The effects of supplemental instruction derived from peer leaders on student outcomes in undergraduate human anatomy
Bibliographic record
Abstract
Supplemental instruction (SI) confers student success, as represented by grades, knowledge retention, and student engagement. However, studies often report professional, not undergraduate, program findings. To measure these effects, students studying human anatomy at a university in Ontario, Canada, attended structured (peer-assisted) or unstructured (nonpeer-assisted) SI sessions and completed a pre-/post-survey. Fifty-eight learners (39 systems (SYS) and 19 musculoskeletal (MSK) anatomy) completed both surveys and had responses analyzed. Both cohorts, maintained initial perceptions across pre-/post-analyses (MSK p = 0.1376 and SYS p = 0.3521). Resource usage was similar across both cohorts with discrepancies in skeletal model and textbook use. No MSK learner ranked any lab resources as "not at all useful." MSK learners felt more prepared to write a graded assessment (p = 0.0269), whereas SYS learners did not (p = 0.0680). Stratification of learners in MSK and SYS revealed learners spending between 30 and 60 min in SI sessions during the study period had the highest grades compared to students who spent less than 30 (p = 0.0286) or more than 60 (p = 0.0286) min attending SI sessions, respectively. Most learners in MSK (89.4%) and SYS (66%) concluded that they preferred structured over unstructured SI. Sentiment/thematic analysis using a generative AI-driven large language model revealed learners held positive perceptions of SI, emphasizing structured learning, resources, personalized learning, and support offered as the most prevalent themes surrounding SI. Ultimately, this study provides evidence that supports SI for improving student outcomes related to perceived preparedness for completing assessments and preferred teaching/learning styles in undergraduate human anatomy.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".