Gamification in Action: Theoretical and Practical Considerations for Medical Educators
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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 it