Does group size matter during collaborative skills learning? A randomised study
Bibliographic record
Abstract
Abstract Background Collaborative skills learning in the form of dyad learning compared with individual learning has been shown to lead to non‐inferior skills retention and transfer. However, we have limited knowledge on which learning activities improve collaborative skills training and how the number of collaborators may impact skills transfer. We explored the effects of skills training individually, in dyads, triads or tetrads on learning activities during training and on subsequent skills transfer. Methods In a randomised, controlled study, participants completed a pre‐post‐transfer‐test set‐up in groups of one to four. Participants completed 2 hours of obstetric ultrasound training. In the dyad, triad and tetrad group participants took turns actively handling the ultrasound probe. All performances were rated by two blinded experts using the Objective Structured Assessment of Ultrasound Skills (OSAUS) scale and a Global Rating Scale (GRS). All training was video recorded, and learning activities were analysed using the Interactive‐Constructive‐Active‐Passive (ICAP) framework. Results One hundred one participants completed the simulation‐based training, and ninety‐seven completed the transfer test. Performance scores improved significantly from pre‐ to post‐test for all groups (p < 0.001, ηp 2 = 0.55). However, group size did not affect transfer test performance on OSAUS scores (p = 0.13, ηp 2 = 0.06) or GRS scores (p = 0.23, ηp 2 = 0.05). ICAP analyses of training activities showed that time spent on non‐learning and passive learning activities increased with group size (p < 0.001, ηp 2 = 0.31), whereas time spent on constructive and interactive learning activities was constant between groups compared with singles (p < 0.001, ηp 2 = 0.72). Conclusion Collaborative skills learning in groups of up to four did not impair skills transfer despite less hands‐on time. This may be explained by a compensatory shift towards constructive and interactive learning activities that outweigh the effect of shorter hands‐on time.
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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 | 0.039 | 0.075 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 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".