Head-Mounted Display Virtual Reality in Post-secondary Education and Skill Training
Bibliographic record
Abstract
Background: This review focused on how immersive head-mounted display virtual reality (VR) was used in post-secondary level education and skill training, with the aim to better understand its state of the art as found from the literature. While numerous studies describe the use of immersive VR within a specific educational setting, they are often standalone events not fully detailed regarding their curricular integration. This review aims to analyse these events, with a focus on immersive VR’s incorporation into post-secondary education. Objectives: O1) Review the existing literature on the use of immersive VR in post-secondary settings, determining where and how it has been used within each educational discipline. This criterion focused on literature featuring the use of immersive VR, due to its influence on a user’s perceived levels of presence and imagination. O2) Identify favourable outcomes from the use of immersive VR when compared to other learning methods. O3) Determine the conceptual rationale (purpose) for each implementation of immersive VR as found throughout the literature. O4) Identify learning theories and recommendations for the utilization of immersive VR in post-secondary education. Methods: A literature review was undertaken with searches of Education Research Complete, ERIC, MEDLINE, EMBASE, IEEE Xplore, Scopus and Web of Science: Core Collection to locate reports on the use of immersive VR in post-secondary curricula. Results: 119 articles were identified, featuring disciplines across Arts and Humanities, Health Sciences, Military and Aerospace, Science and Technology. 35 out of 38 experiments reported to have found a positive outcome for immersive VR, after being compared with a non-immersive platform. Each simulation’s purpose included one or more of the following designations: skill training, convenience, engagement, safety, highlighting, interactivity, team building and suggestion. Recommendations for immersive VR in post-secondary education emphasize experiential learning and social constructivist approaches, including student-created virtual environments that are mainly led by the students themselves under team collaboration. Conclusion: Immersive VR brings convenient, engaging and interactive alternatives to traditional classroom settings as well as offers additional capability over traditional methods. There is a diverse assortment of educational disciplines that have each attempted to harness the power of this technological medium.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".