Educational virtual games in supporting SDG 4: Research trend in Scopus, Topic, and Novelty explored
Bibliographic record
Abstract
Quality education is one of the aspects targeted for development by the SDGs points. Virtual game education is one of the tools used to achieve the goals emphasized by the SDGs. This research aims to identify trends and contributions of virtual game education (VGE) in education. This research uses bibliometric analysis techniques sourced from the Scopus database. The software used to visualize existing data is VosViewer . Over the past five years, research on VGE has been steady, peaking in 2022 with the most significant number of documents. VGE research increased from 2019 to 2022 but decreased in 2023. VGE research has been published as conference papers with Springer as publisher. Canada and the US are countries that have made significant contributions to this research. Ten dominant subjects impact education by developing digital learning media technology. The research uses a combined approach (quantitativequalitative) in the data analysis. These things are closely related to SDG point 4 (quality of education). Research on similar topics can be further developed using data other than Scopus, such as WOS and Citespace , as tools for more interesting visualization compared to VOSviewer .
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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 | 0.016 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.066 | 0.126 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".