Feedback familiarity explains why some students don’t like peer review: Understanding why using a stressor-strain-outcome framework
DOI:
https://doi.org/10.14742/ajet.10414Keywords:
peer feedback, feedback familiarity, information characteristics, stressor-strain-outcome (SSO) framework, negative reactionsAbstract
Computer-based peer review systems have received extensive attention due to their rapid development and broad application. Although studies have generally found strong assessment validity and positive learning effects, several have found negative reactions in students. More work is needed to understand the reasons for students' negative reactions to an approach that generally shows benefits for students. Applying a stressor-strain-outcome framework, this study established the influence mechanism from feedback familiarity to students' negative reactions and examined information-related stressors (overload and narrowing) underlying students' negative reactions to familiarity of peer feedback. The results show that peer feedback is often perceived as having high familiarity, which is related to perceptions of information narrowing and information overload, which in turn were related to students’ feeling exhausted, which in turn produces psychological reactance and decreased willingness to continue using peer feedback. This study extends our knowledge of students' negative responses to peer feedback through peer review platforms. Implications for theory and practice are discussed.
Implications for practice or policy:
- Course leaders could avoid redundant, overly familiar feedback through algorithms that assign diverse reviewers and targeted prompts.
- Course leaders could reduce information redundancy by limiting the number and breadth of reviews, providing tools to summarise or filter comments, and training students to process feedback effectively.
- Students could offset information narrowness and increase awareness of their learning by integrating self-reflection prompts or learning journals.
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Copyright (c) 2025 Zheng Zong, Christian Schunn, Meihui Zheng, Ying Gao

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