The role and scope of gamification in education: A scientometric literature review
Bibliographic record
Abstract
Gamification - the use of game elements in non-game contexts - represents a promising solution to enhance motivation and engagement in education. Traditional lecture-based teaching has been increasingly viewed as insufficient for effective learning, promoting interest in gamified education to sustain student engagement and foster a positive learning environment. This study presents a comprehensive scientometric literature review on the use of gamification in education, analyzing 9163 manuscripts and over 300,000 references from the Scopus database. Through document co-citation analysis, author co-citation analysis, and keyword co-occurrence analysis, the review identifies the most influential publications, authors, and research trends shaping the field. The findings reveal a research trend that initially focused on game design and best practices but has shifted towards systematic literature reviews and evaluations of gamification's educational effectiveness. Six key research clusters emerged: gamified learning experience, student learning, K-12 education, science education, gamification effectiveness, and gaming elements. The study highlights the growing application of gamification in STEM and formal K-12 education, as well as the increasing relevance of online and personalized learning environments. The review also emphasizes significant research gaps, particularly concerning the long-term impact of gamification, the isolated effects of individual game elements, and the need for improved research methodologies. This review offers a research agenda for future studies, calling for more rigorous, context-sensitive research that better addresses the complexity of gamified learning environments.
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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 | 0.039 | 0.163 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.146 | 0.186 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| 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".