Reflecting on Peer Feedback in Problem-Based Learning: Implementing a Group Function Tool
Bibliographic record
Abstract
Introduction Self-directed peer feedback is integral to the problem-based learning (PBL) process, but poorly scaffolded feedback processes can be inefficient and ineffective and there is little guidance on how students should structure these processes. This study aims to identify implementation considerations for a group function reflection tool and explore group feedback behaviours around the operationalization of the tool. Methods We conducted a qualitative study informed by direct content analysis using the group function reflection tool and conducted semi-structured focus groups in 2024 with 24 medical students and two tutors participating in a PBL curriculum. Students conducted peer feedback using the tool over four weeks, submitted feedback through an online form, and reflected on their experiences in focus groups. We analyzed feedback responses and transcripts in a staged approach, sensitized by three frameworks: the Human Factors Framework, the Task-Gap-Action model of feedback, and Thanks for the Feedback: Appreciation, Coaching, and Evaluation. Results We constructed five themes: 1) appreciative feedback is often under-valued, 2) there is tension between structure and flexibility in the feedback process, 3) the interplay between written and verbal feedback, 4) the density of feedback requires careful optimization, and 5) the tool as a threat to tutors. Discussion Operationalization of the tool exposed tensions around the peer feedback process. The tool reinforced the importance of a self-guided process for peer feedback which also requires prompting. It raised assumptions about the PBL feedback process which should be further studied to better understand peer feedback in broader contexts.
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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 | 0.051 | 0.096 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| 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".