Analyzing high-level and low-level comments across a full persuasive-writing task, this study tested how providing peer feedback and receiving it separately affect a secondary student's writing performance and learning.
Wu, Yong, Schunn, Christian (2020). "The Effects of Providing and Receiving Peer Feedback on Writing Performance and Learning of Secondary School Students." American Educational Research Journal.
Secondary school writing instruction has less research behind it than college-level peer review. This study asked, specifically at that grade level: does the effect of giving versus receiving peer feedback on writing performance depend on the level of the comment — high-level concerns like argument and organization, versus low-level concerns like language and conventions?
Students completed persuasive writing tasks analyzing rhetorical strategies in a source passage. Comments were segmented into idea units — over 6,500 in total — and coded as high-level (thesis, argument, rhetorical strategy, evidence, organization) or low-level (language and conventions), then analyzed for their relationship to writing performance and learning.
Consistent with the broader body of Peerceptiv-affiliated research, students learned more from providing feedback than from receiving it. Distinguishing high-level from low-level comments clarified which specific kind of engagement with feedback — as reviewer or as recipient, on substance or on surface issues — tracked most closely with actual writing gains.
Not all feedback is created equal, and neither is the side of it you're on. When designing a workplace peer-review program for a specific skill, distinguishing high-level, substantive feedback from low-level, surface feedback — and making sure employees spend real time in the reviewer role on the high-level kind — does more for their development than simply increasing the volume of feedback exchanged.
Chris Schunn co-authored this study extending the providing-versus-receiving research to secondary school writing instruction. He is a Professor of Psychology, Learning Sciences and Policy, and Intelligent Systems at the University of Pittsburgh and a Senior Scientist at the University's Learning Research and Development Center (LRDC), where he has directed research projects backed by more than $80M in federal grants. As Peerceptiv's Chief Learning Scientist, his research directly shapes how the platform structures reviewer prompts, rubrics, and feedback workflows.
