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Identifying problem localization in peer-review feedback

Xiong, W and Litman, D (2010) Identifying problem localization in peer-review feedback. In: UNSPECIFIED.

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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. © 2010 Springer-Verlag.

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Details

Item Type: Conference or Workshop Item (UNSPECIFIED)
Status: Published
Creators/Authors:
CreatorsEmailPitt UsernameORCID
Xiong, W
Litman, Ddlitman@pitt.eduDLITMAN
Centers: University Centers > Learning Research and Development Center (LRDC)
Date: 27 August 2010
Date Type: Publication
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Journal or Publication Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume: 6095 L
Number: PART 2
Page Range: 429 - 431
Event Type: Conference
DOI or Unique Handle: 10.1007/978-3-642-13437-1_93
Institution: University of Pittsburgh
Schools and Programs: Dietrich School of Arts and Sciences > Computer Science
Dietrich School of Arts and Sciences > Intelligent Systems
Refereed: Yes
ISBN: 364213436X, 9783642134364
ISSN: 0302-9743
Date Deposited: 21 Nov 2014 19:03
Last Modified: 28 Jan 2021 21:55
URI: http://d-scholarship.pitt.edu/id/eprint/22948

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