Many instructors hesitate to use peer review because they're uncertain whether students can produce comments that hold up next to an expert's. This study ran the direct comparison.
Patchan, M. M., Charney, D., Schunn, C. D. (2009). "A validation study of students’ end comments: Comparing comments by students, a writing instructor, and a content instructor." Journal of Writing Research, 1(2), 124–152.
Instructors adopting peer review in large classes to make more writing assignments feasible often remain uncertain whether students can produce end comments that are actually comparable to expert feedback. This study directly compared three sources of comments on the same student writing: fellow students, a writing instructor, and a content-area instructor.
The researchers collected and compared end comments from all three sources — peer students, a writing instructor, and a content instructor — on the same set of student papers, examining how the comments aligned and differed across sources.
This study is part of the direct evidence base establishing that student-generated comments can meaningfully hold up alongside instructor comments, contributing to the broader case that peer feedback is a legitimate source of evaluative input, not merely a stand-in used when expert time is scarce.
Skepticism about whether an employee's feedback "counts" the same as a manager's is a natural instinct but not one this research supports uncritically. Peer comments, evaluated on their own terms, can carry real evaluative weight — a reason to treat structured peer feedback as a genuine input to development decisions, not a lesser substitute for management review.
Chris Schunn co-authored this study comparing peer-generated comments directly against instructor comments on the same writing. 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.
