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Dialog Convergence and Learning

Ward, Arthur and Litman, Diane J. (2007) Dialog Convergence and Learning. In: 2007 Conference on Artificial Intelligence in Education: Building Technology Rich Learning Contexts That Work.

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In this paper we examine whether the student-to-tutor convergence of lexical and speech features is a useful predictor of learning in a corpus of spoken tutorial dialogs. This possibility is raised by the Interactive Alignment Theory, which suggests a connection between convergence of speech features and the amount of semantic alignment between partners in a dialog. A number of studies have shown that users converge their speech productions toward dialog systems. If, as we hypothesize, semantic alignment between a student and a tutor (or tutoring system) is associated with learning, then this convergence may be correlated with learning gains. We present evidence that both lexical convergence and convergence of an acoustic/prosodic feature are useful features for predicting learning in our corpora. We also find that our measure of lexical convergence provides a stronger correlation with learning in a human/computer corpus than did a previous measure of lexical cohesion.


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Item Type: Conference or Workshop Item (Paper)
Status: Published
CreatorsEmailPitt UsernameORCID
Ward, Arthur
Litman, Diane J.dlitman@pitt.eduDLITMAN
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: 262 - 269
Event Title: 2007 Conference on Artificial Intelligence in Education: Building Technology Rich Learning Contexts That Work
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: Discourse, Analysis, Intelligent, Tutoring, Learner, Modeling
Official URL:
Date Deposited: 10 Oct 2014 18:50
Last Modified: 01 Nov 2017 13:57


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