Affective Language in Student Peer Reviews: Exploring Data from Three Institutional Contexts
Abstract
Although peer review is a common practice in writing classrooms, there are still few studies that analyze written patterns in students’ peer reviews across multiple institutional contexts. Based on a sample of approximately 50,000 peer reviews written by students at the University of South Florida (USF), Malmö University (MAU), and the University of Tartu (UT), this study examines how students formulate criticism and praise, negotiate power relations, and express authority and expertise in reviewing their peers’ writing. The study specifically focuses on features of affective language, including adjectives, expressions of suggestion, boosters and hedges, cognitive verbs, personal pronouns, and adversative transitions. The results show that across all three contexts, the peer reviews contain a blend of foci, including descriptions and evaluations of peer texts, directives or suggestions for revisions, responses to the writer or the text, and indications of reader interpretations. Across all three contexts, peer reviews also contain more positively glossed responses than negatively glossed responses. By contrast, certain features of affective language pattern idiosyncratically in different contexts; these distinctions can be explained variously according to writer experience, nativeness, and institutional context. The findings carry implications for continued research and for instructional guidance for student peer review.
- Journal
- Journal of Academic Writing
- Published
- 2018-09-01
- DOI
- 10.18552/joaw.v8i1.429
- CompPile
- Open Access
- OA PDF Diamond
- Topics
- Export
- BibTeX RIS
Citation Context
Cited by in this index (0)
No articles in this index cite this work.
References (0)
No references on file for this article.
Related Articles
-
Computers and Composition Jun 2026Julie Townsend; Melanie Gagich
-
Computers and Composition Jun 2026“Article laundry” or “tutor in pocket?”: Multilingual writers’ generative AI-assisted writing in professional settings ↗Qianqian Zhang-Wu
-
Computers and Composition Jun 2026John R. Gallagher; Antonio Byrd
-
Computers and Composition Jun 2026Jessie Borgman; Amy Cicchino; Heidi Skurat Harris; Casey McArdle; Scott Warnock
-
Technical Communication Quarterly May 2026Rachel Bryson; Thabata Fay; Zabrina Le; Emmerson Martin; Cora Romero