How to Make Peer Feedback in Teams Useful: An Empirical Study
Bibliographic record
Abstract
Evidence is clear that peer feedback has a slightly beneficial effect on future peer ratings of students effectiveness in teams, suggesting that peer feedback is useful. However, the effects are quite modest and are inconsistent. We believe one reason for this is the inconsistent approaches to debriefing and guiding the usage of peer feedback information. That is, instructors vary widely on the process they use to implement peer feedback in the classroom. Our feeling is that interventions that drive attention to and action on the results of the peer feedback will be instrumental for activating regulatory processes and creating stronger, more consistent behavior change. We report on one such intervention versus a control condition without any particular support beyond provision of the peer feedback scores and comments. We find that the intervention actually buffered students from scoring lower at a later time in the semester, suggesting that the intervention was successful in helping students maintain the early levels of positive ratings before the work became intensive and some members potentially failed to perform according to expectation
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".