Gamification in Online Education: A Visual Bibliometric Network Analysis
Bibliographic record
Abstract
This study applies bibliometric and network analysis methods to map the literature-based landscape of gamification in online distance learning. Two thousand four hundred and nineteen publications between 2000 and 2023 from the Scopus database were analyzed. Leading journals, influential articles, and the most critical topics on gamification in online training were identified. The co-authors’ analysis demonstrates a considerable rise in the number of nations evaluating research subjects, indicating increasing international cooperation. The main contributors are the United States, the United Kingdom, China, Spain, and Canada. The co-occurrence network analysis of keywords revealed six distinct research clusters: (i) the implementation of gamification in various learning contexts, (ii) investigating the application of gamification in student education to promote the use of electronic learning, (iii) utilizing artificial intelligence tools in online learning, (iv) exploring educational technologies, (v) developing strategies for creating a playful learning environment, and (vi) understanding children’s learning processes. Finally, an analysis of the most cited articles identified three research themes: (a) gamification-based learning platforms, (b) measurement of users’ appreciation and satisfaction, and (c) 3D virtual immersive learning environments. This study contributes to the subject discipline by informing researchers about the latest research trends in online education gamification and identifying promising research directions.
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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 | 0.008 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.141 | 0.152 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| 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".