Assessing Reviewer's Performance Based on Mining Problem Localization in Peer-Review Data

Current peer-review software lacks intelligence for responding to students' reviewing performance. As an example of an additional intelligent assessment component to such software, we propose an evaluation system that generates assessment on reviewers' reviewing skills regarding the issue of problem localization. We take a data mining approach, using standard supervised machine learning to build classifiers based on attributes extracted from peer-review data via Natural Language Processing techniques. Our work successfully shows it is feasible to provide intelligent support for peer-review systems to assess students' reviewing performance fully automatically.

• Type: Conference
• Year: 2010
• Where: EDM
• Authors: Wenting Xiong, Diane J. Litman, Christian D. Schunn
• Keywords: Additional Intelligent Assessment, Current Peer-review Software, Data Mining, EDM 2010, Peer-review