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Predicting low vs. high disparity between peer and expert ratings in peer reviews of physics lab reports

Nguyen, HV and Litman, DJ (2013) Predicting low vs. high disparity between peer and expert ratings in peer reviews of physics lab reports. In: UNSPECIFIED.

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Abstract

Our interest in this work is to automatically predict whether peer ratings have high or low agreement in terms of disparity with instructor ratings, using solely features extracted from quantitative peer ratings and text-based peer comments. Experimental results suggest that our model can indeed outperform a majority baseline in predicting low versus high rating disparity. Furthermore, the reliability of both peer ratings and comments (in terms of peer disagreement) shows little correlation to disparity. © 2013 Springer-Verlag Berlin Heidelberg.


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Details

Item Type: Conference or Workshop Item (UNSPECIFIED)
Status: Published
Creators/Authors:
CreatorsEmailPitt UsernameORCID
Nguyen, HVhvn3@pitt.eduHVN3
Litman, DJdlitman@pitt.eduDLITMAN
Centers: Other Centers, Institutes, or Units > Learning Research & Development Center
Date: 16 July 2013
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: 7926 L
Page Range: 687 - 691
Event Type: Conference
DOI or Unique Handle: 10.1007/978-3-642-39112-5-89
Institution: University of Pittsburgh
Schools and Programs: Dietrich School of Arts and Sciences > Computer Science
Dietrich School of Arts and Sciences > Computer Science > Intelligent Systems Technical Reports
Refereed: Yes
ISBN: 9783642391118
ISSN: 0302-9743
Related URLs:
Date Deposited: 14 Aug 2014 15:44
Last Modified: 04 Nov 2019 19:58
URI: http://d-scholarship.pitt.edu/id/eprint/22693

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