Controlling for comment length, Yang and Schunn found that feedback focused on content and written with high specificity is what predicts substantial learning gains for the reviewer — not length alone.
Yang, J., Schunn, C. D. (2026). "Beyond small gains: how content-focused high-specificity peer feedback correlates with substantial provider benefits." Instructional Science, 54, 59.
Prior research, including Peerceptiv's own studies, had already established that providing feedback benefits the reviewer on average — but the size of that benefit varies enormously and is usually small. Yang and Schunn asked what separates the small, typical gains from the substantial ones: is it simply writing more, or is it something about what the feedback actually contains?
The researchers strategically selected peer comments while controlling for comment length, then coded them on two dimensions: feedback focus (content-focused versus surface-level) and feedback specificity (how precisely a comment identified an issue). The study measured near-transfer learning — whether reviewing improved a person's performance on a new task, not just their revision of the same assignment.
Content-focused, high-specificity feedback correlated with substantial provider benefits. Comment length on its own, once content and specificity were accounted for, was not the driver.
This refines a principle Peerceptiv's research has built for two decades: encouraging reviewers to write more is not the same as encouraging them to write well. A comment-length target is an easy metric to instrument, but this study shows it's the wrong one to optimize for on its own. Reviewer prompts should push toward identifying specific, substantive content issues, not toward hitting a word count.
Chris Schunn co-authored this recent study refining what makes peer feedback valuable for the person giving it. 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.
