Engagement and learning in an electronic spaced repetition curriculum companion for a paediatrics academic half-day curriculum
Bibliographic record
Abstract
Postgraduate residencies utilize academic half-days to supplement clinical learning. Spaced repetition reinforces taught content to improve retention. We leveraged spaced repetition in a curriculum companion for a paediatric residency program's half-day. One half-day lecture was chosen weekly for reinforcement (day 0). Participants received 3 key points on day 1 and a multiple-choice question (MCQ) on day 8. On day 29, they received two MCQs to test reinforced and unreinforced content from the same day 0. Thirty-one (79%) residents participated over 17 weeks, but only 14 (36%) completed more than half of the weekly quizzes. Of all quizzes, 37.4% were completed, with an average weekly engagement of 5.5 minutes. Helpfulness to learning was rated as 7.89/10 on a Likert-like scale. Reported barriers were missing related half-days and emails, or limited time. There was no significant difference in performance between reinforced (63.4%, [53.6-73.3]) and unreinforced (65.6%, [53.7-73.2]) questions. Spaced repetition is a proven strategy in learning science, but was not shown to improve performance. Operational barriers likely limited participation and underpowered our analysis, therefore future implementation must consider practical and individual barriers to facilitate success. Our results also illustrate that satisfaction alone is an inadequate marker of success.
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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 | 0.010 |
| 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 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".