The Active Methodology of Gamification to Improve Motivation and Academic Performance in Educational Context: A Meta-Analysis
Bibliographic record
Abstract
Gamification is an active methodology that involves using game elements in a non-game situation. Gamification has shown to increase motivation and learning in different types of academic students; however, the educational literature shows inconclusive findings. The aim of this meta-analysis is to analyse the effects of gamification on motivation and academic performance in an educational context. Sixteen interventions carried out between January 2010 and the end of January 2022 were retrieved from the databases and included in this meta-analysis. One study was carried out in Primary school, three in Secondary school, and twelve in universities. Four papers analysed the effects on motivation, five on academic performance and seven on both. Results showed that gamification could increase the motivation (SMD = 0.51; 95% CI [0.29, 0.73]; I2 59%; p < 0.00001) and academic performance (SMD = 0.89; 95% CI [0.45, 1.32]; I2 90%; p < 0.0001) in all the educational stages. The implications of including gamification programmes in the educational context are discussed.
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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 | 0.026 | 0.054 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.042 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".