A Systematic Review of Virtual Reality Features for Skill Training
Bibliographic record
Abstract
Abstract The evolving complexity of Virtual Reality (VR) technologies necessitates an in-depth investigation of the VR features and their specific utility. Although VR is utilized across various skill-training applications, its successful deployment depends on both technical maturity and context-specific suitability. A comprehensive understanding of advanced VR features, both technical and experiential, their prospective impact on designated learning outcomes, and the application of appropriate assessment methodologies is essential for the effective utilization of VR technologies. This systematic literature review explored the inherent associations between various VR features employed in professional training environments and their impact on learning outcomes. Furthermore, this review scrutinizes the assessment techniques employed to gauge the effects of VR applications in various learning scenarios. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method was used to systematically select 50 empirical VR studies sourced from three (03) academic databases. The analysis of these articles revealed complex, context-dependent relationships between VR features and their impact on professional training, with a pronounced emphasis on skill-based learning outcomes over cognitive and affective ones. This review also highlights the predominantly subjective nature of the assessment methods used to measure the effects of VR training. Additionally, the findings call for further empirical exploration in novel skill training contexts encompassing cognitive and affective learning outcomes, as well as other potential external factors that may influence learning outcomes in VR.
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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 | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".