The effect of educational games on medical students’ learning outcomes: A systematic review: BEME Guide No 14
Bibliographic record
Abstract
BACKGROUND: An educational game is 'an instructional method requiring the learner to participate in a competitive activity with preset rules.' A number of studies have suggested beneficial effects of educational games in medical education. AIM: The objective of this study was to systematically review the effect of educational games on medical students' satisfaction, knowledge, skills, attitude, and behavior. METHODS: We used the best evidence medical education (BEME) collaboration methods for conducting systematic reviews. We included randomized controlled trials (RCT), controlled clinical trials, and interrupted time series. Study participants were medical students. Interventions of interest were educational games. RESULTS: The title and abstract screening of the 1019 unique citations identified 26 as potentially eligible for this article. The full text screening identified five eligible papers, all reporting RCTs with low-to-moderate methodological quality. Findings in three of the five RCTs suggested but did not confirm a positive effect of the games on medical students' knowledge. CONCLUSION: The available evidence to date neither confirm nor refute the utility of educational games as an effective teaching strategy for medical students. There is a need for additional and better-designed studies to assess the effectiveness of these games and this article will inform this research.
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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 | 0.012 | 0.046 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.011 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".