A Dashboard to Provide Instructors with Automated Feedback on Students’ Peer Review Comments

Abstract

Writing-to-Learn (WTL) is an evidence-based instructional practice which can help students construct knowledge across many disciplines. Though it is known to be an effective practice, many instructors do not implement WTL in their courses due to time constraints and inability to provide students with personalized feedback. One way to address this is to include peer review, which allows students to receive feedback on their writing and benefits them as they act as reviewers. To further ease the implementation of peer review and provide instructors with feedback on their students’ work, we labeled students’ peer review comments across courses for type of feedback provided and trained a machine learning model to automatically classify those comments, improving upon models reported in prior work. We then created a dashboard which takes students’ comments, labels the comments using the model, and allows instructors to filter through their students’ comments based on how the model labels the comments. This dashboard can be used by instructors to monitor the peer review collaborations occurring in their courses. The dashboard will allow them to efficiently use information provided by peers to identify common issues in their students’ writing and better evaluate the quality of their students’ peer review.