Impact of Different Small Student Group Learning Approaches to Compressed Medical Anatomy Education
Bibliographic record
Abstract
The University of Ottawa utilizes two approaches to small group laboratory learning in its compressed (46.5 hrs) medical anatomy program. The Facilitated Active Learning (FAL) approach is driven by Faculty who promote student progression through learning objectives. In the Emphasized Independent Learning (EIL) approach, independent pre‐lab preparation is stressed, with limited Faculty involvement, based on 'flipped classroom' principles. This study characterized student perceptions and academic performance related to these approaches. Perceptions were surveyed using both Likert‐style items and open‐ended items. Open‐ended items were analyzed for consensus, emerging themes. Student performance was compared on overall practical examinations, anatomy‐related items, and discriminating anatomy items (testing knowledge application). While the survey analysis revealed perceived strengths attributed to both EIL (collaboration, communication skills) and FAL (appropriate direction and enhanced learning of objectives) methods, the EIL method was noted for lack of direction and inefficient student learning. Active learning in FAL classes was variable and sometimes limited by Faculty teaching style. While FAL students only scored 4% higher than EIL students on practical exams (P<0.05), FAL students scored 6% and 8% higher on anatomy‐related MCQs and discriminating anatomy‐related MCQs, respectively, indicating a significant (P<0.01) impact of teaching style on learning. Despite some perceived benefits, emphasizing independent student learning had a negative impact on student performance (particularly on discriminating items), relative to Faculty‐facilitated learning, in a compressed anatomy program.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".