Identifying Problem Localization in Peer-Review Feedback
نویسندگان
چکیده
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.
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