Student perceptions of independent versus facilitated small group learning approaches to compressed medical anatomy education
Bibliographic record
Abstract
The purpose of this study was to compare student perceptions regarding two, small group learning approaches to compressed (46.5 prosection-based laboratory hours), integrated anatomy education at the University of Ottawa medical program. In the facilitated active learning (FAL) approach, tutors engage students and are expected to enable and balance both active learning and progression through laboratory objectives. In contrast, the emphasized independent learning (EIL) approach stresses elements from the "flipped classroom" educational model: prelaboratory preparation, independent laboratory learning, and limited tutor involvement. Quantitative (Likert-style questions) and qualitative data (independent thematic analysis of open-ended commentary) from a survey of students who had completed the preclerkship curriculum identified strengths from the EIL (promoting student collaboration and communication) and FAL (successful progression through objectives) approaches. However, EIL led to student frustration related to a lack of direction and impaired completion of objectives, whereas active learning opportunities in FAL were highly variable and dependent on tutor teaching style. A "hidden curriculum" was also identified, where students (particularly EIL and clerkship students) commonly compared their compressed anatomy education or their anatomy learning environment with other approaches. Finally, while both groups highly regarded the efficiency of prosection-based learning and expressed value for cadaveric-based learning, student commentary noted that the lack of grade value dedicated to anatomy assessment limited student accountability. This study revealed critical insights into small group learning in compressed anatomy education, including the need to balance student active learning opportunities with appropriate direction and feedback (including assessment).
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".