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Record W4408054097 · doi:10.1080/02602938.2025.2468848

Enhancing the structure of feedback forms increases trustworthiness and usefulness of peer feedback

2025· article· en· W4408054097 on OpenAlexaff
Jessica Quinton, Lorien Nesbitt, Johanna Bock

Bibliographic record

VenueAssessment & Evaluation in Higher Education · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicStudent Assessment and Feedback
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsPeer feedbackTrustworthinessPsychologyPeer evaluationPeer reviewHigher educationNegative feedbackSocial psychologyMathematics educationPolitical scienceEngineering

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.178
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.040
GPT teacher head0.402
Teacher spread0.363 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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