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2010

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

13 years 5 months ago
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.
Wenting Xiong, Diane J. Litman, Christian D. Schun
Added 29 Oct 2010
Updated 29 Oct 2010
Type Conference
Year 2010
Where EDM
Authors Wenting Xiong, Diane J. Litman, Christian D. Schunn
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