Both preparing to teach and teaching positively impact learning outcomes for peer teachers
Bibliographic record
Abstract
BACKGROUND: We sought to evaluate the independent effects of preparing to teach and teaching on peer teacher learning outcomes. AIM: To evaluate the independent contributions of both preparing to teach and teaching to the learning of peer teachers in medical education. METHOD: In total, 17 third-year medical students prepared to teach second-year students Advanced Cardiac Life Support algorithms and electrocardiogram (ECG) interpretation. Immediately prior to teaching they were randomly allocated to not teach, to teach algorithms, or to teach ECG. Peer teachers were tested on both topics prior to preparation, immediately after teaching and 60 days later. RESULTS: Compared to baseline, peer teachers' mean examination scores (±SD) demonstrated the greatest gains for content areas they prepared for and then taught (43.0% (13.9) vs. 66.3% (8.8), p < 0.001, d = 2.1), with gains persisting to 60 days (45.1% (13.9) vs. 61.8% (13.9), p < 0.01, d = 1.3). For content they prepared to teach but did not teach, less dramatic gains were evident (43.6% (8.3) vs. 54.7% (9.4), p < 0.001, d = 1.3), but did persist for 60 days (42.6% (8.1) vs. 53.2% (14.5), p < 0.05, d = 1.3). Increase in test scores attributable to the act of teaching were greater than those for preparation (23.3% (10.9) vs. 8% (9.6), p < 0.001, d = 1.6), but the difference was not significant 60 days later (16.7% (14.4) vs. 10.2% (16.9), p = 0.4). CONCLUSION: Our results suggest preparing to teach and actively teaching may have independent positive effects on peer teacher learning outcomes.
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.
How this classification was reachedexpand
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 | 0.004 | 0.019 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".