Understanding differences in perceived peer-review helpfulness using natural language processing

Authors:
Wenting Xiong;Diane Litman
Affiliations:
University of Pittsburgh, Pittsburgh, PA;University of Pittsburgh, Pittsburgh, PA
Venue:
IUNLPBEA '11 Proceedings of the 6th Workshop on Innovative Use of NLP for Building Educational Applications
Year:
2011

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Abstract

Identifying peer-review helpfulness is an important task for improving the quality of feedback received by students, as well as for helping students write better reviews. As we tailor standard product review analysis techniques to our peer-review domain, we notice that peer-review helpfulness differs not only between students and experts but also between types of experts. In this paper, we investigate how different types of perceived helpfulness might influence the utility of features for automatic prediction. Our feature selection results show that certain low-level linguistic features are more useful for predicting student perceived helpfulness, while high-level cognitive constructs are more effective in modeling experts' perceived helpfulness.