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Comparing Linguistic Features for Modeling Learning in Computer Tutoring

Forbes-Riley, Kate and Litman, Diane J. and Purandare, Amruta and Rotaru, Mihai and Tetreault, Joel R. (2007) Comparing Linguistic Features for Modeling Learning in Computer Tutoring. In: Proceedings of the 2007 Conference on Artificial Intelligence in Education: Building Technology Rich Learning Contexts That Work, 09 July 2007 - 13 July 2007, Los Angeles, CA.

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Abstract

We compare the relative utility of different automatically computable linguistic feature sets for modeling student learning in computer dialogue tutoring. We use the PARADISE framework (multiple linear regression) to build a learning model from each of 6 linguistic feature sets: 1) surface features, 2) semantic features, 3) pragmatic features, 4) discourse structure features, 5) local dialogue context features, and 6) all feature sets combined. We hypothesize that although more sophisticated linguistic features are harder to obtain, they will yield stronger learning models. We train and test our models on 3 different train/test dataset pairs derived from our 3 spoken dialogue tutoring system corpora. Our results show that more sophisticated linguistic features usually perform better than either a baseline model containing only pretest score or a model containing only surface features, and that semantic features generalize better than other linguistic feature sets.


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Details

Item Type: Conference or Workshop Item (Paper)
Status: Published
Creators/Authors:
CreatorsEmailPitt UsernameORCID
Forbes-Riley, Kate
Litman, Diane J.dlitman@pitt.eduDLITMAN
Purandare, Amruta
Rotaru, Mihai
Tetreault, Joel R.
Centers: University Centers > Learning Research and Development Center (LRDC)
Date: 2007
Date Type: Publication
Publisher: IOS Press
Place of Publication: Amsterdam, The Netherlands, The Netherlands
Page Range: 270 - 277
Event Title: Proceedings of the 2007 Conference on Artificial Intelligence in Education: Building Technology Rich Learning Contexts That Work
Event Dates: 09 July 2007 - 13 July 2007
Event Type: Conference
Schools and Programs: Dietrich School of Arts and Sciences > Computer Science
Dietrich School of Arts and Sciences > Intelligent Systems
Refereed: Yes
Uncontrolled Keywords: Linguistics, Multiple, Linear, Regression, Tutoring, Dialogue, Systems
Official URL: http://dl.acm.org/citation.cfm?id=1563601.1563646
Date Deposited: 18 Dec 2014 15:53
Last Modified: 01 Nov 2017 13:57
URI: http://d-scholarship.pitt.edu/id/eprint/23201

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