Shining a Light Into the Black Box of Group Learning: Medical Students’ Experiences and Perceptions of Small Groups
Bibliographic record
Abstract
PURPOSE: Group work is seen as serving multiple positive purposes in health professions education, such as providing an opportunity for students to master course content, transfer knowledge into clinical practice, and develop collaborative/teamwork skills. However, there have been relatively few studies exploring medical students' experiences of the small-group learning context or what they learn in and from that context. METHOD: Between January 2018 and January 2019, the authors used grounded theory methods to conduct semistructured interviews with 9 medical students to explore their perceptions of the value of the group as a mechanism for learning both content and teamwork skills. Sessions were audiorecorded and transcribed verbatim. One author coded the transcripts and identified codes, which the team then discussed, refined, and used to develop themes. RESULTS: Students were able to express all the expected goals for small-group learning, such as retaining course materials, mimicking future health care team interactions, and creating a collaborative environment. However, when their experiences were further explored, students seemed to have perceived that the value of group learning was as a mechanism for reviewing rather than for deepening their learning. Further, students frequently expressed the opinion that the tutor was the primary factor in the success of a group, and when group function was suboptimal, students described giving up on the group or relying on the tutor to address the problem. CONCLUSIONS: Formal, small-group, tutor-led learning sessions, at least in the context of single-term groups, may not be accomplishing what educators might hope. Although students understand the intent of small-group learning, it cannot be assumed that such groups are deepening learning or solving the teamwork problems in health professions education.
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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 | 0.012 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.012 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".