Students helping students: Evaluating a pilot program of peer teaching for an undergraduate course in human anatomy
Bibliographic record
Abstract
The educational literature generally suggests that supplemental instruction (SI) is effective in improving academic performance in traditionally difficult courses. A pilot program of peer teaching based on the SI model was implemented for an undergraduate course in human anatomy. Students in the course were stratified into three groups based on the number of peer teaching sessions they attended: nonattendees (0 sessions), infrequently attended (1-3 sessions), and frequently attended (≥ 4 sessions). After controlling for academic preparedness [i.e., admission grade point average (AGPA)] using an analysis of covariance, the final grades of frequent attendees were significantly higher than those of nonattendees (P = 0.025) and infrequent attendees (P = 0.015). A multiple regression analysis was performed to estimate the relative independent contribution of several variables in predicting the final grade. The results suggest that frequent attendance (β = 0.245, P = 0.007) and AGPA (β = 0.555, P < 0.001) were significant positive predictors, while being a first-year student (β = -0.217, P = 0.006) was a significant negative predictor. Collectively, these results suggest that attending a certain number of sessions may be required to gain a noticeable benefit from the program, and that first-year students (particularly those with a lower level of academic preparedness) would likely stand to benefit from maximally using the program. End-of-semester surveys and reports indicate that the program had several additional benefits, both to the students taking the course and to the students who served as program leaders.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| 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".