In natural science courses, the two most common evaluators of student writing are peers and graduate teaching assistants. This study examined what each covers well, and what critical knowledge gets missed depending on who's reviewing.
Patchan, M. M., Schunn, C.D., Russell, R. (2011). "Writing in natural sciences: Understanding the effects of different types of reviewers on the writing process." Journal of Writing Research, 2(3), 365–393.
Writing instruction in natural science courses requires balancing three kinds of knowledge: subject matter knowledge, rhetorical knowledge, and writing-process knowledge. In most university science courses, the two people actually positioned to give feedback are fellow students and graduate teaching assistants, most of whom have no formal training in writing instruction. This study asked how feedback from these two reviewer types differs, and whether either one reliably covers all three knowledge types on their own.
The researchers compared feedback from peer reviewers (via a structured online peer review system) against feedback from graduate teaching assistants across natural science writing assignments, coding comments for which type of knowledge — subject matter, rhetorical, or writing-process — each addressed.
Peers and teaching assistants brought different strengths and different blind spots to their feedback, reflecting their different backgrounds and training. Neither reviewer type alone reliably covered all three knowledge types a strong piece of scientific writing depends on.
The practical lesson for any expertise-diverse workplace review process is the same: no single reviewer type, whether a domain expert or a generalist peer, is likely to cover every dimension a piece of work needs evaluated on. Combining reviewer perspectives, rather than relying on one type of reviewer alone, closes gaps that any single reviewer type would leave.
Chris Schunn co-authored this study comparing what peer reviewers and graduate teaching assistants each catch, and each miss. 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.
