The Effect of Spaced Repetition on Learning and Knowledge Transfer in a Large Cohort of Practicing Physicians
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Bibliographic record
Abstract
PURPOSE: Spaced repetition is superior to repeated study for learning and knowledge retention, but literature on the effect of different spaced repetition strategies is lacking. The authors evaluated the effects of different spaced repetition strategies on long-term knowledge retention and transfer. METHOD: This prospective cohort study, conducted from October 1, 2020, to July 20, 2023, used the American Board of Family Medicine Continuous Knowledge Self-Assessment (CKSA) to assess learning and knowledge transfer of diplomates and residents. Participants were randomized to a control group or 1 of 5 spaced repetition conditions during 5 calendar quarters (January 1, 2021, to March 31, 2022). Participants in the spaced repetition groups received 6 repeated questions once or twice. Incorrectly but confidently answered questions were prioritized for repetition, with decreasing priority for questions answered incorrectly with lesser confidence. All participants received 6 rewritten questions corresponding to their initial questions chosen for repetition in quarter 10 (second quarter of calendar year 2023). RESULTS: A total of 26,258 family physicians or residents who completed the CKSA in the baseline period were randomized. Spaced repetition was superior to no spaced repetition for learning at quarter 6 (58.03% vs 43.20%, P < .001, Cohen d = 0.62) and knowledge transfer at quarter 10 (58.33% vs 52.39%, P < .001, Cohen d = 0.26). Double-spaced repetitions were superior to single-spaced repetitions for learning (62.24% vs 51.83%, P < .001, Cohen d = 0.43) and transfer (60.08% vs 55.72%, P < .001, Cohen d = 0.20). There were no meaningful differences in learning or transfer between repetition strategy chosen in the single- or double-repetition groups. CONCLUSIONS: This study affirms the value of spaced repetition in improving learning and retention in medical education and ongoing professional development.
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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 | 0.010 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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