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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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, unvalidatedMachine predicted; a candidate call from one teacher head, 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".