Does self-modulated learning vs. algorithm-regulated learning of dermatology morphology affect learning efficiency of medical students?
Bibliographic record
Abstract
BACKGROUND: Deliberate practice is an important method of skill acquisition and is under-utilized in dermatology training. We delivered a dermatologic morphology training module with immediate feedback for first year medical students. Our goal was to determine whether there are differences in accuracy and learning efficiency between self- regulated and algorithm-regulated groups. METHODS: First year medical students at the University of Calgary completed a dermatologic morphology module. We randomly assigned them to either a self-regulated arm (students removed cases from the practice pool at their discretion) or an algorithm-regulated arm (an algorithm determined when a case would be removed). We then administered a pre-survey, pre-test, post-test, and post-survey. Data collected included mean diagnostic accuracy of the practice sessions and tests, and the time spent practicing. The surveys assessed demographic data and student satisfaction. RESULTS: Students in the algorithm-regulated arm completed more cases than the self-regulated arm (52.9 vs. 29.3, p<0.001) and spent twice as much time completing the module than the self-regulated participants (34.3 vs. 17.0 min., p<0.001). Mean scores were equivalent between the algorithm- and self-regulated groups for the pre-test (63% vs. 66%, n = 54) and post-test (90% vs. 86%, n = 10), respectively. Both arms demonstrated statistically significant improvement in the post-test. CONCLUSION: Both the self-regulated and algorithm-regulated arms improved at post-test. Students spent significantly less time practicing in the self-directed arm, suggesting it was more efficient.
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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 | 0.003 | 0.013 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".