh is about 7 million [ 26 ]. The sampled population of both universities’ undergraduate students was approximately 0.027 million (27,000). The male-to-female ratio of undergraduate students at King Saud University (KSU) is about 67%: 33%; the male-to-female ratio of undergraduate students at Prince Sultan University (PSU) is about 28%: 72%, as this is a female-dominated university. Since the sampled population was largely heterogeneous, we minimized the heterogeneity by dividing the given population into sub-populations to obtain sampling units that are homogeneous internally and heterogeneous externally. Hence, we used a stratified random sampling technique, which is more appropriate than other sampling techniques for obtaining better estimates of the parameters of interest. To ensure the efficiency of the estimates, we used the proportional allocation technique to determine the sample size.

A Monkey survey was used to prepare the questionnaire, following the approval of PSU’s Institutional Review Board. The questionnaire included demographic questions, such as gender, age, and college. Zung’s Self-Rating Depression Scale (ZSDS), with 20 items on a 4-point Likert scale, was used to measure depression. The questionnaire also had questions to address distress factors associated with virtual learning because of the COVID-19 pandemic. The students were asked to read all the questions carefully and answer them.

The survey was written in English and Arabic side by side. A subject expert translated the questionnaire from Arabic to English. Thereafter, five more experts checked the same questions for more corrections and authenticity. The actual online survey took place from March to April 2021. The survey was voluntary, and the informed consent of the students was sought. We received reasonable responses from the students; however, we also received some incomplete responses. The missing/incomplete responses were discarded from the study so that the estimated results were not compromised. The valid responses received from males and females were 49.7% and 50.3%, respectively.

Measuring instruments

Demographic data and personal characteristics, such as age, gender, and area of study, were recorded.

Depression measure

The ZSDS was used to measure the levels of depression. The tool is a 20-item self-reporting assessment device used for measuring depression levels [ 27 , 28 ]. This is divided into 10 positively worded and 10 negatively worded items. The latter items were reversely scored. Each item was scored on a Likert-type scale as follows: 1 =  Never , 2 =  Sometimes , 3 =  Often/most of the time , and 4 =  Always . The total raw scores ranged from 20–80, and when converted into the depression index (termed "ZSDS index"), the range becomes 25–100. To determine the level of depression, we classified the ZSDS index into four classes (levels). Therefore, ZSDS index scores were considered "normal" from 25–49, "Mildly Depressed,” from 50–59, “Moderately Depressed” from 60–69, and “Severely Depressed” from 70 and above [ 27 ]. In [ 29 ], the author translated the ZSDS measure into Arabic and further validated it. Question 6, “I still enjoy sex,” was deemed offensive religiously and culturally. Therefore, it was rephrased to “I enjoy looking at, talking to, and being with attractive women/men,” which is culturally more appropriate. The accuracy of the new version was verified in [ 29 ]. The Arabic and English languages were used side by side to prepare the questionnaire. The Cronbach’s alpha coefficient of this study was 0.87, showing high internal consistency.

Data on distress factors associated with virtual learning

Data on distress factors associated with virtual learning due to the COVID-19 pandemic were divided into two categories. The first category dealt with questions on how virtual learning due to the pandemic affected students’ feelings from an educational perspective and caused a) lack of motivation/boredom, b) stress, c) worry and fear of exams, and d) decreased productivity. The second category dealt with questions on virtual learning and its effect on students’ understanding of subjects/materials, such as a) It needs more self-effort to understand, b) It made learning and understanding harder for them, c) They need more time to understand the subject, i.e., the understanding pace became slower, d) Virtual learning is boring, and e) they had difficulty solving problems in academic subjects and writing down the solutions correctly. The answer to each question was either “Yes” or “No.”

Finally, the questionnaire had an open-ended question that offered students a chance to express in their own words how the lockdown and virtual teaching had affected their educational advancement.

Data analysis

Data were analyzed using IBM SPSS version 24 software. The categorical variable demographic data were analyzed descriptively to determine the essential characteristics of the sample and were presented as counts and percentages. The level of depression index among university students in Riyadh, and its association with gender, age, and their field of education, was analyzed using the chi-square test and descriptive statistics. Multilinear regression analysis was performed to investigate the connection between depression levels and various factors associated with virtual learning due to the COVID-19 pandemic. The statistically significant level was set at \(p \le 0.05.\)

Demographic characteristics

The total number of participants was 157 university students. Table 1 shows the demographic characteristics of the participants.

Students’ levels of depression and demographic variables

In the univariate analysis, chi-square tests were used to determine the associations between students’ demographic variables and the ZSDS level. Table 2 displays the association between depression levels with gender, age, and college. Among the demographic variables, only the association with gender was statistically significant at \({\chi }^{2}\) = 20.229, and p  < 0.001, while the association with age and college was not significant. A total of 74.4% of the students had various levels of depression. Of these, 37%, 21.7%, and 16% had mild, moderate, and severe depression levels, respectively. In addition, females (28%) had more depressive symptoms than males (4%).

Educational distress factors associated with virtual learning and descriptive statistics

The factors related to virtual learning sequel to the COVID-19 pandemic, and its impact on students’ educational behaviors were divided into two categories. Questions on virtual learning's effect on students' feelings from an educational perspective (Category 1) had four items, each with a "Yes" or "No" answer. Likewise, questions on virtual learning and its effect on students’ understanding of the subjects/materials (Category 2) had five items, each with a “Yes” or “No” answer. Table 3 demonstrates the descriptive statistics. In the first category, the highest percentage was feeling worried and having a fear of exams (79%), followed by stress (75.2%), lack of motivation, and decreased productivity (70%, each). In the second category, the highest percentage was 78%, who felt they had to put extra self-effort into understanding and studying.

Furthermore, 74.5% felt that virtual learning was more challenging for them to understand than physical learning. In addition, 73% said virtual learning was slow and extra time was needed to understand and learn the concepts, while 64% found it boring. Finally, 58.6% had difficulty solving problems and submitting properly written answers (for math and computer science subjects).

Distress factors related to virtual learning and depressive symptoms

Multilinear regression analysis was used to study whether various distress factors related to virtual learning can influence depressive symptoms among students.

The first category, which dealt with students’ feelings from the educational point of view, hypothesized that lack of motivation, stress, worry/fear of examinations, and decreased productivity would significantly impact the development of depressive symptoms among students.

Multi-regression analysis was used to test the hypotheses, with the Zung depression index as a dependent variable. The results show that 24.6% of the variance in Zung’s depression index can be accounted for by four predictors, collectively \(, F(4, 152) = 12.414, p < 0.001\) . Looking at the unique individual contribution of the predictors, the result shows that worry and fear of exams ( \(\beta =0.290, t=3.589, p<0.001)\) , stress ( \(\beta =0.202, t=2.566, p=0.011<0.05)\) , and decreased learning amount and not being productive ( \(\beta =0.211, t=2.783, p=0.006<0.05)\) , statistically significantly contributed to worsening depressive symptoms. The predictor, feeling lack of motivation, did not significantly impact developing depressive symptoms.

The second category dealt with virtual learning and its effect on students’ understanding of the subjects/materials. It was hypothesized that the need for extra self-effort to understand the subject, learning became harder, learning became slower, learning was boring, and difficulty in solving problems and writing answers properly would have a statistically significant impact on developing depressive symptoms among students.

Multi-regression analysis was used to test the hypotheses, with Zung’s depression index as a dependent variable. The test showed that 13% of the variance in Zung's depression index can be accounted for by the five predictors, collectively \(, F(5, 151) = 4.505, p < 0.001\) . Looking at the unique individual contribution of the predictors, the result shows that learning is not much fun or exciting ( \(\beta =0.250, t=3.060, p=0.003<0.05)\) , and facing difficulty in solving questions and writing answers properly ( \(\beta =0.176, t=2.067, p=0.05<0.05)\) , were statistically significantly associated with worsening depressive symptoms. While the other three predictors, learning became harder, learning became slower, and the need to put extra self-effort did not contribute significantly to depressive symptoms, as shown in Table 4 .

Furthermore, we explored two distress factors, stress, and worry/fear of exams, which contributed statistically significantly to worsening depressive symptoms. Using the chi-square test, we examined the association of the distress factors with depression levels; that is, what extent does stress or worry/fear of exams contribute to moderate or severe depression. The results showed a statistically significant association between stress and moderate to severe levels of depression ( \({\chi }^{2}\) = 17.179, and p  < 0.001). Likewise, there was a statistically significant association between worry/fear of exams and moderate to severe levels of depression ( \({\chi }^{2}\) = 30.236, and p  < 0.001), Table 5 .

The association between stress or worry/fear of exams and gender was examined using the chi-square test. There was a statistically significant association between these two factors and gender, with more females having higher stress levels (54%) than males (41%). Also, worry/fear of exams manifested in 60% of females and 40% of males during virtual learning, sequel to the COVID-19 pandemic. The results are presented in Table 6 .

Open-ended questions

The questionnaire ended with an open-ended question, in which students were asked to write in their own words how the lockdown has affected their educational advancement. Excerpts of the negative comments from students are outlined below:

“Virtual teaching and exam resulted in increased cheating." “Virtual teaching caused difficulty in understanding the subject, which resulted in lowering my grades.” "I have to sit in the same room with my siblings while learning online, as my home is small. So, I cannot concentrate at all; it just makes me very frustrated.”

From their comments, it is clear that a virtual learning environment is entirely different from a physical classroom teaching environment where exams are conducted with invigilators proctoring.

Significantly few students provided positive comments.

"Virtual teaching made me understand better and increased productivity and my grades."

In this study, we investigated the severity of depressive symptoms among university students while learning virtually because of the COVID-19 pandemic and its impact on educational behaviors in KSA We collected samples from different universities in Riyadh. The total number of complete responses was 157. The Zung Self-Rating Depression measure was used to measure depression levels. Our results indicate that 75% of the students suffer from different levels of depression (37%, 22%, and 16% of the students reported mild, moderate, and extremely severe levels of depression, respectively). This result is consistent with an American study, which reported that 44% of students in the USA experienced an augmented level of depressive thoughts [ 30 ].

The association between the levels of depression and various distress factors associated with virtual learning due to the pandemic and its impact on students’ educational behaviors was explored using multilinear regression. These factors are divided into two main categories: Category 1: These factors relate to how virtual learning has affected students’ feelings from an educational perspective. This consists of four items: lack of motivation, stress, worry/fear of exam, and decreased productivity. Category 2 factors relate to how virtual learning has affected students’ understanding of the subjects/materials. This category has five items, including need of extra self-effort, need to study harder, learning is slower, virtually learning is boring, difficulty in solving problems, and writing properly.

Consistent with our hypotheses, we confirmed that stress, worry/fear of examinations, and decreased productivity were significantly associated with an increased level of depression. Another recognized factor that contributes significantly to a higher risk of developing depressive symptoms among university students is that virtual teaching and learning becomes boring. Furthermore, students faced difficulty in solving mathematics and science problems and writing the answers properly due to online teaching. A few other factors, such as lack of motivation, learning became more complex and slower, and the need to put extra self-effort contributed to developing depressive symptoms.

Our results indicate that 75% of the students suffer from stress, and about half (47%) have high levels of depression. This is consistent with the results in [ 13 ]. Our findings also indicate that 79% of the students suffer from fear of exams, and about half of them (47%) experience moderate to severe levels of depression. It is usual for some students to have worries and fear for exams; however, it is highly unusual for more than three-fourths of the students to experience fear and worry. This is a clear indication that the changed mode of lecture delivery and exam administration because of COVID-19 has a significant role in raising depression levels among university students. Our findings indicate that a higher percentage of females experience extreme levels of depression than males (28% of females compared to only 4% of males), stress (59% females, vs. 41% males), and worry/fear of exams (60% females, vs. 40% males). This finding is consistent with many studies concerning college students, in which females were at a higher risk of suffering psychologically during virtual learning because of the COVID-19 pandemic [ 9 , 31 , 32 , 33 ]. Another study showed that Vietnamese female students had a higher percentage of depression compared to male students [ 34 ]. Furthermore, Huange et al. [ 35 ] reasoned that Chinese females experienced more anxiety than males during the COVID-19 pandemic. Thus, we assert that feamles are more commonly inclined toward depression and anxiety disorders than males [ 36 ].

The results of the open-ended responses demonstrated the students’ frustration and stress relating to online learning. In contrast, very few students positively indicated that online learning and studying from home felt relaxing.

COVID-19 has been a catastrophic experience. Although it has largely subsided, new variants are causing apprehension among health officials. Our research found that 75% of university students in Saudi Arabia suffer from some degree of depression. Half of these students showed moderate to extreme levels of depression. This is greater than the expected depression level in the overall population. Our study confirms that stress, worry, and fear of examinations, decreased productivity, and the fact that virtual learning is boring are significantly associated with increased depression. Our findings also indicate that 75% (79%) of the students suffer from stress (fear of exams), and that about half of them have increased levels of depression. It should be noted that the students are 18–24 year olds. This is consistent with the study [ 22 ], which found that psychological distress, stress, and anxiety were higher in the younger age group during the COVID-19 pandemic.

Remarkably, more female students experienced extreme depression, stress, and fear of exams than male students. This result supports previous reports that females were at higher risk of psychological distress during the COVID-19 pandemic [ 9 , 31 , 32 , 33 ].

Our observation calls for instant attention and sustenance for students. There is a requirement to explore potential coping policies that have been shown to be effective during pandemics [ 37 ]. The results of our research might direct policymakers to develop distress management protocols as part of their policy for dealing with future pandemics [ 38 ]. It is essential to provide training for students to redirect their educational experience mindset to focus on the “bright side” and expand instances that may guide "depression and stress-related growth.” A flexible mindset can also help students adapt to new ways of learning and developing tremendous gratitude for life. In addition, there is a need to explore better online teaching delivery methods to lower students’ stress and fear of exams.

Study strengths and limitations

The strength of this study is that it was conducted after students had received virtual teaching for more than one year because of the Pandemic. Therefore, this study accurately reflects students’ depression levels and how these impact their educational behaviors in KSA.

Furthermore, the study was conducted in Riyadh, the capital of KSA, hence our study sample is more reflective of the Saudi student population. Moreover, the depression assessment tool for the study, the Zung Self-Rating Depression Scale, is a reliable, universally acceptable scale.

The limitation of our study is that the sample was not randomly selected from all university students, as a convenience sampling method was used.

Availability of data and materials

The raw data supporting the results of this study will be made available by the corresponding author without undue reservation.

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Acknowledgements

Fatima Azmi would like to thank Prince Sultan University for funding the project and covering the publication fees.

This work was supported by a research project grant [Grant number: COVID-19-DES-2020–43] from Prince Sultan University, KSA.

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Conceptualization: FMA; AMA. Data Curation: FMA; AMA. Formal Analysis: FMA. Methodology: FMA; HNK. Writing-Original Draft: FMA; AMA. Writing-Review and Editing: FMA; HNK; AMA. The author(s) read and approved the final manuscript.

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Azmi, F.M., Khan, H.N. & Azmi, A.M. The impact of virtual learning on students’ educational behavior and pervasiveness of depression among university students due to the COVID-19 pandemic. Global Health 18 , 70 (2022). https://doi.org/10.1186/s12992-022-00863-z

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Method: Two scientific databases—Scopus and Web of Science—were used for this review. Following the PRISMA method, a total of 139 articles were analyzed. Reliability of this selection process was assessed using five additional coders. A taxonomy was used to classify these articles. Another coder was used to assess the reliability of the primary coder before this taxonomy was applied to the selected articles

Results: Based on the literature reviewed, skills and competences developed are divided into five categories. Educational fields and domains seem interested in Virtual Reality for Collaborative Learning because of a need for innovation, communities and remote socialization and collaboration between learners. Systems primarily use monitor-based Virtual Reality and mouse-and-keyboard controls. A general optimism is visible regarding the use of Virtual Reality to support and enhance Collaborative Learning

Conclusion: Five distinct affordances of Virtual Reality for Collaborative Learning are identified: it 1) is an efficient tool to engage and motivate learners, 2) supports distance learning and remote collaboration, 3) provides multi- and interdisciplinary spaces for both learning and collaborating, 4) helps develop social skills and 5) suits Collaborative Learning-related paradigms and approaches. Overall, the reviewed literature suggests Virtual Reality to be an effective tool for the support and enhancement of Collaborative Learning, though further research is necessary to establish pedagogies.

1 Introduction

Beginning in the 1980s, academia has studied how to support and enhance Collaborative Learning (CL) in educational settings using technology. Referred to as Computer-Supported Collaborative Learning (CSCL), this pedagogical approach stems from social learning, an educational theory revolving around the idea that “new behavior can be acquired through the observation of other people’s behaviors” ( Shi et al., 2019 ) and focusing on social interaction between learners. CSCL’s strength appears to lie in its flexibility: by using characteristics of technology, both distant and face-to-face collaboration, as well as synchronous and asynchronous collaboration between learners, can be supported ( Stahl et al., 2006 ). As such, CSCL has been attributed numerous affordances, including joint information processing, sharing resources and co-construction of knowledge ( Shawky et al., 2014 ; Jeong and Hmelo-Silver, 2016 ).

An on-going development in the field of CSCL is the use of Virtual Reality, a technology that ‘[transports] a person to a reality (i.e., a virtual environment) which he or she is not physically present but feels like he or she is there’ ( Rebelo et al., 2012 ). These virtual environments (VEs) are shared, simulated spaces that allow distributed users to communicate with each other, as well as to participate in joint activities, making them an effective tool for remote collaboration ( Daphne et al., 2000 ). VEs tend to be highly customizable; their visual representation can be realistic (i.e., similar to reality or containing recognizable elements from reality) or abstract (e.g., three-dimensional representations of abstract concepts) depending on their purpose, making VEs adaptable for many different fields and disciplines ( Jackson et al., 1999 ; Joyner et al., 2021 ). Virtual Reality (VR), then, functions as a human-computer interface, allowing users to access these VEs through a variety of hardware, including flat-surface monitors and displays connected to desktop computers, room-sized devices called CAVE systems that project the VE onto its walls and Head-Mounted Displays (HMDs), helmets or headpieces that visualize the VE individually for each eye. In some cases, users inhabit avatars, virtual embodiments that represent their place inside the VE, though in other cases (such as the aforementioned CAVE systems, where users do not have to wear HMDs), no avatars are required for users to detect each other. Like VEs, the visual representation of avatars can be diverse: avatars can provide realistic depictions of users’ real-life appearances, but can also be visualized as something abstract, such as geometric objects or animals. Using these avatars to mediate interactions with each other, users progressively construct a shared understanding of the VE together ( Girvan, 2018 ). Of particular interest is VR’s ability to “immerse” users, providing them a sense of being inside the VE despite its non-physical, digital nature ( Freina and Ott, 2015 ). This immersion may lead to a state of presence, wherein users begin to behave inside the VE as they would in the physical world ( Jensen and Konradsen, 2018 ). Affordances of VR in education include enhancement of experiential learning ( Le et al., 2015 ; Kwon, 2019 ), spatial learning ( Dalgarno and Lee, 2010 ; de Back et al., 2020 ) and motivation and engagement among different types of learners ( Merchant et al., 2014 ; Chavez and Bayona, 2018 ). While research on VR has generally revolved around discovering its potential to support and enhance education, academics appear to agree that the field of educational use of VR lacks pedagogical practices or strategies, with little focus on how the technology should be implemented to reap its benefits ( Cook et al., 2019 ; Smith, 2019 ; Scavarelli et al., 2021 ).

VR technology has already shown potential for the field of CSCL, improving the effectiveness of team behavior, enhancing communication between group members and increasing learning outcome gains ( Le et al., 2015 ; Godin and Pridmore, 2019 ; Zheng et al., 2019 ). What makes the use of Virtual Reality for Collaborative Learning (VRCL) even more appealing for education is its diversity in hardware and, as a result, the different forms it can take depending on the setting. Whether learners interact with the VEs via display monitors, CAVE systems or HMDs, they all seem to produce positive effects such as positive learning gains and outcomes, as well as engagement and motivation for CL ( Abdullah et al., 2019 ; Zheng et al., 2019 ; de Back et al., 2020 ; Tovar et al., 2020 ).

To advance the field of VRCL, as well as to establish its benefits and affordances, several literature reviews have examined research on VRCL. For example, Muhammad Nur Affendy and Ajune Wanis (2019) , aiming to provide an overview of the capabilities of CL through the adoption of collaborative system in AR and VR, review how VEs are used for different types of collaboration (e.g., remote and co-located collaboration), with different VR hardware (e.g., eye tracking) and multiple intended uses (e.g., increasing social engagement and supporting awareness of collaboration among learners). In comparison, Zheng et al. (2019) evaluate VRCL technology affordances by conducting a meta-analysis as well as a qualitative analysis of VRCL prototypes to explore potential learning benefits; Scavarelli et al. (2021) explore a more theoretical side with the intention to produce educational frameworks for future VRCL-related research, discussing how several learning theories (e.g., constructivism, social cognitive theory and connectivism) are reflected in prior research on the potential of VR as well as Augmented Reality (AR) for social learning spaces.

Together, the literature reviews of Muhammad Nur Affendy and Ajune Wanis (2019) , Zheng et al. (2019) ; Scavarelli et al. (2021) describe a general optimism towards VR in educational settings to support collaboration. The reviews outline VRCL’s strengths as 1) its ability to enhance learning outcomes, 2) its potential to facilitate learning, 3) its effectiveness in supporting remote collaboration between learners, as well as experts and novices, 4) its support for interpersonal awareness between collaborating learners and 5) its diversity, both in terms of its customizability (allowing VEs to better suit objectives) as well as its technology. Affordances of VRCL are identified as 1) social interaction (strengthened by VR’s affordances of immersion and presence), 2) resource sharing (strengthened by VR’s ability to present imaginary elements) and 3) knowledge construction (supported by the two prior affordances of VRCL). Furthermore, challenges and gaps related to (research on) VRCL are outlined. First, accessibility should be considered a primary concern according to Scavarelli et al.,; this does not just relate to the technical accessibility of VR when used in education, but more so to the accessibility of social engagement between learners sharing these virtual learning spaces. Second, they recommend to explore the interplay and connectivity between VEs and the real world, as doing so could reveal new learning theories that innovate VRCL. Third, Zheng et al., suggest that research focus on pedagogical strategies involving VRCL, including how to apply VR to educational settings involving collaboration. Fourth, they propose a focus on finding a balance between using VRCL to recreate (or simulate) existing (“real”) situations and creating new situations that would normally be impossible, considering that prior work has primarily been centered on the former and as such misses out on VR’s potential to do the latter.

Considering that remote collaboration and distance learning, especially since the COVID-19 pandemic, are becoming increasingly important for learners, an understanding of VR’s potential for CL could prove beneficial for the field of education. While research on the topic is apparent, studies focusing on VR’s ability to support and enhance CL are still small in scale ( Zheng et al., 2019 ; Scavarelli et al., 2021 ), accentuating the scarcity of knowledge on the topic. This systematic review specifically centers on scientific research on VRCL, with a particular focus on the empirical knowledge that such literature has established. The aim of this paper is to examine in what ways VR supports and enhances CL according to prior research on these topics; to achieve this, it reports on what VRCL is used for in different fields of education, discusses what research has stated regarding VRCL in terms of affordances and benefits for education, describes the characteristics of VRCL that allow these benefits to come to fruition and provides an insight into the technology behind VRCL, as well as how this compares to the state-of-the-art of VR. In doing so, this study intends to identify possible gaps in the field of VRCL research for possible future studies, in addition to highlighting VRCL’s strengths to support current research. To the best of the authors’ knowledge, this study is the first systematic review on the topic of VRCL. As a means to provide the relevant information, this review addresses the following four research questions.

1. What skills and competences have been trained with use of VRCL (and what should a VRCL environment provide to train these)?

2. What domains and disciplines have been addressed (and why)?

3. What systems have been developed and/or established?

4. What empirical knowledge has been established (and with what methods and/or study designs)?

This section discusses the process of collecting the relevant studies for this literature review. In particular, the inclusion and exclusion criteria, databases and methods used are described.

2.1 Identification

The systematic review used two databases: Scopus and Web of Science. The search query contained the following key elements: 1) collaborative interaction, 2) VR, 3) education, training and learning, 4) simulations of a three-dimensional nature, 5) empirical data and 6) the use of a system (application or prototype). As such, the following search string was used in both databases:

[collaboration OR cooperation OR collaborative OR cooperative OR collaborate OR cooperate] [AND] ["virtual reality” OR “mixed reality” OR “extended reality"] [AND] ["3D” OR 3d OR 3-D OR 3-d OR threedimension* OR three-dimension* OR “three dimension*" OR CGI OR “computer generated” OR “computer-generated” OR model* OR construct*] [AND] [evaluat* OR data OR result* OR observ* OR empiric* OR trial* OR experiment* OR significan* OR participant* OR subject*] [AND] [education OR training OR learning OR university OR school OR vocational] [AND] [system* OR prototyp* OR application* OR program*]

To be considered suitable, papers had to meet five specific inclusion criteria. Firstly, an article had to discuss collaborative or cooperative interaction between human users of a virtual, three-dimensional simulation. Secondly, the article had to include and discuss Virtual-, Augmented-, Mixed Reality (MR) or Extended Reality (XR) as a three-dimensional simulation of a physical space or object(s). While this review focuses on VR for CL, mediums such as AR, MR and XR were included in this search for two reasons. On the one hand, definitions for these mediums appear to overlap to such an extent (with some even considering them too vague and ambiguous ( Tovar et al., 2020 )) that ‘pedagogical advantages of either technologies are [considered] comparable’ ( Sims et al., 2022 ). On the other hand, the mediums in question do not always get defined as separate ones, but rather as different points on one spectrum, commonly referred to as the virtuality continuum, in which ‘“reality” lies at one end, and “virtuality” […] at the other, with Mixed Reality […] placed between’ ( Scavarelli et al., 2021 ). As such, the decision was made to include these mediums, so as to ensure that no pedagogical advantages of VR would be excluded. The third inclusion criterium required an article to include an empirical study (i.e., containing qualitative or quantitative data) for it to be considered suitable. For the fourth and fifth criteria, an article had to contain an educational objective or goal (for human entities) and discuss a system used for educational purposes (for human entities) in order to be eligible.

Additionally, studies would be disqualified from the literature review if they 1) only described a patent, 2) only contained a summary (review) of a conference, 3) only consisted of a literature review, 4) were not accessible to the authors of this study, 5) were not available in English, 6) were a duplicate or a version, edition or release of an older study that already had been included or 7) did not specifically state the number of participants of any experiment involved in the study.

The search query resulted in 1,058 publications for Scopus and 845 studies for Web of Science, resulting in a total of 1,608 studies after duplicates were removed. Using the inclusion and exclusion criteria to filter out ineligible articles (initially based on title and abstract, then on full text), this review resulted in 139 articles analyzed. Results and details of the process (which followed the guidelines of the PRISMA method ( Moher et al., 2009 )) can be seen in Figure 1 . Appendix A shows the complete list of all 139 articles.

FIGURE 1 . Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) diagram of the screening process.

To examine reliability of the selection process, five additional coders screened a random sample of 50 studies individually (10 per coder) using the inclusion and exclusion criteria. After comparing and discussing results, inter-rater reliability (between the first coder and the five coders) was calculated using a Kappa-metric, resulting in a moderate level of agreement of 0.77 ( McHugh, 2012 ) (results can be found in Supplementary Table B1 ).

A taxonomy ( Figure 2 ) was created to help classify all 139 articles. With this review’s research questions in mind, three vital topics were established to function as main categories for the coding process: education, system and evaluation (illustrated in column C1 in Figure 2 ). For RQ1 and RQ2, the first category, education, was established to extract information from the articles, concentrating on six classes. Similarly, information necessary to answer RQ3 was collected by coding attributes related to the second category, system, which included eight classes. Focusing the coding on elements related to the third category, evaluation (with five classes), allowed for extraction of relevant information required to answer RQ4. After the relevant categories, classes (visible in column C2 in Figure 2 ) and attributes (visible in column C3 in Figure 2 ) were decided upon, the classification hierarchy in Figure 2 was constructed, partially based on scientific literature ( Bloom et al., 1956 ; Schreiber and Asnerly-Self, 2011 ; Motejlek and Alpay, 2019 ), to provide assistance during the coding process. For an in-depth description of the motivation behind this classification hierarchy, please see Supplementary Appendix C . While the required information for some of these attributes could easily be inferred directly from each study, other attributes required the first coder to deduce which attributes were applicable.

FIGURE 2 . Classification hierarchy used for coding, including percent agreement ( p a ) and Cohen’s kappa (K) between first and second coder on the right.

To assess reliability of the first coder, a second coder classified articles with the taxonomy ( Supplementary Table D1, D2, D3 ). Inter-rater reliability between the two coders for 30 randomly selected studies was 0.60 (with a percent agreement of 0.85), considered a moderate level of agreement ( McHugh, 2012 ). Additionally, Figure 2 shows the inter-rater reliability for each individual class.

3 Descriptive results

In this section, discussion of descriptive results is divided into three sections according to the structure of the taxonomy. An overview of all results (according to the taxonomy) can be found in Figure 3 .

FIGURE 3 . Results of coding of data found in the literature, according to the taxonomy.

3.1 Education

As a first dimension, elements related to education were analyzed. A majority of the selected articles focused on VRCL in tertiary education (i.e., university), discussing possible uses for students. Educators providing support (e.g., scaffolding) for learners proved most prominent, though not all studies discussed this topic. While a wide selection of educational domains were discussed, computer sciences and social sciences were the most popular fields. Most studies specifically focused on synchronous collaboration. Prevalent among learning paradigms and educational approaches were problem-based learning (PBL) and constructivism. The specific results related to this dimension are found in Table 1 .

TABLE 1 . Distribution of Education-related attributes.

In contrast to the high number of articles focusing on tertiary education (64.0%), primary education was central in 10.8% while 5.0% discussed VRCL in secondary education. A small percentage of studies (6.5%) focused on types of learners outside of formal education (e.g., on-the-job training). In relation to the educators, a little over half of the studies reported on educators supporting the learners by providing varying degrees of scaffolding (55.4%). For 20.9% of cases, educators provided presentations and lectures inside the VE, providing a more passive learning experience. On a broader scope, the studies showed a wide variety of educational domains and fields of expertise to which VR was applied. While approximately a quarter of studies reviewed (25.9%) reported use of VRCL for education, specific domains that were often discussed included computer science, robotics, ICT and informatics (12.2%), social sciences (11.5%) medical fields (9.4%) and engineering (8.6%).

Also shown in Table 1 is the appearance of different types of social learning: 62.6% of studies reviewed discussed synchronous (collaborative) interaction, while in comparison a much lower 18.0% discussed asynchronous (cooperative) interaction. For a 10th of the studies, an expert-novice type of social learning was apparent (9.4%). On the topic of educational approaches and learning paradigms, 29.5% of articles did not seem to discuss any specific approaches. Among those that did, constructivism and PBL were featured substantially (33.1% and 41.0%, respectively), while paradigms such as experientialism, situated learning and distributed cognition were discussed less frequently. Other educational approaches, discussed in 35.3% of articles, included self-regulation and shared regulation (e.g., Al-Hatem et al., 2018 ) as well as cognitive apprenticeship (e.g., Bouta and Retalis, 2013 ). Looking at the learning goals and outcomes, the cognitive domain proved to be popular (50.4%), whereas affective and psychomotor domains were featured much less (7.9% and 5.0%, respectively). Other goals and outcomes included general student engagement (discussed in 31.7%) and support of collaboration amongst learners (60.4%).

The second dimension took a closer look at systems used in the studies, including aspects related to the hardware used (e.g., devices, types of control) as well as users’ interaction with VEs (e.g., degree of virtuality, virtual embodiment). A majority of the studies reviewed did not use VR technologies such as HMD-based VR (HMD VR), but instead focused on monitors and displays when discussing VRCL. Most studies chose general purpose controls (e.g., mouse and keyboard) over more advanced hardware such as positional tracking. A majority of studies provided their participants with full-body embodiment (e.g., avatars) and the ability to manipulate virtual objects while inside the VEs. Approximately a quarter of studies used systems for edutainment purposes (i.e., learning by having fun), while system use for training or therapeutic purposes was less common. Table 2 shows these results in detail.

TABLE 2 . Distribution of System-related attributes.

Results showed a clear preference for 3D (non-HMD) simulations, i.e., a virtual simulation of a (physical) environment projected on a surface or display that is not a Head-Mounted Display (and, as such, is considered less immersive): this degree of virtuality was far more prominent in the reviewed studies (78.4%) compared to the lesser implemented AR/MR (16.5%) and HMD VR (7.2%). The hardware used in these studies reflected this: a large amount (89.2%) implemented flat-surface monitors and displays to present VRCL environments. These studies commonly used desktop computer set-ups that included a keyboard, mouse and monitor, though in the case of AR and MR, surface-based mobile devices were often used. When using the system in a larger setting (i.e., larger group size), studies utilized projector-based (but still flat) surfaces to display the VE (e.g., Bower et al., 2017 ). In some cases, several types of these flat-surface displays were being used in different phases of a study (e.g., Nuñez et al., 2008 ). Cases that used CAVE systems (3.6%) included ImmersaDesks, CAVE-like devices that derive from the original CAVE systems. Studies that involved HMD VR used devices like the Oculus Rift and HTC Vive, while studies revolving around AR and MR implemented devices like the HoloLens. Some studies involved multiple devices to compare effects based on the difference (e.g., monitor-based vs HMD VR, as discussed in Vallance et al., 2015 ) while others discussed implementation of HMD VR and AR-related devices as possible future directions without using these in their experiments. With regard to user interaction, studies that implemented general purpose controls used simple computer keyboard and mouse, though some cases also involved video game controllers such as the Nintendo Wiimote and Nunchuck ( Li et al., 2012 ). Apart from the more default specialized controls such as 3DoF and 6DoF controllers or mobile device-based touch screens, studies also discussed a wide variety of other tools in this category, including multi-touch tabletops, haptic feedback devices, Xbox Kinect and gesture-sensing data gloves. While scarce, gaze control and positional tracking (15.1% and 11.5%, respectively) was primarily found in studies that used (mobile-based) AR and HMD VR, though some studies also provided these through devices such as the HoloLens or as part of a CAVE system.

Of the studies examined for this review, 55.4% discussed (self-developed) prototypes, while 44.6% used (pre-existing) applications. The most prominently-mentioned engine for prototypes was Unity, with % (of 77 studies) using it. Concerning the ones that used applications (62 of 139), more than half discussed VE application Second Life (%), while open-source VEs OpenSimulator and Open Wonderland were used in smaller numbers (% and %, respectively). In regard to the intended function of systems used, the majority of articles described a strictly educational one (58.3%) and revolved around implementing these systems in educational contexts as well as using them to facilitate collaborative learning. Studies that used systems to both educate and entertain (22.3%) tended to focus on game-based learning and serious games, though some cases also discussed video games originally not intended for educational purposes (e.g., World of Warcraft ( Kong and Kwok, 2013 ), Minecraft ( Mørch et al., 2019 )). When training purposes were mentioned (17.3%), this often indicated the use of VEs to train specific expertises, such as liver surgery or aircraft inspection. Rare cases where a system was used for therapeutic purposes (just 2.2%) included use of VRCL to teach social skills to patients with autism ( Ke and Lee, 2016 ) or to train physical activities amongst elderly ( Arlati et al., 2019 ).

Motivation behind studies’ choices for the size of collaboration differed between experimental reasons (e.g., a limited number of participants), pedagogical reasons (e.g., using pairs to better stimulate personal social interaction between members compared to larger groups) and reasons related to the systems (e.g., limited hardware availability). Small groups proved to be the most used group size, with 37.4% describing groups of between three and nine members. Pairs were used in 22.3% of studies. Motivations behind pairs included focus on expert-novice interaction and system capabilities (e.g., support for two users maximum). Articles that described larger groups (ten or more members) generally had entire classes of learners interact with system (15.1%).

Apart from a small number of studies that did not provide sufficient information on the matter, virtual embodiment of the users was featured prominently. In cases where physical attributes were virtually represented by (imagery of) tools (18.0%), the VRCL environment was often implemented for specific training of certain expertises. In general, partial virtual embodiment appears in first person, HMD VR (for example, when only the user’s hands are made visible); while scarce (3.6%), studies that displayed partial virtual embodiment provided some interesting examples outside of HMD VR. Examples of partial embodiment included a detailed 3D face to focus on emotional and social expressions ( Cheng and Ye, 2010 ) and using controllable, flat-surfaced rectangles in a 3D environment on which users’ real-life faces were projected via webcam ( Nikolic and Nicholls, 2018 ). Full-body embodiment proved to be the most popular, with 67.6% of studies using systems that provide users complete (full-body) virtual representation. To a degree, the relatively high number of studies that present full-body embodiment can be explained by the systems that were implemented; applications such as Open Simulator and Second Life provide users with customizable avatars, making a full-body virtual embodiment a default feature. In some cases, however, studies specifically examined the effects of virtual embodiment, such as Gerhard et al. (2001) examining possible influences of different avatars on users’ sense of presence. On the topic of user influence on VEs, a little more than half the studies (53.2%) used systems that allowed (some degree of) virtual object manipulation, whereas approximately a quarter of the studies (26.6%) also provided users the tools to manipulate actual content of the VRCL environment. In 16.5% of studies, the system only allowed users to be visibly present inside the VE, while only 3.6% did not provide sufficient information on the matter.

3.3 Evaluation

For the third dimension, the selected articles were analyzed on how they evaluated applying VRCL. Articles frequently concentrated on evaluation of the system(s), with a higher number of them using self-report evaluation methods. Study design of the studies shows a similar result: pre-experimental study design (typically used for preliminary testing of systems) was regularly implemented, with surveys being a popular method of collecting data. While the number of participants was diverse, roughly half of studies reviewed used a sample size between 1 and 25 participants. The majority of articles discussed positive outcomes, whereas only a small amount featured negative results. Detailed results are displayed in Table 3 .

TABLE 3 . Distribution of Evaluation-related attributes.

The majority of studies focused on evaluating a system’s effectiveness when using it in educational settings (71.2%). These studies concentrated on the system’s capacity to support collaboration between learners. Other topics of discussion were student interest in the system and how the system can facilitate learning. Whenever studies examined processes (34.5%), evaluation would be centered around attempts to understand how group interaction materializes in these environments. This included how learners resolve social conflicts ( Cheong et al., 2015 ) and examining how co-presence (e.g., Kong and Kwok, 2013 ) and PBL take shape in VRCL environments. 35.3% of articles discussed learning outcomes after participants interacted with the system. The few situations where the above three attributes did not apply (3.6%) included a study that aimed to develop design guidelines ( Economou et al., 2001 ) and a study primarily interested in the teacher’s role when learners interact with VEs ( Lattemann and Stieglitz, 2012 ).

Most studies collected self-reported data from their participants (85.6%), while over half used behavioral methods to obtain tracking and observational data (59.0%). Articles that reported on knowledge- and/or performance-based assessments (20.9% of studies) often used pre- and post-tests to acquire their data, while only one appeared to use physiological data, tracking participants’ heart rate (0.7%). A notable number of articles (79.9%) implemented pre-experimental design in their studies. Some of these were case studies, applying VEs to educational settings (e.g., Terzidou et al., 2012 ), while others performed pilot studies to establish a first impression of the effects of a system on specific pedagogical situations (e.g., examining how VE-based application OpenSimulator influences Transactive Memory Systems amongst learners ( Kleanthous et al., 2016 )). Quasi-experimental- (13.7%) and true experimental designs (5.8%) were used scarcely, while only 2 out of 139 studies (1.4%) performed an experiment with single-subject design. With respect to non-experimental and descriptive designs, 84.9% of studies implemented a survey-based design, whereas a little over half used observational designs to collect data (56.1%). In some cases, comparative and correlation designs were implemented (7.9% and 15.8%, respectively).

Table 3 also reveals that approximately half of the studies sampled between 1 and 25 participants (53.2%), while around a quarter (26.6%) used a sample size between 26 and 50 participants. For 13.7% of articles, between 51 and 100 participants were used, whereas only 6.5% discussed using more than 100 participants for collecting data. In terms of outcomes, around half of the studies concluded that their system(s) seemed positive and promising (53.2%), while 17.3% draw positive conclusions based on significant outcomes from statistical hypothesis testing. Negative outcomes were scarce, with only 2.2% of the studies reporting negative results. Mixed outcomes were reported for 7.2% of the studies, whereas 20.1% discussed results that were inconclusive, showed no effect or reported outcomes on which positive and negative effects are not applicable.

4 Qualitative results

In general, the literature reviewed for this paper shows a positive attitude towards the use of VR to support and enhance CL. However, the results quickly make it apparent that the methods of applying VR to educational fields to support and enhance CL can vary greatly amongst the studies examined here. In order to acquire a general understanding how these studies have attempted to support and enhance CL using VR, this section will discuss qualitative results established. The rest of this section will be divided into sub-sections, each focusing on discussing results related to one of the four research questions of this literature review.

4.1 Skills and competences trained with VRCL

A number of elements can be identified regarding skills and competences trained with VRCL. Based on the skills and competences discussed in the reviewed literature, five categories were established for this study with the intention to provide a concise overview. These categories, including examples of each category, can be viewed in Table 4 .

TABLE 4 . Skills focused on in the reviewed literature.

For the types of skills and competences shown in Table 4 to be trained effectively, a VRCL environment requires a number of features that support the learners in learning these abilities. Based on the information provided by the reviewed literature, nine required features and design parameters of VRCL can be identified. First, virtual embodiment plays an important role in how learners view themselves and each other inside the VE, impacting learning outcomes and collaborative behavior by providing a sense of awareness and belonging ( Edirisingha et al., 2009 ; McArdle and Bertolotto, 2012 ). Second, efficient communicational tools are essential for effective collaboration: verbal (audio) communication is crucial ( Economou et al., 2001 ; De Pace et al., 2019 ), though additional modalities such as haptic technology can further enhance collaboration ( Moll and Pysander, 2013 ). Third, usability and accessibility should be taken into consideration: VRCL systems should be accessible to all levels of technical skills as differences negatively affect group cohesion and learning between group members (Y. Chang et al., 2016 ; Denoyelles and Kyeong-Ju Seo, 2012 ). Fourth, learners’ perceived usefulness of the VE also affects group cohesion; factors such as awareness, presence and social presence appear to significantly influence this perceived usefulness ( Denoyelles and Kyeong-Ju Seo, 2012 ; Yeh et al., 2012 ). Fifth, the ability to interact with elements inside the VE are considered key: to optimize learning outcomes, learners must have the option to manipulate elements inside the VE (e.g., virtual objects or virtual tools) in a seemingly natural and intuitive way ( Vrellis et al., 2010 ; Bower et al., 2017 ). Sixth, academic efficacy can be achieved if tasks inside the VE are designed around its educational, collaborative objectives, especially when designed for equal input from all learners in a group ( Wang et al., 2014 ; Nisiotis and Kleanthous, 2019 ). Seventh, educators should be ready to provide support, motivation and moderation of collaboration while learners interact inside the VE ( Lattemann and Stieglitz, 2012 ; Bower et al., 2017 ). However, the eighth feature, a level of autonomy, is equally important for each individual learner, not just in terms of independence from the educators, but more importantly from each other, as this allows them to provide different points of views as well as to explore multiple representations, thus improving CL ( Hwang and Hu, 2013 ). Ninth, implementation of VRCL should make sure to primarily support socialization inside the VE, as underestimating the importance of socialization might lead to features of VR obstructing rather than facilitating CL ( Chang et al., 2009 ).

Surprisingly, only a small number of the literature reviewed focused on goals related to the affective domain (7.9%). With some calling VR the “ultimate empathy machine” ( Rueda and Lara, 2020 , p.6), the medium’s ability to induce emotions has been prominently discussed and studied. Not only has VR been shown to indeed be capable of enhancing empathy amongst users ( Herrera et al., 2018 ), with some even arguing it to be more effective than traditional empathy-shaping methods ( Liu, 2020 ), studies have also suggested it to be an effective tool to offer a uniquely different level of understanding ( de la Peña et al., 2010 ). This would suggest that VR’s ability to create a better understanding of different group members’ points of view could in turn support collaboration between learners.

Similarly, even less literature reviewed focused on goals related to the psychomotor domain (5.0%). Prior studies have been positive and hopeful regarding VR to expand the possibilities of physical training ( Pastel et al., 2020 ). Interestingly, technical features such as positional tracking even seem to be effective in predicting psychomotor outcomes ( Moore et al., 2021 ), which could prove useful for domains that specifically focus on expert-novice training in primarily physical tasks (e.g., certain types of engineering). However, positional tracking, not unlike psychomotor outcomes, is only discussed sparingly (11.5%) in the literature reviewed.

An interesting observation in relation to the evaluation methods used in the scientific literature is that only 1 out of 139 articles used physiological measures. As suggested by research, physiological synchrony between group members can serve as an effective indicator for the quality of interpersonal interaction between them (with a higher physiological synchrony correlating with a higher interaction level) ( Liu et al., 2021 ). Furthermore, physiological measurements can be used to identify multiple predictors related to education and training, including the quality of collaboration between group members ( Dich et al., 2018 ). Additionally, visualizing physiological results of each member of a group to the others in real-time during collaboration has shown to have a positive effect on the empathy levels and cohesion of the group, further suggesting how collaboration between learners could benefit from physiological measures ( Tan et al., 2014 ). Considering VR’s visual characteristics as well as research arguing that physical signals such as electroencephalogram (EEG) can conveniently and unobtrusively be tracked during use of HMD VR ( Tremmel et al., 2019 ), future research on VRCL could prove fruitful in terms of training collaborative skills and competences via use of physiological-based information.

4.2 Disciplines focused on regarding VRCL

When looking at the most prominently-featured domains in the literature reviewed (as shown in Figure 4 ), examining what motivated researchers to study VRCL in the field of 1) education, 2) computer science, robotics and informatics and 3) social sciences can provide an understanding of VRCL’s role in these different disciplines.

FIGURE 4 . Results of the educational disciplines focused on in the reviewed literature.

For the field of education, some studies focus on the potential behind VRCL, intending to discover what it can mean for the development of cognitive and technical skills ( Franco and De Deus Lopes, 2009 ). Other studies focus on possible learning gains, examining how knowledge gained in VEs transfers to the real world (i.e., how learners apply outcomes in VEs to situations in actual reality) or attempting to facilitate this transfer by implementing elements of both ( Carron et al., 2013 ). In certain cases, articles specifically examine VEs’ effects on collaboration and how VR can be used to reinforce CL (e.g., Tüzün et al., 2019 ), whereas others aim to determine if existing educational paradigms such as constructivism can be applied to VRCL environments and, if so, how that affects group knowledge gain between learners ( Girvan and Savage, 2010 ). Together, these studies present a general motivation to discover what VRCL can mean for education and where its potential may lie.

For computer science, robotics and informatics, use of VRCL can be summarized in two motivations: 1) innovate these domains and 2) create a learning community. In the first case, researchers intend to utilize the affordances VRCL environments have to offer to further advance fields such as computer science, which have been criticized in the past for using two-dimensional learning platforms and oral-based teaching methods ( Pellas, 2014 ). With VEs, educators can provide learners realistic yet illusionary worlds that are flexible, customizable and even allow for detailed statistics on learners’ performance ( Champsas et al., 2012 ). In the second case, reviewed articles vocalize a desire to use VRCL to provide learners purposeful collaborative activities that create a sense of belonging to a learning community, using aspects such as awareness, presence and different methods of communication to motivate learners in these fields to work together closely ( De Lucia et al., 2009 ).

In similar fashion, social studies appears to be interested in how socialization between learners is manifested inside VRCL (e.g., Edirisingha et al., 2009 ). Some articles go further, studying how VRCL can support socialization: Molka-Danielsen and Brask (2014) suggest that presence, awareness and belonging allow for communication, negotiation and trust between learners, elements deemed necessary for completing collaborative tasks. Other studies focus on specific characteristics of socialization, such as how gender could affect social interaction and group cohesion inside VEs ( Denoyelles and Kyeong-Ju Seo, 2012 ). Collectively, these articles show a desire to understand how elements related to socialization transfer to VRCL, as well as how these environments can sustain and even enhance those elements.

4.3 Systems developed and/or established for VRCL

The results related to systems used show that there is quite a disparity between use of HMD VR and that of non-HMD VR. Almost 80% of systems implemented non-HMD VR, with AR/MR and HMD VR implemented far less frequently (16.5% and 7.2%, respectively, as illustrated in Figure 5 ). Almost 90% of studies described the use of flat-surface monitors and displays, which, when compared to the 10.8% of studies that used HMD devices, further highlights the low use of HMD VR in the literature reviewed (see Figure 6 ).

FIGURE 5 . Results of the degree of virtuality of systems discussed in the reviewed literature.

FIGURE 6 . Results of the hardware used in the reviewed literature.

The lack of representation of HMD VR in these articles is somewhat surprising, considering this type of virtuality and hardware is commonly associated with the medium of VR ( Dixon, 2006 ; Bonner and Reinders, 2018 ; Jing et al., 2018 ). The statement that research into application of VR to the field of education lacks a focus on HMD VR, however, is not uncommon ( Sousa Santos et al., 2009 ; Scavarelli et al., 2021 ), thus begging the question: why is it underrepresented in the reviewed literature?

One possible explanation could be that HMD VR is known to be difficult to apply to educational settings because of its high costs ( Olmos et al., 2018 ). Some of the articles analyzed for this review were published in the late 90s; while HMD VR technology was already available in those times, devices were more expensive and less technologically advanced compared to the technology that is available now ( Mehrfard et al., 2019 ; Wang et al., 2022 ). Furthermore, the technical skills necessary to implement VR properly in educational settings can prove challenging ( Jensen and Konradsen, 2018 ). Since collaboration involves multiple people, difficulties related to accessibility could be more severe when applying VR to a larger group of learners. Another possible reason is the health risks associated with the technology: HMD VR is often connected to motion sickness and cybersickness ( Olmos et al., 2018 ; Yoon et al., 2020 ). A third reason refers to the general lack of pedagogy on the topic of HMD VR: while the medium’s potential for education is often discussed, general guidelines as to how it should be applied efficiently to educational settings ( Cook et al., 2019 ; Zheng et al., 2019 ) as well as an understanding of how learning mechanisms operate inside VR environments ( Smith, 2019 ) are missing. Naturally, the small size of research done on VR and CL exacerbates this lack even further when specifically discussing VRCL. A possible fourth reason that is more closely tied to this particular literature review is that, despite its popularity in research, HMD VR appears to still lack empirical evidence of its educational value ( Sousa Santos et al., 2009 ; Makransky et al., 2019 ; Radianti et al., 2020 ), which, considering this review’s focus on empirically-based knowledge, could explain its scarcity.

The low representation of HMD VR and high representation of non-HMD VR could be related to the ongoing discussion about what defines VR and how it differs from VEs, as discussed in-depth by Girvan, (2018) . Girvan argues that some use terms synonymously with VR and/or VEs, while others use these same terms to classify different types of VEs, thus creating a fragmented understanding of what these are (and what they are not). Girvan’s point is reflected in the reviewed literature of this paper: while some studies identify Second Life as a “virtual environment” or “virtual world” (e.g., Terzidou et al., 2012 ), others refer to it as “virtual reality” (e.g., Sulbaran and Jones, 2012 ). To prevent further confusion with technologies with similar technical features, Girvan suggests to conceptualize VEs as ‘shared, simulated spaces which are inhabited and shaped by their inhabitants, who are represented as avatars [that] mediate our experience of this space as (…) we interact with others, with whom we construct a shared understanding of the world at that time’. VR, then, should be defined as ‘a technical system through which a user or multiple users can experience [such] a simulated environment’ ( Girvan, 2018 ).

Apart from causing a fragmented understanding of the terms in the literature, different interpretations of VR and VEs also lead to HMD VR and non-HMD VR being described as one and the same thing under the moniker of “virtual reality”. Though this may seem a trivial dispute about labels, treating these two types as identical will lead to misconceptions regarding both, as HMD and non-HMD VR contain different benefits and limitations when applied to education. While some studies showed no differences between the two in terms of specific learning outcomes (e.g., spatial- ( Srivastava et al., 2019 ) and language learning (J. Y. Jeong et al., 2018 )), other research highlighted several differences between HMD and non-HMD. Compared to non-HMD, HMD VR has shown to provide a much higher sense of embodiment, which in turn is hypothesized to lead to higher performances, in particular in psychomotor skills ( Juliano et al., 2020 ; Saldana et al., 2020 ). Similarly, HMD VR appeared superior to computer screens in terms of arousal, engagement and motivation in learners ( Makransky and Lilleholt, 2018 ). In contrast, however, Makransky et al. (2019) reported overloads and distractions caused by HMD VR, leading to poorer learning outcomes compared to non-HMD, a sentiment shared by Parong and Mayer (2021) , who described HMD VR to cause high affective and cognitive distractions. Amati and McNeill (2012) even argue that the difference between HMD and non-HMD VR (and in particular how the two are interacted with by users) have severe implications for teaching and practice.

With all of the above in mind, the low representation of HMD VR in the literature examined for this review can be interpreted in two ways. On the one hand, the underutilization underlines that HMD VR is not being used to its full potential and could very well hold much more promise for the field of education and CL. On the other hand, the low use of HMD VR could suggest that implementation of HMD VR in education and/or CL is, in fact, not worth the trouble it brings with it. Whether HMD VR is a benefit or a burden, then, arguably depends on three important elements: 1) the goals (i.e., what skills and/or competences are supposed to be trained), 2) the setting (i.e., the disciplines and fields to which it is applied), and 3) the affordances of VRCL (and to what degree these conform to the goals and setting).

4.4 Empirical knowledge established regarding VRCL

When summarizing the outcomes of the 139 articles, 70% of the studies reviewed displayed a positive attitude towards the application of VRCL to education. While a relatively low number (approximately 25%) presented statistically significant outcomes, this does illustrate a strong optimism amongst those studying VRCL environments in different fields of education as described in prior literature reviews on the topic. This could also explain the high number of studies that deployed pre-experimental study designs: with VRCL being a relatively new addition to the world of CSCL, as well as one that continues to rapidly advance because of the technology behind it, many seem enthusiastic and eager to see what promises VRCL holds when used in different fields and with different types of learners.

Regarding affordances discussed in the reviewed literature, several features are identified. First, VRCL appears an efficient tool to engage learners and to motivate them to study and learn. The ability to customize VRCL environments and their content provides learners more personalized experiences that better suit their personalities and attitudes, thereby enhancing the motivation to learn on both an individual and group level ( Arlati et al., 2019 ). Furthermore, VRCL’s immersive qualities tend to make the experiences more engaging for learners, encouraging them to engage in presentations and demonstrations as well as to communicate and collaborate with each other ( Avanzato, 2018 ).

The second affordance identified VRCL as a great tool for distance learning and remote collaboration. VEs provide a method for learners and educators to work together and collaborate despite distances. In comparison to other media, however, VRCL brings with it a high sense of immediacy (i.e., ‘verbal and non-verbal behaviors that give a sense of reduction of physical and psychological distance between the communicators’), which in turn presents an increased perception of learning ( Edirisingha et al., 2009 ). Additionally, VRCL’s immersive qualities and high presence allow for environments capable of simulating training as preparation for real-life experiences ( Al-Hatem et al., 2018 ) that simultaneously promote active participation and social interaction ( Mystakidis et al., 2017 ) in a setting that feels personal despite distances between learners ( Desai et al., 2017 ). In certain cases, such as education for learners with physical disabilities, learners and educators even considered connectivity to be more accessible and easier than real-life equivalents ( Aydogan and Aras, 2019 ), illustrating that VRCL environments can potentially go beyond simply being a replacement. To effectively support the distance learning and remote collaboration, however, design of the VEs should focus on providing learners a sense of 1) presence, 2) awareness and 3) belonging to the group ( Molka-Danielsen and Brask, 2014 ).

Thirdly, the literature reviewed suggests that VRCL environments are effective spaces to support multi- and interdisciplinary learning and collaboration. The ability to customize VEs, adapting to suit users’ needs, prevents them from being restricted to just a single specific subject field. This in turn allows educators to change the environments to accommodate many different subject fields and topics so as to make sure that learners from different backgrounds can collaborate with each other undisturbed ( Bilyatdinova et al., 2016 ). Moreover, it seems that VRCL environments made some of the literature studies reviewed realize the importance of interdisciplinary collaboration in the learning process ( Franco et al., 2006 ; Nadolny et al., 2013 ).

The fourth affordance identified might be an unsurprising but nonetheless important one: VRCL seems to be a tool for the development of social skills. While identity construction and projection through virtual embodiments can be complex for learners (depending on their technical skills), VRCL is found to facilitate social presence and foster socialization ( Edirisingha et al., 2009 ). VRCL’s customizability allows learners to integrate personal preferences and identity expressions into processes inside the environment (e.g., through their virtual embodiments), in turn mediating identity and norm construction for real-life social settings ( Ke and Lee, 2016 ). Vital social skills, such as the ability to identify and manipulate basic emotional states, can be taught and trained using VEs, improving learners’ socialization, communication skills and emotional intelligence ( López-Faican and Jaen, 2020 ). Learners’ prior experience with VEs, however, should not be underestimated, as a difference in familiarity with VRCL environments has been shown to impact collaboration ( Bluemink et al., 2010 ).

Fifth, VEs appear fitting for CL-related learning paradigms and educational approaches. Some studies specifically focus on examining to what degree VRCL environments are applicable to paradigms such as constructivism, socio-constructivism and constructionism (e.g., Girvan and Savage, 2010 ; Pellas et al., 2013 ; Abdullah et al., 2019 ), concluding that these indeed go well together. Other studies, however, focus on theories and methods commonly associated with these paradigms. In particular, experiential learning and PBL seem appropriate for VRCL environments. VEs allow for safe, consequence-free learning for exploring, experiencing and practicing without any real-life risks ( Cheong et al., 2015 ; Le et al., 2015 ), making it suitable for experiential learning. Moreover, VRCL’s immersive qualities seem to support and even elevate experiential learning strategies such as roleplay and improvisation, providing learners close to real-world experiences in a controlled environment ( Jarmon et al., 2008 ; Ashley et al., 2014 ). In the case of PBL, each individual learner can use different tools inside VRCL environments to illustrate and represent ideas and suggestions to the rest of the group. Considering that VEs seem great tools for conceptual learning because of their customizability and visual nature ( Brna and Aspin, 1998 ; Griol et al., 2014 ), learners can use these features to explain their point of view in ways that they otherwise could not. As a result, learners appear to become more active and effective in sharing ideas, joint problem solving and the co-construction of mental models when working in groups inside VRCL environments ( Rogers, 2011 ; Hwang and Hu, 2013 ).

Returning to the topic of disparity between HMD and non-HMD VR represented in the reviewed literature, as well as both being discussed as one and the same “Virtual Reality”, an important question to ask is whether the affordances identified here are transferable between the two. HMD and non-HMD VR differ in several ways: they are interacted with differently, face different obstacles when applied to education and appear to have different learning outcomes based on different educational settings.

With the definitions of VEs and VR as given by Girvan (2018) as a frame of reference, however, an answer can be given regarding the transferability of these affordances between HMD and non-HMD VR. Both HMD and non-HMD VR should be considered tools, technical systems through which users can virtually enter VEs, i.e., shared simulated spaces in which they can interact with the environment as well as each other. As such, the affordances described in this paper do not revolve around the tools used, but that which they provide access to: the VRCL environments. Simultaneously, which tool is used to access these VRCL environments can in turn affect both the interaction and the outcome of users’ experiences with VEs. For example, HMD VR might offer more effective development of social skills compared to non-HMD VR, considering the former provides a higher sense of embodiment and, in extension, more intuitive and expansive methods of expression. If, however, cognitive learning outcomes are the most important educational objective, non-HMD VR could be a better option, considering HMD VR’s tendency to cause affective and cognitive distractions. This, then, reflects the aforementioned statement regarding HMD VR being a benefit or a burden. While affordances of VRCL environments apply to both HMD and non-HMD VR, the effect of these affordances depend on 1) the goals, 2) the setting and 3) which affordances of VRCL are most vital to the first two elements. As such, the choice between non-HMD VR and HMD VR should be made depending on those three elements.

5 Conclusion and future research

With current research on the topic being scarce while the demand for remote collaboration and distance learning keeps increasing, this literature review intends to study how VR has been (and can be) used to support and enhance CL. To achieve this, it attempts to answer four research questions regarding prior research on VRCL: what skills and competences have been trained with VRCL and what does VRCL provide in these scenarios? To what educational domains has VRCL been applied and why? What systems have been used for VRCL? And what empirical knowledge has been established regarding VRCL?

This paper identifies five types of skills and competences commonly trained with the use of VRCL. Furthermore, a number of features and design principles are identified in terms of what these environments should offer for these skills to be developed. Educational fields and domains appear to be interested in VRCL because of a desire to innovate, to form communities, to support remote collaboration and to enhance socialization skills of learners. In terms of technology, systems used for VRCL-related purposes appear to predominantly focus on monitor-based (non-HMD) VR and mouse-and-keyboard controls, contrasting what VR is commonly associated with (e.g., HMD VR, specialized controls involving gaze control and positional tracking). This study perceives a general optimism present in the literature reviewed regarding the use of VR to support and enhance CL in learners. Additionally, a number of affordances of VRCL are described, though it is of importance to note that these affordances could differ in strength depending on which type of VR (i.e., non-HMD or HMD) is used.

While the literature on VRCL reviewed for this paper is diverse, it suggests that Virtual Reality can be an effective tool for supporting and enhancing Collaborative Learning. This diversity, however, also highlights that pedagogies of VRCL are lacking, with studies showing many different and contrasting approaches to applying VR to their respective fields for the support of CL. In order to see VR become more adopted as an educational tool for collaborative purposes, pedagogies should be clearly structured, highlighting similarities and differences in regards to both the technologies used and the domains they are used in. As such, this paper proposes a number of suggestions for future research. First, the difference between hardware used in the literature reviewed and the state-of-the-art of VR suggests that further examination of differences between non-HMD and HMD VRCL, both in terms of affordances as well as challenges and obstacles, could lead to a better understanding of VRCL’s potential. Second, despite the advantages VR has for development in affective and psychomotor skills, the scientific literature on VRCL shows only minor focus on these domains. This study argues that CL would benefit from both these domains being featured more prominently and as such encourages more research into these matters. Third, this paper suggests that research into VRCL focuses on using study designs and evaluation methods that are less frequently (or barely) featured in the reviewed literature. While the repeated and dominant use of pre-experimental study design is understandably meant to identify the potential behind the technology, the domain of VRCL (and, in extension, research on VR in education) would benefit from more true experimental design. Additionally, considering that the use of physiological data for evaluation methods appears to be unexplored terrain, this paper suggests that future research into VRCL implements these types of methods.

Author contributions

NvM: Main author VvW: Co-author and coder W-PB: Co-author and supervisor MS: Co-author and supervisor. All authors contributed to the article and approved the submitted version.

This project has been funded by the Leiden-Delft-Erasmus Centre for Education and Learning (LDE-CEL).

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Supplementary material

The supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/frvir.2023.1159905/full#supplementary-material

Supplementary Appendix A | List of all articles included.

Supplementary Appendix B1 | Results of agreement between first author and five additional coders on in- and exclusion criteria.

Supplementary Appendix C | Explanation/motivation behind Taxonomy.

Supplementary Appendix D1 | Results of agreement between first author and coder on use of taxonomy’s first category (Education).

Supplementary Appendix D2 | Results of agreement between first author and coder on use of taxonomy’s second category (System).

Supplementary Appendix D3 | Results of agreement between first author and coder on use of taxonomy’s third category (Evaluation).

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Keywords: virtual reality, collaborative learning, virtual reality education, collaborative virtual environment, virtual reality and collaborative learning, collaborative virtual reality, collaborative virtual reality systems, educational technologies

Citation: van der Meer N, van der Werf V, Brinkman W-P and Specht M (2023) Virtual reality and collaborative learning: a systematic literature review. Front. Virtual Real. 4:1159905. doi: 10.3389/frvir.2023.1159905

Received: 06 February 2023; Accepted: 02 May 2023; Published: 19 May 2023.

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Copyright © 2023 van der Meer, van der Werf, Brinkman and Specht. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Nesse van der Meer, [email protected]

• Research article
• Open access
• Published: 23 October 2017

The acceptance and use of a virtual learning environment in higher education: an empirical study in Turkey, and the UK

• Özlem Efiloğlu Kurt 1 &
• Özhan Tingöy 2  

International Journal of Educational Technology in Higher Education volume  14 , Article number:  26 ( 2017 ) Cite this article

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This study evaluated the acceptance and use of a virtual learning environment in higher education by using the unified theory of acceptance and use of technology (UTAUT) model. Study data were collected by means of a questionnaire form, completed by 1032 students receiving undergraduate education in Turkey and the United Kingdom, who currently use similar virtual learning environments. The role of performance expectancy, effort expectancy, social influence and facilitating conditions were evaluated and tested for both countries. The study results demonstrated that the behavioral intention and use behavior regarding the utilization of a virtual learning environment in higher education differed between the two countries, and that the level of impact of the factors that shape behavioral intention and use behavior also differed from one factor to another.

Significance statement

While the fast progress in technology has been continuing, the transfer of improved technologies into different application fields has become a current issue. In parallel with the accelerated technological innovations, the utilization of technology in educational processes has also increased. Hence, the studies focusing on the acceptance and utilization of these technologies particularly by students have come into prominence. The key motivation of the present study, which investigates the different models dealing with the acceptance and utilization of information systems in the literature, is to determine the student acceptance and utilization of a virtual learning system based on a pre-tested model. Building on similar virtual learning systems in two public universities-one is in Turkey and the other is in the UK- this research aims to reveal the students’ intentions to utilize the system and also determine similarities and differences in their behaviours in using the system.

Introduction

Parallel to the rapid developments and changes in information technologies, the application of these technologies in new areas has been a subject of considerable interest in the literature. There are numerous studies focusing on the acceptance and use of information technologies (Fishbein & Ajzen, 1975 ; Davis, Bagozzi, & Warshaw, 1989 ; Taylor & Todd, 1995 ; Venkatesh, Morris, Davis, & Davis, 2003 ; Sun & Zhang, 2006 ; Al-Gahtani, Hubona, & Wang, 2007 ; Lee, Choi, Kim, & Hong, 2007 ; Im, Hong, & Kang, 2011 ). These studies indicate that there are various important factors that affect the acceptance and use of information technologies by individuals belonging to different countries and cultures. These studies that began in the 1980s first made use of the Technology Acceptance Model (TAM), which is based on different social and behavioral theories, and which was gradually revised and further developed until it was finalized as the Unified Theory of Acceptance and Use of Technology (UTAUT). Although these theories have been tested in many different countries, the number of comparative studies between them is still very limited. The present study utilizes the UTAUT, an up-to-date and highly descriptive model, to demonstrate the acceptance and use of a virtual learning environment in higher education within the context of two countries (Turkey and the United Kingdom), and thereby seeks to contribute to the existing literature. In this context, the study first describes in detail the UTAUT model by providing comprehensive information on the models relating to the use and acceptance of technology, and then describes the relationships between these models. After providing information regarding the study sample and scales, the study hypotheses were tested, and the results were discussed in detail.

Literature review

As information technologies become more widespread around the world and in every possible area of use, there has also been a growing interest on how technology is perceived, accepted and used by individuals. Whether technologies that originate from a single centre will be equally accepted by different societies with different beliefs, values, approaches, and even physical characteristics, and whether these technologies will be used with the same content and in the same function across different societies and present satisfying ergonomics, are important questions that are subject to considerable debate. There are various studies in the literature attempting to answer these questions, which have mainly focused on behavioral models. In this context, a number of different models regarding the acceptance and use of technology have been proposed and tested. Among these different models, the ones most frequently used within the frame of various studies are the Theory of Reasoned Action, the Theory of Planned Behavior, the Innovation Diffusion Theory, and the Unified Theory of Acceptance and Use of Technology (Raaij & Schepers, 2008 ; Martinez-Torres et al., 2008 ; Wang, Wu, & Wang, 2009 ; Al-Senaidi, Lin, & Poirot, 2009 ; Premkumar, Ramamurthy, & Liu, 2008 ; Usluel, Aşkar, & Baş, 2008 ). The first model on the acceptance of technology to be developed in the literature is the Theory of Reasoned Action (TRA). Initially developed in 1980, it has been revised many times since, eventually becoming its current and most comprehensive version in 2003, as the UTAUT.

One of the most fundamental theoretical models in the literature concerning the acceptance of technology is the TRA developed in 1980 by Icek Ajzen and Martin Fishbein (Sheppard, Hartwick, & Warshaw, 1988 , p. 325). The foundations of this theory are based on social psychology. The theory is also an extension of Dulany’s Theory of Propositional Control. Dulany ( 1967 ) argued that an individual’s behavior is the product of his/her behavioral intention (Ajzen & Fishbein, 1969 , p. 400; Ajzen & Fishbein, 1970 , p. 466). According to the TRA, the behaviors of individuals are influenced by their attitudes towards the outcomes of their behaviors, as well as the opinions of other individuals within their social environment. According to Ajzen and Fishbein, the TRA is a psychological process model that mediates the observed relationship between behavior and attitude (Ajzen & Madden, 1986 ). The Theory of Planned Behavior (TPB), which is based on the TRA, is built on the assumption that humans generally act in a rational way by taking all available information into account and observing the consequences of their behaviors. TPB was put forward by Ajzen in 1985 as an extension of the TRA that also sought to resolve its shortcoming (Ajzen, 1985 p. 11; Ajzen & Madden, 1986 , p. 456). According to the TPB, an individual’s intention in performing or avoiding a certain behavior is the most important determinant of whether that behavior is exhibited (Ajzen, 2005 , p. 117). The Innovation Diffusion Theory (IDT), which is a sociology-based theory, traces its roots back to the 1960s (Rogers & Shoemaker, 1971 ; Tornatzky & Klein, 1982 , p. 29). The concept of diffusion has been extensively studied in the fields of sociology, economy, politics, and communication. These studies are based on Tarde’s work entitled “The Laws of Imitation,” published in 1903. Roger’s work entitled “Diffusion of Innovations,” which was first published in 1962, includes citations from Tarde. Studies on diffusion have been conducted in many different fields such as agricultural applications, technology, reproduction control methods, politics, and political reforms with the aim of defining the process, principles, and components of diffusion under different circumstances (Wejnert, 2002 , p. 298). IDT was put forward by Everett M. Rogers to examine the factors that influence the diffusion of innovation by taking into account the perception of individuals, as well, and to analyze how innovation (or the “new”) can be disseminated in social systems through communication. In other words, IDT evaluates the factors that affect diffusion by taking individual perceptions into consideration, and examines how innovation is diffused through communication processes within social systems.

The Technology Acceptance Model (TAM), which is one of the most widely used models in the literature, was first proposed by Fred D. Davis in 1985 within the frame of his doctorate thesis as a model for testing and developing user acceptance of computer-based information systems (Davis, 1985 ). The aim of this model is to form a theoretical basis for explaining within a broad context the determinants involved in the acceptance of computer technologies, and the end user behaviors towards computer technologies. The model has found widespread acceptance by researchers and users, since it not only permits the prediction of the reasons why a certain system may fail to find acceptance, but also assists in explaining these reasons and determining corrective action (Srite, Thatcher, & Galy, 2008 , p. 3). According to Davis et al. ( 1989 ), p. 985, the purpose of the model is to provide a general model for predicting and explaining the use of communication technologies. TAM explains the beliefs, behaviors, and intentions of users with regards to communication technologies based on a theoretical construct. TAM assumes that the acceptance of technologies by individuals is primarily dictated by two factors, which are the perceived usefulness and perceived ease of use (Davis, 1989 , p. 320; Lee, Kozar, & Larsen, 2003 , p. 752). Perceived usefulness and perceived ease of use with computer-based information systems have also been subject to considerable study by management and behavioral researchers (Schewe, 1976 , p. 577; Robey, 1979 , p. 527; Davis, 1989 , p. 319). In addition to the TAM he proposed, Davis ( 1985 ), p. 25 also described various external variables. The most commonly described variables in the literature include system quality, training, computer anxiety (Igbaria, Guimaraes, & Davis, 1995a ), self-efficacy (Igbaria, Livari, & Maragahh, 1995b ), enjoyment (Igbaria et al., 1995b ), compatibility (Chau & Hu, 2001 ), accessibility, support and experience (Chau, 1996 ). TAM is a theory that measures the users’ willingness and intent to use a certain technology based on certain factors. The theory has been occasionally criticized for being limited in scope, and researchers have attempted to improve the theory’s descriptive power by adding different elements. However, despite all the criticism it has drawn, TAM has become one of the most valid models in the literature evaluating the acceptance of technology at an individual level, and is one of the most widely accepted behavioral models in the field of information technologies (Legris, Ingham, & Collerette, 2003 , p. 202; McCoy, Galetta, & King, 2007 , p. 81; Turan & Çolakoğlu, 2008 , p. 112). Since it was first developed, TAM underwent various changes. As such, different alternatives of the model were developed, and based on the criticism it received, various studies were conducted to reduce the limitations of the model. In 2000, Venkatesh and Davis developed the Technology Acceptance Model 2 (TAM2), which, in addition to being a synthesis of previous studies on the TAM, sought to remedy some of the criticized aspects of the model. The model clearly takes into account the external variables of perceived usefulness and perceived ease of use. In this context, Venkatesh and Davis defined the external variables of perceived usefulness, such as social effect and cognitive tools. When recent developments are considered, it is possible to see that TAM has constantly evolved since it was first proposed. As a result of this evolution, TAM was followed by TAM2, and later by Technology Acceptance Model 3 (TAM3) in the literature (Venkatesh & Davis, 2000 ; Venkatesh & Bala, 2008 ).

To further build upon the progress made with previous studies on the TAM, Venkatesh et al. ( 2003 ) developed the UTAUT model. The UTAUT focuses on the intent to use and the use behavior of users towards information technologies, placing emphasis on four main determinants of the intention to use and use behavior (Venkatesh et al., 2003 ). These determinants include performance expectancy, effort expectancy, social influence, and facilitating conditions Performance expectancy, which is the first component of the UTAUT, can be defined as the individual’s beliefs regarding the benefit he/she will draw from using a system. Effort expectancy can be defined as the ease of using a particular system. Social influence can be defined as the importance an individual accords to the opinions of other regarding his/her use of a new system. Studies in the literature emphasize that performance expectancy (Venkatesh et al., 2003 ; Al-Gahtani et al., 2007 ; Taiwo & Downe, 2013 ; Kaba & Touré, 2014 ), effort expectancy (Venkatesh et al., 2003 ; Chiu & Wang, 2008 ; Diño & de Guzman, 2015 ), and social influence (Venkatesh et al., 2003 ; Taiwo & Downe, 2013 ) are important factors in predicting behavioral intention. The hypotheses proposed in this study concerning the factors that affect behavioral can be listed as follows:

H1: Performance expectancy has a positive impact on behavioral intention.

H2: Effort expectancy has a positive impact on behavioral intention.

H3: Social influence has a positive impact on behavioral intention.

Facilitating conditions can be defined as the individual’s belief in the availability of the necessary organizational and technical infrastructure for enabling the use of a system. Studies in the literature emphasize that facilitating conditions have an effect on use behavior rather than behavioral intention (Venkatesh et al., 2003 ; Chiu & Wang, 2008 ; Wang & Shih, 2009 ; Taiwo & Downe, 2013 ). Similarly, another factor that affects use behavior is behavioral intention (Venkatesh et al., 2003 ; Lin & Anol, 2008 ; Zaremohzzabieh, Samah, Omar, Bolong, & Mohamed Shaffril, 2014 ; Hou, 2014 ). The hypotheses proposed in this study concerning the factors that affect use behavior can be listed as follows:

H4: Facilitating conditions have a positive impact on use behavior.

H5: The users’ behavioral intention has a positive impact on use behavior.

Various studies in the literature emphasize that there are differences between countries with regards to the acceptance and use of different technologies (Sun & Zhang, 2006 ; Lee et al., 2007 ; Al-Gahtani et al., 2007 ; Im et al., 2011 ). The hypotheses put forward in this study concerning the differences between countries can be listed as follows:

H6: There is a difference in VLE using intentions of users from two countries.

H7: There is a difference in VLE use behavior of users from two countries.

In the UTAUT model, the gender, age, experience, and willingness variables are used to describe the effect of performance expectancy, effort expectancy, social influence, and facilitating conditions on behavioral intention and use behavior (Venkatesh et al., 2003 ). There are various studies in the literature where these variables are not included into the study model (Sumak, Polančič, & Heričko, 2010 ; Im et al., 2011 ; Nistor, Lerche, Weinberger, Ceobanu, & Heymann, 2014 ; Magsamen-Conrad, Upadhyaya, Joa, & Dowd, 2015 ). As such, the effect of these variables was similarly not included into our study model. The study model used for testing the study hypotheses is shown in Fig.  1 .

Research Model

Data collection and study sample

The necessary data for testing the study hypotheses were collected using a questionnaire-based method. Previous studies on the acceptance of technology were examined in order to prepare questionnaire items for this study. When preparing the questionnaire, the dimensions and expressions used for describing the acceptance of technology were obtained from the study of Venkatesh et al. ( 2003 ), the developers of the UTAUT model. The questionnaire items were prepared in two languages, Turkish and English, and presented to the respondents in their respective languages. The questionnaire form prepared in English was reviewed by three native English speaking academicians, and the form was finalized based on the changes they recommended. A similar approach was followed for the Turkish questionnaire, which was also finalized based on the views of three academicians.

The study sample consisted of 522 undergraduate students at Leeds University in the United Kingdom using the Virtual Learning Environment (VLE), and 510 undergraduate students at Sakarya University in Turkey using the Educational Information System (EIS). The questionnaires completed by these students were included in the study analysis. The reason for choosing the University of Leeds for this study is for two reasons. First, the related technology has been frequently used at their undergraduate level education. Second is having access to the university during the data collection process. The rationale for choosing Sakarya University from Turkey is that they have more experience than many other Turkish universities in using information technologies for educational purpose and the virtual learning environment they use is quite similar with the one used in the University of Leeds.

Sample characteristics

The descriptive results of the study were evaluated by using the frequency distributions of the participants’ demographic characteristics. To ensure that the study results would better shed light on the analyses that will be performed in the following sections of the manuscript, the results for Turkey and the United Kingdom were divided and presented separately on the study tables.

Results and Discussion

Testing the validity and reliability of the scales.

Exploratory factor analysis (EFA) was performed for both data sets. The Alpha model was used for performing reliability analyses. Tables  1 and 2 summarizes the factor analysis performed on the data set from Turkey, and also show the factor loads of the questionnaire items and the Cronbach’s alpha coefficients of the variables. Cronbach’s alpha coefficients ranged between 0.82 and 0.90, indicating that the factor had fairly high reliability.

Table  3 illustrates the correlation coefficients between the variables in the samples from Turkey and the United Kingdom.

Testing of the hypotheses

A regression analysis was used to determine the effect of social influence, performance expectancy, effort expectancy, and facilitating conditions variables that were determined through factor analysis on the behavioral intention and use behavior variables. In accordance with the study model, multi-linear regression was applied to both data sets to observe the effect of performance expectancy, effort expectancy, and social influence on behavioral intention. In the model including the performance expectancy, effort expectancy, and social influence variables, the F value for the data set from Turkey was 107.509 with p  < 0.01, while the F value for the data set from the United Kingdom was 94.272 with p  < 0.01. Both F values were statistically significant within the frame of the regression model. An evaluation of the adjusted R 2 values indicated that the model explained 39% of the variance in the sample from Turkey, and 35% of the variance in the sample from the United Kingdom. Table  4 provides the beta coefficients and significance levels concerning the effect of the variables.

The effect of the performance expectancy on behavioral intention was significant in both the samples from Turkey (β = 0.217, p  < 0.01) and the United Kingdom (β = 0.379, p  < 0.01). This finding indicated the validity of the H1 hypothesis. A comparison of the results for both countries indicated that performance expectancy had a stronger effect on behavioral intention in the United Kingdom sample. An evaluation of the effect of effort expectancy on behavioral intention revealed a significant effect at a p level of 0.01 for the sample in Turkey (β = 0.246), and a significant effect at a p level of 0.05 for the sample in the United Kingdom (β = 0.089). These findings indicated the validity of the H2 hypothesis. An evaluation of the beta coefficients indicated that the effort expectancy variable had a more prominent effect on the sample in Turkey. The analysis results demonstrated that the social influence variable had a significant effect on behavioral intention in both the samples in Turkey (β = 0.301, p  < 0.01) and in the United Kingdom, thus indicating the validity of the H3 hypothesis. A comparison of the results for the samples in Turkey and the United Kingdom showed that in Turkey, social influence is the variable that has the most significant effect on the intention to use a new system.

In the following stage, a multi-linear regression analysis was used to test the H4 and H5 hypotheses. In the model where facilitating conditions and behavioral intention were included as independent variables, the data set for Turkey had an F value of 48.702 and a p value <0.01, while the data set for the United Kingdom had an F value of 54.347 and a p value <0.01. These results indicated that the regression models were statistically significant. Adjusted R 2 values, which indicate the explanatory power of the regression model, were 0.165 for the sample in Turkey, and 0.170 for the sample in the United Kingdom. This shows that the model explained 16% of the variance in the use behavior observed in the Turkey sample, and 17% of the variance in the use behavior observed in the United Kingdom sample. Table  5 provides the beta coefficients and significance levels concerning the effects of the variables.

According to the analysis results, the effect of the facilitating conditions variables on the use behavior was significant in both samples from Turkey (β = 0.377, p  < 0.01) and the United Kingdom (β = 0.097, p  < 0.05). This indicated the validity of the H4 hypothesis. An evaluation of the coefficients related to the effect of behavioral intention on use behavior revealed a significant effect for the sample in the United Kingdom (β = 0.372, p  < 0.01), while the same effect was not significant for the sample in Turkey ( p  > .05). For this reason, the H5 hypothesis was partially supported. An evaluation of the coefficients in Table  3 indicated a moderate and significant relationship between behavioral intention and use behavior for the sample in Turkey, and that when the facilitating conditions variable was added to the regression model, the strong effect of this variable masked the relationship between intention and behavior. A general evaluation of the regression model results indicated that in the sample from the United Kingdom, use behavior was predominantly determined by behavioral intention and consequently by performance expectancy, effort expectancy, and social influence – which are the antecedents of behavioral intention. In contrast, the sample in Turkey showed that facilitating conditions played a more predominant role than behavioral intention and its antecedents.

In the following stage, the T -test was applied to test the H6 and H7 hypothesis, which predicted differences with regards to the levels of behavioral intention and use behavior. The relevant results are shown in Tables  6 and 7 . According to these results, there was a significant difference ( p  < 0.01) between the mean behavioral intention for the sample in Turkey (mean = 3.99) and the mean behavioral intention for the sample in the United Kingdom (mean = 5.18), which supported the validity of the H6 hypothesis. A similarly significant difference was also identified between the mean values of the use behavior (Turkey sample mean = 3.69; United Kingdom sample mean = 5.18, p  < .01), thus confirming the validity of the H7 hypothesis. Although this was not required by any of the study hypotheses, a comparison was performed between behavioral intention and the antecedents of use behavior in order to obtain more detailed information. These relevant results are shown in Table  6 .

In the present-day world, where technology is rapidly developing and influencing every aspect of society, one of the most intensely studied subjects is the technology adoption. Technology adoption and use of technology is an essential subject for nearly all areas and actors, from private businesses to public institutions, and from the health sector to the education sector. Numerous studies have been conducted on this subject, and a number of models that mainly attempt to describe technology adoption on an individual basis have been developed. Various models were developed in the literature based on social and behavioral theories, starting with the TAM, which was theoretically revised and further developed to obtain the UTAUT. These models have sought to explain the diffusion of certain technologies and their related applications. In this study, which aimed to demonstrate the acceptance and use of a virtual learning environment in higher education through a comparative approach, a total of 1032 undergraduate students from two samples in Turkey and the United Kingdom were administered with a questionnaire asking them to assess the VLE used in their respective universities.

The study determined that the students’ use behavior and behavioral intention to use towards the VLE differed between the samples in Turkey and the United Kingdom. Students in the United Kingdom displayed a higher level of intention to use and use frequency than the students in Turkey. Although both the universities in Turkey and the United Kingdom (Sakarya University and Leeds University) began transitioning to virtual learning environments at approximately the same period, British students exhibited greater ease in adopting these systems, possibly because these students have better access to the internet and digital technology than Turkish students. Moreover, significant differences were clearly observed between the two countries not only in terms of the behavioral intention to use and use behavior, but also in terms of the relative effects of the factors that influence this intention and behavior.

Statistical analyses revealed that the effect of performance expectancy on behavioral intention was significant for both the samples in Turkey and the United Kingdom, although in the United Kingdom sample, performance expectancy had a relatively greater effect on behavioral intention. A more favorable student perception concerning the benefits of the VLE and its effect on their academic performance and grades was associated with a greater willingness to use this system. The effort expectancy was observed to have a greater effect on behavioral intention in the sample from Turkey. Turkish students placed greater importance on the ease of learning and using a system. On the other hand, for British students, it was noted that the ease of use of a system did not have a considerable effect on their intention to use. Similarly, in the sample from Turkey, social influence had a stronger effect on behavioral intention. In fact, social influence was identified in the sample from Turkey as the variable with the strongest effect on the intention to use. Having friends who thought that they should use the VLE, as well as having lecturers who assist with the use of the VLE, positively affected the students’ behavioural intention to use. In the sample from the United Kingdom, the factor with the strongest effect on behavioral intention was the students’ belief that the system would contribute to their academic performance.

It was observed that the antecedents of the use behavior exhibited different characteristics between the two samples. A general evaluation of the results from the two regression models used within the scope of the study showed that in the United Kingdom sample, use behavior was mainly influenced by behavioral intention and by performance expectancy, effort expectancy and social influence, which are the antecedents of behavioral intention. This means that individuals in this sample were more willing to use a technology depending on the extent the conditions became conducive for its use. However, one noteworthy aspect concerning this sample was that external supportive conditions had a greater explanatory power than behavioral intention. Concerning the sample from Turkey, the analysis revealed a weak relationship between intention and use behavior, which was rendered insignificant -or, in other words, masked - when the external factors that affect use were taken into account. This finding indicates that for Turkey, the most important factor or approach that needs be considered for promoting the use of virtual learning environments is increasing the amount of resources available for the use of these systems, and enhancing the level of knowledge concerning these systems. In addition to these analyses the differences between two countries in terms of behavioral intention and use behavior are tested and significant differences were observed. As a result, the UTAUT model is valid for a virtual learning environment both in Turkey and in the UK on the basis of the two universities.

Limitations and future directions

The study had a number of limitations. First of all, the study is based on the comparison of two countries, and attempts to describe the acceptance and use of a virtual learning environment in higher education exclusively within the context of these countries. However, both samples representing these two countries consisted exclusively of students from two universities, one in Turkey and the other in the United Kingdom. While both countries have a large number of universities that utilize virtual learning environments in higher education, time-and cost-related constraints limited the number of universities that could be accessed for the purposes of this study. For this reason, when evaluating the study results, it is important to bear in mind that these results are not generalizable to the entire university student population in both countries.

Despite the fact that the study was conducted in two countries that differ significantly from one another with respect to culture, culture in itself was not included as a factor into the study model. There is consequently a need for more comprehensive studies that take into account the effect of culture on the use of technology, and which examine the relationship between culture and technology use. We also believe that performing comparisons with countries in the same region as Turkey will yield interest results. In addition, based on the study findings regarding the virtual learning environments presented by Turkish universities, it might also be possible to evaluate and explain the acceptance and use of other technologies used in educational institutions. Furthermore, considering Turkey’s large geographical area, it might also be interesting to assess whether there is any variation in the acceptance and use of technologies in higher education with respect to region.

Abbreviations

The innovation diffusion theory

Motivational model

The model of personal computer utilization

Social cognitive theory

Technology Acceptance Model

Technology Acceptance Model 2

Technology Acceptance Model 3

The theory of planned behavior

Theory of reasoned action

• United Kingdom

Unified theory of acceptance and use of technology

Virtual learning environment

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Acknowledgements

We would like to thank the instructors at the University of Leeds and Sakarya University who helped us with data collection and all the students who participated in our survey.

Based on virtual learning systems which possess similar features, the research focuses on the system acceptance and utilization behaviours of the students from two public universities from Turkey and the UK through employing and pre-tested and accepted model in the literature. Hence, we believe that this study fits well into the aims and scope of International Journal of Educational Technology in Higher Education.

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ÖEK  is responsible for approximately 90% of the experimental work and paper authoring. Both authors read and approved the final manuscript.

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Computer Programming Department, Yalova University, Yalova Meslek Yüksekokulu, Baglarbasi M. Safranyolu C., 77100, Merkez, Yalova, Turkey

Özlem Efiloğlu Kurt

Informatics Department, Marmara University, Nişantaşı Kampüsü Büyükçiftlik Sokak No:6, Nişantaşı, 34365, Şişli, İstanbul, Turkey

Özhan Tingöy

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Efiloğlu Kurt, Ö., Tingöy, Ö. The acceptance and use of a virtual learning environment in higher education: an empirical study in Turkey, and the UK. Int J Educ Technol High Educ 14 , 26 (2017). https://doi.org/10.1186/s41239-017-0064-z

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Received : 22 March 2017

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DOI : https://doi.org/10.1186/s41239-017-0064-z

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• Unified theory of acceptance and use of technology (UTAUT)
• Technology adoption
• Virtual learning environment (VLE)

Capturing the benefits of remote learning

How education experts are applying lessons learned in the pandemic to promote positive outcomes for all students

Vol. 52 No. 6 Print version: page 46

• Schools and Classrooms

With schools open again after more than a year of teaching students outside the classroom, the pandemic sometimes feels like a distant memory. The return to classrooms this fall brings major relief for many families and educators. Factors such as a lack of reliable technology and family support, along with an absence of school resources, resulted in significant academic setbacks, not to mention stress for everyone involved.

But for all the downsides of distance learning, educators, psychologists, and parents have seen some benefits as well. For example, certain populations of students found new ways to be more engaged in learning, without the distractions and difficulties they faced in the classroom, and the general challenges of remote learning and the pandemic brought mental health to the forefront of the classroom experience.

Peter Faustino, PsyD, a school psychologist in Scarsdale, New York, said the pandemic also prompted educators and school psychologists to find creative new ways of ensuring students’ emotional and academic well-being. “So many students were impacted by the pandemic, so we couldn’t just assume they would find resources on their own,” said Faustino. “We had to work hard at figuring out new ways to connect with them.”

Here are some of the benefits of distance learning that school psychologists and educators have observed and the ways in which they’re implementing those lessons post-pandemic, with the goal of creating a more equitable, productive environment for all students.

Prioritizing mental health

Faustino said that during the pandemic, he had more mental health conversations with students, families, and teachers than ever. “Because COVID-19 affected everyone, we’re now having mental health discussions as school leaders on a daily and weekly basis,” he said.

This renewed focus on mental health has the potential to improve students’ well-being in profound ways—starting with helping them recover from the pandemic’s effects. In New York City, for example, schools are hiring more than 600 new clinicians, including psychologists , to screen students’ mental health and help them process pandemic-related trauma and adjust to the “new normal” of attending school in person.

Educators and families are also realizing the importance of protecting students’ mental health more generally—not only for their health and safety but for their learning. “We’ve been seeing a broader appreciation for the fact that mental health is a prerequisite for learning rather than an extracurricular pursuit,” said Eric Rossen, PhD, director of professional development and standards at the National Association of School Psychologists.

As a result, Rossen hopes educators will embed social and emotional learning components into daily instruction. For example, teachers could teach mindfulness techniques in the classroom and take in-the-moment opportunities to help kids resolve conflicts or manage stress.

Improved access to mental health resources in schools is another positive effect. Because of physical distancing guidelines, school leaders had to find ways to deliver mental health services remotely, including via online referrals and teletherapy with school psychologists and counselors.

Early in the pandemic, Faustino said he was hesitant about teletherapy’s effectiveness; now, he hopes to continue offering a virtual option. Online scheduling and remote appointments make it easier for students to access mental health resources, and some students even enjoy virtual appointments more, as they can attend therapy in their own spaces rather than showing up in the counselor’s office. For older students, Faustino said that level of comfort often leads to more productive, open conversations.

Autonomy as a key to motivation

Research suggests that when students have more choices about their materials and activities, they’re more motivated—which may translate to increased learning and academic success. In a 2016 paper, psychology researcher Allan Wigfield, PhD, and colleagues make the case that control and autonomy in reading activities can improve both motivation and comprehension ( Child Development Perspectives , Vol. 10, No. 3 ).

During the period of online teaching, some students had opportunities to learn at their own pace, which educators say improved their learning outcomes—especially in older students. In a 2020 survey of more than 600 parents, researchers found the second-most-valued benefit of distance learning was flexibility—not only in schedule but in method of learning.

In a recent study, researchers found that 18% of parents pointed to greater flexibility in a child’s schedule or way of learning as the biggest benefit or positive outcome related to remote learning ( School Psychology , Roy, A., et al., in press).

This individualized learning helps students find more free time for interests and also allows them to conduct their learning at a time they’re most likely to succeed. During the pandemic, Mark Gardner, an English teacher at Hayes Freedom High School in Camas, Washington, said he realized how important student-centered learning is and that whether learning happens should take precedence over how and when it occurs.

For example, one of his students thrived when he had the choice to do work later at night because he took care of his siblings during the day. Now, Gardner posts homework online on Sundays so students can work at their own pace during the week. “Going forward, we want to create as many access points as we can for kids to engage with learning,” he said.

Rosanna Breaux , PhD, an assistant professor of psychology and assistant director of the Child Study Center at Virginia Tech, agrees. “I’d like to see this flexibility continue in some way, where—similar to college—students can guide their own learning based on their interests or when they’re most productive,” she said.

During the pandemic, many educators were forced to rethink how to keep students engaged. Rossen said because many school districts shared virtual curricula during the period of remote learning, older students could take more challenging or interesting courses than they could in person. The same is true for younger students: Megan Hibbard, a teacher in White Bear Lake, Minnesota, said many of her fifth graders enjoyed distance learning more than in-person because they could work on projects that aligned with their interests.

“So much of motivation is discovering the unique things the student finds interesting,” said Hunter Gehlbach, PhD, a professor and vice dean at the Johns Hopkins School of Education. “The more you can facilitate students spending more time on the things they’re really interested in, the better.”

Going forward, Rossen hopes virtual curricula will allow students greater opportunities to pursue their interests, such as by taking AP classes, foreign languages, or vocational electives not available at their own schools.

Conversely, Hibbard’s goal is to increase opportunities for students to pursue their interests in the in-person setting. For example, she plans to increase what she calls “Genius Hours,” a time at the end of the school day when students can focus on high-interest projects they’ll eventually share with the class.

Better understanding of children's needs

One of the most important predictors of a child’s success in school is parental involvement in their education. For example, in a meta-analysis of studies, researchers linked parental engagement in their middle schoolers’ education with greater measures of success (Hill, N. E., & Tyson, D. F., Developmental Psychology , Vol. 45, No. 3, 2009).

During the pandemic, parents had new opportunities to learn about their kids and, as a result, help them learn. According to a study by Breaux and colleagues, many parents reported that the pandemic allowed them a better understanding of their child’s learning style, needs, or curriculum.

James C. Kaufman , PhD, a professor of educational psychology at the University of Connecticut and the father of an elementary schooler and a high schooler, said he’s had a front-row seat for his sons’ learning for the first time. “Watching my kids learn and engage with classmates has given me some insight in how to parent them,” he said.

Stephen Becker , PhD, a pediatric psychologist at Cincinnati Children’s Hospital Medical Center, said some parents have observed their children’s behavior or learning needs for the first time, which could prompt them to consider assessment and Individualized Education Program (IEP) services. Across the board, Gehlbach said parents are realizing how they can better partner with schools to ensure their kids’ well-being and academic success.

For example, Samantha Marks , PsyD, a Florida-based clinical psychologist, said she realized how much help her middle school daughter, a gifted and talented student with a 504 plan (a plan for how the school will offer support for a student’s disability) for anxiety, needed with independence. “Bringing the learning home made it crystal clear what we needed to teach our daughter to be independent and improve executive functioning” she said. “My takeaway from this is that more parents need to be involved in their children’s education in a healthy, helpful way.”

Marks also gained a deeper understanding of her daughter’s mental health needs. Through her 504 plan, she received help managing her anxiety at school—at home, though, Marks wasn’t always available to help, which taught her the importance of helping her daughter manage her anxiety independently.

Along with parents gaining a deeper understanding of their kids’ needs, the pandemic also prompted greater parent participation in school. For example, Rossen said his kids’ school had virtual school board meetings; he hopes virtual options continue for events like back-to-school information sessions and parenting workshops. “These meetings are often in the evening, and if you’re a single parent or sole caregiver, you may not want to pay a babysitter in order to attend,” he said.

Brittany Greiert, PhD, a school psychologist in Aurora, Colorado, says culturally and linguistically diverse families at her schools benefited from streamlined opportunities to communicate with administrators and teachers. Her district used an app that translates parent communication into 150 languages. Parents can also remotely participate in meetings with school psychologists or teachers, which Greiert says she plans to continue post-pandemic.

Decreased bullying

During stay-at-home orders, kids with neurodevelopmental disorders experienced less bullying than pre-pandemic (McFayden, T. C., et al., Journal of Rural Mental Health , No. 45, Vol. 2, 2021). According to 2019 research, children with emotional, behavioral, and physical health needs experience increased rates of bullying victimization ( Lebrun-Harris, L. A., et al., ), and from the U.S. Department of Education suggests the majority of bullying takes place in person and in unsupervised areas (PDF) .

Scott Graves , PhD, an associate professor of educational studies at The Ohio State University and a member of APA’s Coalition for Psychology in Schools and Education (CPSE), said the supervision by parents and teachers in remote learning likely played a part in reducing bullying. As a result, he’s less worried his Black sons will be victims of microaggressions and racist behavior during online learning.

Some Asian American families also report that remote learning offered protection against racism students may have experienced in person. Shereen Naser, PhD, an associate professor of psychology at Cleveland State University and a member of CPSE, and colleagues found that students are more comfortable saying discriminatory things in school when their teachers are also doing so; Naser suspects this trickle-down effect is less likely to happen when students learn from home ( School Psychology International , 2019).

Reductions in bullying and microaggressions aren’t just beneficial for students’ long-term mental health. Breaux said less bullying at school results in less stress, which can improve students’ self-esteem and mood—both of which impact their ability to learn.

Patricia Perez, PhD, an associate professor of international psychology at The Chicago School of Professional Psychology and a member of CPSE, said it’s important for schools to be proactive in providing spaces for support and cultural expression for students from vulnerable backgrounds, whether in culture-specific clubs, all-school assemblies that address racism and other diversity-related topics, or safe spaces to process feelings with teachers.

According to Rossen, many schools are already considering how to continue supporting students at risk for bullying, including by restructuring the school environment.

One principal, Rossen said, recently switched to single-use bathrooms to avoid congregating in those spaces once in-person learning commences to maintain social distancing requirements. “The principal received feedback from students about how going to the bathroom is much less stressful for these students in part due to less bullying,” he said.

More opportunities for special needs students

In Becker and Breaux’s research, parents of students with attention-deficit/hyperactivity disorder (ADHD), particularly those with a 504 plan and IEP, reported greater difficulties with remote learning. But some students with special learning needs—including those with IEPs and 504 plans—thrived in an at-home learning environment. Recent reporting in The New York Times suggests this is one reason many students want to continue online learning.

According to Cara Laitusis, PhD, a principal research scientist at Educational Testing Service ( ETS ) and a member of CPSE, reduced distractions may improve learning outcomes for some students with disabilities that impact attention in a group setting. “In assessments, small group or individual settings are frequently requested accommodations for some students with ADHD, anxiety, or autism. Being in a quiet place alone without peers for part of the instructional day may also allow for more focus,” she said. However, she also pointed out the benefits of inclusion in the classroom for developing social skills with peers.

Remote learning has improved academic outcomes for students with different learning needs, too. Marks said her seventh-grade daughter, a visual learner, appreciated the increase in video presentations and graphics. Similarly, Hibbard said many of her students who struggle to grasp lessons on the first try have benefited from the ability to watch videos over again until they understand. Post-pandemic, she plans to record bite-size lessons—for example, a 1-minute video of a long division problem—so her students can rewatch and process at their own rate.

Learners with anxiety also appreciate the option not to be in the classroom, because the social pressures of being surrounded by peers can make it hard to focus on academics. “Several of my students have learned more in the last year simply due to the absence of anxiety,” said Rosie Reid, an English teacher at Ygnacio Valley High School in Concord, California, and a 2019 California Teacher of the Year. “It’s just one less thing to negotiate in a learning environment.”

On online learning platforms, it’s easier for kids with social anxiety or shyness to participate. One of Gardner’s students with social anxiety participated far more in virtual settings and chats. Now, Gardner is brainstorming ways to encourage students to chat in person, such as by projecting a chat screen on the blackboard.

Technology has helped school psychologists better engage students, too. For example, Greiert said the virtual setting gave her a new understanding of her students’ personalities and needs. “Typing out their thoughts, they were able to demonstrate humor or complex thoughts they never demonstrated in person,” she said. “I really want to keep incorporating technology into sessions so kids can keep building on their strengths.”

Reid says that along with the high school students she teaches, she’s seen her 6-year-old daughter benefit from learning at her own pace in the familiarity of her home. Before the pandemic, she was behind academically, but by guiding her own learning—writing poems, reading books, playing outside with her siblings—she’s blossomed. “For me, as both a mother and as a teacher, this whole phenomenon has opened the door to what education can be,” Reid said.

Eleanor Di Marino-Linnen, PhD, a psychologist and superintendent of the Rose Tree Media School District in Media, Pennsylvania, says the pandemic afforded her district a chance to rethink old routines and implement new ones. “As challenging as it is, it’s definitely an exciting time to be in education when we have a chance to reenvision what schools have looked like for many years,” she said. “We want to capitalize on what we’ve learned.”

Further reading

Why are some kids thriving during remote learning? Fleming, N., Edutopia, 2020

Remote learning has been a disaster for many students. But some kids have thrived. Gilman, A., The Washington Post , Oct. 3, 2020

A preliminary examination of key strategies, challenges, and benefits of remote learning expressed by parents during the COVID-19 pandemic Roy, A., et al., School Psychology , in press

Remote learning during COVID-19: Examining school practices, service continuation, and difficulties for adolescents with and without attention-deficit/hyperactivity disorder Becker S. P., et al., Journal of Adolescent Health , 2020

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