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Natural language processing techniques for researching and improving peer feedback

UNSPECIFIED (2012) Natural language processing techniques for researching and improving peer feedback. Journal of Writing Research, 4 (2). 155 - 176. ISSN 2030-1006

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Abstract

Peer review has been viewed as a promising solution for improving studennts' writing, which still remains a great challenge for educators. However, one core problem with peer review of writing is that potentially useful feedbback from peers is not always presented in ways that lead to revision. Our prior investigations found that whether students implement feedback is significantly correlated with two feedback features: localization information and concrete solutions. But focusing on feedback features is time-intensive for researchers and instructors. We apply data mining and Natural Languagee Processing techniques to automatically code reviews for these feedback features. Our results show that it is feasible to provide intelligent suppport to peer review systems to automatically assess students' reviewing performance with respect to problem localization and solution. We also show that similar research conclusions about helpfulness perceptions of feedback across students and different expert types can be drawn from automatically coded data and from hand-coded data. © Earli.


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Details

Item Type: Article
Status: Published
Centers: University Centers > Learning Research and Development Center (LRDC)
Date: 1 January 2012
Date Type: Publication
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Journal or Publication Title: Journal of Writing Research
Volume: 4
Number: 2
Page Range: 155 - 176
DOI or Unique Handle: 10.17239/jowr-2012.04.02.3
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
ISSN: 2030-1006
Date Deposited: 08 Sep 2014 16:16
Last Modified: 05 Jan 2019 15:56
URI: http://d-scholarship.pitt.edu/id/eprint/22888

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