Patchan, Melissa M., and Christian D. Shunn. “Understanding the Effects of Receiving Peer Feedback for Text Revision: Relations between Author and Reviewer Ability.” Journal of Writing Research 8.2 (2016): 227-65. Web. 18 Nov. 2016. doi: 10.17239/jowr-2016.08.02.03
Melissa M. Patchan and Christian D. Shunn describe a study of the relationship between the abilities of writers and peer reviewers in peer assessment. The study asks how the relative ability of writers and reviewers influences the effectiveness of peer review as a learning process.
The authors note that in many content courses, the time required to provide meaningful feedback encourages many instructors to turn to peer assessment (228). They cite studies suggesting that in such cases, peer response can be more effective than teacher response because, for example, students may actually receive more feedback, the feedback may be couched in more accessible terms, and students may benefit from seeing models and new strategies (228-29). Still, studies find, teachers and students both question the efficacy of peer assessment, with students stating that the quality of review depends largely on the abilities of the reviewer (229).
Patchan and Shunn distinguish between the kind of peer review characteristic of writing classrooms, which they describe as “pair or group-based face-to-face conversations” emphasizing “qualitative feedback” and the type more often practiced in large content classes, which they see as more like “professional journal reviewing” that is “asynchronous, and written-based” (228). Their study addresses the latter format and is part of a larger study examining peer feedback in a widely required psychology class at a “large, public research university in the southeast” (234).
A random selection of 189 students wrote initial drafts in response to an assignment assessing media handling of a psychological study using criteria from the course textbook (236, 238). Students then received four drafts to review and were given a week to revise their own drafts in response to feedback. Participants used the “web-based peer assessment functions of turnitin.com” (237).
The researchers rated participants as high-ability writers using SAT scores and grades in their two first-year writing courses (236). Graduate rhetoric students also rated the first drafts. The protocol then included a “median split” to designate writers in binary fashion as either high- or low-ability. “High” authors were categorized as “high” reviewers. Patchan and Shunn note that there was a wide range in writer abilities but argue that, even though the “design decreases the power of this study,” such determinations were needed because of the large sample size, which in turn made the detection of “important patterns” likely (236-37). They feel that “a lower powered study was a reasonable tradeoff for higher external validity (i.e., how reviewer ability would typically be detected)” (237).
The authors describe their coding process in detail. In addition to coding initial drafts for quality, coders examined each reviewer’s feedback for its attention to higher-order problems and lower-order corrections (239-40). Coders also tabulated which comments resulted in revision as well as the “quality of the revision” (241). This coding was intended to “determine how the amount and type of comments varied as a function of author ability and reviewer ability” (239). A goal of the study was to determine what kinds of feedback triggered the most effective responses in “low” authors (240).
The study was based on a cognitive model of writing derived from the updated work of Linda Flower and John R. Hayes, in which three aspects of writing/revision follow a writer’s review of a text: problem detection, problem diagnosis, and strategy selection for solving the diagnosed problems (230-31). In general, “high” authors were expected to produce drafts with fewer initial problems and to have stronger reading skills that allowed them to detect and diagnose more problems in others’ drafts, especially “high-level” problems having to do with global issues as opposed to issues of surface correctness (230). High ability authors/reviewers were also assumed to have a wider repertoire of solution strategies to suggest for peers and to apply to their own revisions (233). All participants received a rubric intended to guide their feedback toward higher-order issues (239).
Some of the researchers’ expectations were confirmed, but others were only partially supported or not supported (251). Writers whose test scores and grades categorized them as “high” authors did produce better initial drafts, but only by a slight margin. The researchers posit that factors other than ability may affect draft quality, such as interest or time constraints (243). “High” and “low” authors received the same number of comments despite differences in the quality of the drafts (245), but “high” authors made more higher-order comments even though they didn’t provide more solutions (246). “High” reviewers indicated more higher-order issues to “low” authors than to “high,” while “low” reviewers suggested the same number of higher-order changes to both “high” and “low” authors (246).
Patchan and Shunn considered the “implementation rate,” or number of comments on which students chose to act, and “revision quality” (246). They analyzed only comments that were specific enough to indicate action. In contrast to findings in previous studies, the expectation that better writers would make more and better revisions was not supported. Overall, writers acted on only 32% of the comments received and only a quarter of the comments resulted in improved drafts (248). Author ability did not factor into these results. Moreover, the ability of the reviewer had no effect on how many revisions were made or how effective they were (248).
It was expected that low-ability authors would implement more suggestions from higher-ability reviewers, but in fact, “low authors implemented more high-level criticism comments . . . from low reviewers than from high reviewers” (249). The quality of the revisions also improved for low-ability writers when the comments came from low-ability reviewers. The researchers conclude that “low authors benefit the most from feedback provided by low reviewers” (249).
Students acted on 41% of the low-level criticisms, but these changes seldom resulted in better papers (249).
The authors posit that rates of commenting and implementation may both be impacted by limits or “thresholds” on how much feedback a given reviewer is willing to provide and how many comments a writer is able or willing to act on (252, 253). They suggest that low-ability reviewers may explain problems in language that is more accessible to writers with less ability. Patchan and Shunn suggest that feedback may be most effective when it occurs within the student’s zone of proximal development, so that weaker writers may be helped most by peers just beyond them in ability rather than by peers with much more sophisticated skills (253).
In the authors’ view, that “neither author ability nor reviewer ability per se directly affected the amount and quality of revisions” (253) suggests that the focus in designing effective peer review processes should shift from how to group students to improving students’ ability to respond to comments (254). They recommend further research using more “direct” measures of writing and reviewing ability (254). A major conclusion from this study is that “[h]igher-ability students will likely revise their texts successfully regardless of who [they are] partnered with, but the lower-ability students may need feedback at their own level” (255).