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Correlations between dialogue acts and learning in spoken tutoring dialogues

Litman, D and Forbes-Riley, K (2006) Correlations between dialogue acts and learning in spoken tutoring dialogues. Natural Language Engineering, 12 (2). 161 - 176. ISSN 1351-3249

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Abstract

We examine correlations between dialogue behaviors and learning in tutoring, using two corpora of spoken tutoring dialogues: a human-human corpus and a human-computer corpus. To formalize the notion of dialogue behavior, we manually annotate our data using a tagset of student and tutor dialogue acts relative to the tutoring domain. A unigram analysis of our annotated data shows that student learning correlates both with the tutor's dialogue acts and with the student's dialogue acts. A bigram analysis shows that student learning also correlates with joint patterns of tutor and student dialogue acts. In particular, our human-computer results show that the presence of student utterances that display reasoning (whether correct or incorrect), as well as the presence of reasoning questions asked by the computer tutor, both positively correlate with learning. Our human-human results show that student introductions of a new concept into the dialogue positively correlates with learning, but student attempts at deeper reasoning (particularly when incorrect), and the human tutor's attempts to direct the dialogue, both negatively correlate with learning. These results suggest that while the use of dialogue act n-grams is a promising method for examining correlations between dialogue behavior and learning, specific findings can differ in human versus computer tutoring, with the latter better motivating adaptive strategies for implementation. © 2006 Cambridge University Press.


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Details

Item Type: Article
Status: Published
Creators/Authors:
CreatorsEmailPitt UsernameORCID
Litman, Ddlitman@pitt.eduDLITMAN
Forbes-Riley, K
Centers: University Centers > Learning Research and Development Center (LRDC)
Date: 1 June 2006
Date Type: Publication
Journal or Publication Title: Natural Language Engineering
Volume: 12
Number: 2
Page Range: 161 - 176
DOI or Unique Handle: 10.1017/s1351324906004165
Schools and Programs: Dietrich School of Arts and Sciences > Computer Science
Dietrich School of Arts and Sciences > Intelligent Systems
Refereed: Yes
ISSN: 1351-3249
Date Deposited: 16 Oct 2014 18:34
Last Modified: 02 Feb 2019 15:59
URI: http://d-scholarship.pitt.edu/id/eprint/23241

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