This doctoral dissertation examined how students learn from two distinct activities in peer review: revising their own work based on feedback, and reviewing their peers' texts.
Patchan, M. M. (2011). "Peer review of writing: Learning from revision using peer feedback and reviewing peers’ texts." Doctoral dissertation, Cognitive Psychology, University of Pittsburgh.
Peer review bundles together two distinct learning activities: revising your own work in response to feedback you receive, and evaluating and commenting on someone else's work. This dissertation set out to separate these two activities and ask what each one contributes to a student's development as a writer.
Conducted in the Cognitive Psychology program at the University of Pittsburgh under Chris Schunn's guidance, this dissertation examined student writers in the context of structured online peer review, tracing what students learned specifically from the revision process versus what they learned from the act of reviewing peers' texts.
This early work helped establish a distinction that later research, including subsequent studies on reviewer benefits, built directly on: reviewing someone else's work and revising your own are separate learning mechanisms, not interchangeable versions of the same experience.
The practical implication carries into any workplace peer-review design: a program that gives every employee a chance to revise based on feedback but never has them review a colleague's work is capturing only half of what peer review can teach. Both roles need to be built into the process deliberately.
This dissertation was advised by Chris Schunn and laid groundwork for a decade of subsequent research on what reviewers themselves gain from peer review. 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.
