An Immersive Virtual Learning Environment for the Development of Hard Skills: A Scientometric Analysis and Systematic Review
Bibliographic record
Abstract
A scientometric analysis and systematic review of an immersive virtual learning environment (IVLE) designed to improve the development of hard skills comprised this study. The review focused on the period between 2016 and 2023 and was conducted using the Scopus database. This study employed a scientometrics analysis and systematic review methodology. In this study, a comprehensive analysis was conducted on a corpus of 12 scholarly articles that have been published within the last 8 years. This study looked at the aggregate magnitude and trajectory of scholarly publications and looked at a number of trends in scholarly research, including annual reports, article counts, article distribution across different sources, finding the most useful sources, keyword analysis, looking into the best collaboration groups, looking at how themes have changed over time, judging contributions, and practical implications. The primary findings regarding the data and document types indicated that a total of 12 articles were published across 10 different sources within the timeframe of 2016 to 2023. The research conducted on IVLE for the development of hard skills exhibited fluctuations in its pace, alternating between deceleration and positive acceleration. The growth rate of the articles peaked in 2022. The primary publication with the greatest of source is “Lecture Notes in Networks and Systems.” Keyword Plus growth rate for terms like “virtual reality,” “e-learning,” and “teaching” The nations exhibit a high degree of collaboration in the field of research. The findings indicated that the authors hailing from Mexico and Colombia exhibited the greatest frequency (n = 2). The thematic evolution of the study indicated two significant advancements: firstly, the emergence of e-learning studies that have propelled the field of e-learning, and secondly, the integration of virtual reality into e-learning practices. The classification of applications encompasses various components: title, solution, learning strategy, and learning tasks.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Systematic review | low |
| gpt | Bibliometrics Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | high |
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 | 0.061 | 0.200 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.078 | 0.055 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".