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Proceedings of the 14th International Conference on Artificial Intelligence in Education (AIED)

dc.contributor.authorLitman, Diane
dc.contributor.authorMoore, Johanna D.
dc.contributor.authorDzikovska, Myroslava
dc.contributor.authorFarrow, Elaine
dc.date.accessioned2010-10-28T10:15:36Z
dc.date.available2010-10-28T10:15:36Z
dc.date.issued2010-10-28T10:04:40Z
dc.identifier.isbn978-1-60750-028-5en
dc.identifier.urihttp://portal.acm.org/citation.cfm?id=1659476&dl=GUIDE,en
dc.identifier.urihttp://hdl.handle.net/1842/4097
dc.description.abstractOur research goal is to investigate whether previous findings and methods in the area of tutorial dialogue can be generalized across dialogue corpora that differ in domain (mechanics versus electricity in physics), modality (spoken versus typed), and tutor type (computer versus human). We first present methods for unifying our prior coding and analysis methods. We then show that many of our prior findings regarding student dialogue behaviors and learning not only generalize across corpora, but that our methodology yields additional new findings. Finally, we show that natural language processing can be used to automate some of these analyses.en
dc.language.isoenen
dc.subjectDiscourseen
dc.subjectIntelligent Tutoringen
dc.subjectNatural Languageen
dc.subjectTutorial Dialogueen
dc.titleUsing Natural Language Processing to Analyze Tutorial Dialogue Corpora Across Domains and Modalitiesen
dc.typeConference Paperen
rps.titleProceedings of the 14th International Conference on Artificial Intelligence in Education (AIED)en
dc.extent.noOfPages8en
dc.date.updated2010-10-28T10:15:36Z
dc.date.openingDate2009-07-06
dc.date.closingDate2009-07-10


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