Virtual Reality (VR) Potential for Education in the Future: A Bibliometric Analysis
Bibliographic record
Abstract
Language learning apps, interactive multimedia, and virtual classrooms help students learn independently and differently, improving teaching and achievement. VR brings new scenarios and experiences to education. Many consider it a valuable learning tool. VR education can replace multimedia because it's affordable and improves critical thinking and community engagement. Education VR technology was bibliometrically analyzed from 2020 to 2024 using R Studio and Biblioshyni. VR education articles rose from 34 in 2020 to 172 in 2022. Educational and Information Technology published most VR articles, then Heliyon Journal. USAISR, FT Sam Houston, Wang Y, and Texas, USA are the top ten VR education authors. FT Sam Houston, USA, USAISR, and Texas had the most documents. The US had the most VR education articles from 2020 to 2024. China has 182 articles, Canada 84. Australia, Brazil, Germany, India, South Africa, Spain, and the Netherlands produced 80 articles. Human was the most-used VR term in education from 2020 to 2024, appearing 179 times. COVID-19 shows that time and space shouldn't limit research. VR was most cited in 44 education and IT articles. Chinese authors dominate VR research and US scientific publications, making VR media in education appealing in Indonesia
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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 | 0.010 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.155 | 0.202 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".