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Metacognition and learning in spoken dialogue computer tutoring

Forbes-Riley, K and Litman, D (2010) Metacognition and learning in spoken dialogue computer tutoring. In: UNSPECIFIED.

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We investigate whether four metacognitive metrics derived from student correctness and uncertainty values are predictive of student learning in a fully automated spoken dialogue computer tutoring corpus. We previously showed that these metrics predicted learning in a comparable wizarded corpus, where a human wizard performed the speech recognition and correctness and uncertainty annotation. Our results show that three of the four metacognitive metrics remain predictive of learning even in the presence of noise due to automatic speech recognition and automatic correctness and uncertainty annotation. We conclude that our results can be used to inform a future enhancement of our fully automated system to track and remediate student metacognition and thereby further improve learning. © Springer-Verlag Berlin Heidelberg 2010.


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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 December 2010
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: 6094 L
Number: PART 1
Page Range: 379 - 388
Event Type: Conference
DOI or Unique Handle: 10.1007/978-3-642-13388-6_42
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: 3642133878, 9783642133879
ISSN: 0302-9743
Date Deposited: 18 Dec 2014 16:21
Last Modified: 26 Dec 2021 13:55


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