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Adapting to student uncertainty improves tutoring dialogues

Forbes-Riley, K and Litman, D (2009) Adapting to student uncertainty improves tutoring dialogues. In: UNSPECIFIED.

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This study shows that affect-adaptive computer tutoring can significantly improve performance on learning efficiency and user satisfaction. We compare two different student uncertainty adaptations which were designed, implemented and evaluated in a controlled experiment using four versions of a wizarded spoken dialogue tutoring system: two adaptive systems used in two experimental conditions (basic and empirical), and two non-adaptive systems used in two control conditions (normal and random). In prior work we compared learning gains across the four systems; here we compare two other important performance metrics: learning efficiency and user satisfaction. We show that the basic adaptive system outperforms the normal (non-adaptive) and empirical (adaptive) systems in terms of learning efficiency. We also show that the empirical (adaptive) and random (non-adaptive) systems outperform the basic adaptive system in terms of user perception of tutor response quality. However, only the basic adaptive system shows a positive correlation between learning and user perception of decreased uncertainty. © 2009 The authors and IOS Press. All rights reserved.


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Item Type: Conference or Workshop Item (UNSPECIFIED)
Status: Published
CreatorsEmailPitt UsernameORCID
Forbes-Riley, K
Litman, Ddlitman@pitt.eduDLITMAN
Centers: University Centers > Learning Research and Development Center (LRDC)
Date: 1 January 2009
Date Type: Publication
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Journal or Publication Title: Frontiers in Artificial Intelligence and Applications
Volume: 200
Number: 1
Page Range: 33 - 40
Event Type: Conference
DOI or Unique Handle: 10.3233/978-1-60750-028-5-33
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: 9781607500285
ISSN: 0922-6389
Date Deposited: 12 Dec 2014 17:50
Last Modified: 02 Feb 2019 15:59


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