A Systematic Overview of Reviews of the Use of Immersive Virtual Reality in Higher Education
Bibliographic record
Abstract
Objectives: Immersive virtual reality (IVR) provides opportunities to learn within a nonphysical, digital world. The purpose of this critical review was to examine published systematic reviews regarding the benefits and challenges of IVR in higher education to inform best practices. Method: We followed the Preferred Reporting Items for Overviews of Reviews (PRIOR) to ensure transparency and to afford an evidence-based approach for synthesizing insights from a broad range of research. We analyzed and synthesized 10 reviews that include 332 studies with over 9,878 participants, following an integrated synthesis design process using thematic analysis and emergent coding. Results: Results confirmed the various benefits and challenges of IVR. The benefits include improved student learning and behaviours, while challenges include technology issues, behaviours that inhibit learning, and learning how to use IVR. Conclusions: IVR holds considerable potential in disciplines requiring practical applications such as simulation-based training and testing. However, further research into contexts such as participant age, gender, instructional design or learning theory, and longitudinal study is required. Finally, higher education stakeholders will benefit from budgeting time and costs, aligning IVR use with real-world applications, maintaining an adaptive mindset, and developing scaffolded instructional design. Implications for Theory and/or Practice: The primary benefits of student learning through IVR include enhanced skill acquisition, experiences, and learning outcomes. In addition, while immersive platforms housed in static rooms may present financial challenges, the emergence of—and increased investment into—untethered headsets and haptic controllers can reduce operational costs and increase student access to high-quality learning experiences.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.026 | 0.135 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.021 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".