An Exploratory Study of Small-Group Learning Interactions in Pre-Clerkship Medical Education: Uncovering a Mismatch Between Student Perceptions and Real-Time Observations
Bibliographic record
Abstract
Small-group learning is a mainstay of medical education, and group functioning can have a major influence on these learning experiences. Our objective was to explore verbal exchange patterns within small-group learning sessions and examine how different patterns related to tutor involvement, tutor expertise, and participants’ perceptions. A non-participant observer collected group interactivity data using a real-time mobile device-based system. Verbal interaction patterns were visualized and analyzed using social network analysis and correlated with participant survey data and aggregate course grades. There were 46 observations across 30 separate groups. Group interactions clustered into four patterns defined by (1) tutor involvement (high vs. low) and (2) interactivity (high vs. low). Interaction patterns were largely stable for a given group and groups with content expert facilitators were generally less interactive. Students reported objectively fewer interactive groups as more interactive and enjoyable. There were no significant intergroup differences in aggregate course grades. Paradoxically, student perceptions were not aligned with observed interactivity data, and tutor content expertise influenced group interactivity. These findings suggest the need to better manage learner expectations of small-group learning, and to explicitly reflect on and develop skills for effective collaborative learning with both faculty and students.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".