Identifying Problem Localization in Peer-Review Feedback

Abstract. In this paper, we use supervised machine learning to automatically identify the problem localization of peer-review feedback. Using five features extracted via Natural Language Processing techniques, the learned model significantly outperforms a standard baseline. Our work suggests that it is feasible for future tutoring systems to generate assessments regarding the use of localization in student peer reviews.

• Type: Conference
• Year: 2010
• Where: ITS
• Authors: Wenting Xiong, Diane J. Litman
• Keywords: ITS 2010, Multimedia, Natural Language Processing, Peer-review Feedback, Problem Localization