Enhancing the structure of feedback forms increases trustworthiness and usefulness of peer feedback
Bibliographic record
Abstract
Peer feedback is commonly used in higher education for both practical and pedagogical reasons. However, peer feedback has been criticized by teachers, researchers, and students for being superficial, harsh, uncritical, and/or detached from learning objectives. This study contributes to the existing literature on how to enhance the effectiveness of peer feedback by examining the impact of increasing the structure of peer feedback forms. Student participants were assigned to use either an open-ended peer feedback form or a structured form with predetermined options created by the teaching team. After giving and receiving feedback, students completed a brief survey about their experience. Feedback underwent quantitative content analysis to support interpretation and contextualization of survey results. We found that students receiving feedback from the structured form perceived the feedback to be more useful and trustworthy than those receiving feedback from the unstructured form. There was limited evidence that the structured feedback form impacted the experience of providing feedback, including student’s perceived level of self-reflection during the feedback process. The findings suggest that structured feedback forms can be used to address student concerns about the usefulness and trustworthiness of peer feedback without negatively impacted self-reflection during the feedback process.
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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 | 0.041 | 0.296 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".