Bibliographic record
Abstract
INTRODUCTION: Peer tutor-led small group sessions are a valuable learning strategy but students may lack confidence in the absence of a content expert. This study examined whether faculty reinforcement of peer tutor-led small group content was beneficial. METHODS: Two peer tutor-led small group sessions were compared with one faculty-led small group session using questionnaires sent to student participants and interviews with the peer tutors. One peer tutor-led session was followed by a lecture with revision of the small group content; after the second, students submitted a group report which was corrected and returned to them with comments. RESULTS: Student participants and peer tutors identified increased discussion and opportunity for personal reflection as major benefits of the peer tutor-led small group sessions, but students did express uncertainty about gaps in their learning following these sessions. Both methods of subsequent faculty reinforcement were perceived as valuable by student participants and peer tutors. Knowing in advance that the group report would be corrected reduced discussion in some groups, potentially negating one of the major benefits of the peer tutor-led sessions. DISCUSSION: Faculty reinforcement of peer-tutor led small group content benefits students but close attention should be paid to the method of reinforcement.
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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 | 0.028 | 0.088 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".