Gamification in Action: Theoretical and Practical Considerations for Medical Educators
Bibliographic record
Abstract
Gamification involves the application of game design elements to traditionally nongame contexts. It is increasingly being used as an adjunct to traditional teaching strategies in medical education to engage the millennial learner and enhance adult learning. The extant literature has focused on determining whether the implementation of gamification results in better learning outcomes, leading to a dearth of research examining its theoretical underpinnings within the medical education context. The authors define gamification, explore how gamification works within the medical education context using self-determination theory as an explanatory mechanism for enhanced engagement and motivation, and discuss common roadblocks and challenges to implementing gamification.Although previous gamification research has largely focused on determining whether implementation of gamification in medical education leads to better learning outcomes, the authors recommend that future research should explore how and under what conditions gamification is likely to be effective. Selective, purposeful gamification that aligns with learning goals has the potential to increase learner motivation and engagement and, ultimately, learning. In line with self-determination theory, game design elements can be used to enhance learners' feelings of relatedness, autonomy, and competence to foster learners' intrinsic motivation. Poorly applied game design elements, however, may undermine these basic psychological needs by the overjustification effect or through negative effects of competition. Educators must, therefore, clearly understand the benefits and pitfalls of gamification in curricular design, take a thoughtful approach when integrating game design elements, and consider the types of learners and overarching learning objectives.
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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 | 0.014 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.016 |
| Scholarly communication | 0.014 | 0.012 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.012 | 0.016 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".