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AIED
2009
Springer

Discovering Tutorial Dialogue Strategies with Hidden Markov Models

12 years 2 months ago
Discovering Tutorial Dialogue Strategies with Hidden Markov Models
Identifying effective tutorial strategies is a key problem for tutorial dialogue systems research. Ongoing work in human-human tutorial dialogue continues to reveal the complex phenomena that characterize these interactions, but we have not yet seen the emergence of an automated approach to discovering tutorial dialogue strategies. This paper presents a first step toward establishing a methodology for such an approach. In this methodology, a corpus is first annotated with dialogue acts that are grounded in theories of tutoring and natural language dialogue. Hidden Markov modeling is then applied to discover tutorial strategies inherent in the structure of the sequenced dialogue acts. The methodology is illustrated by demonstrating how hidden Markov models can be learned from a corpus of human-human tutoring in the domain of introductory computer science. Keywords. Tutorial dialogue, tutorial strategies, machine learning, hidden Markov modeling.
Kristy Elizabeth Boyer, Eunyoung Ha, Michael D. Wa
Added 25 May 2010
Updated 25 May 2010
Type Conference
Year 2009
Where AIED
Authors Kristy Elizabeth Boyer, Eunyoung Ha, Michael D. Wallis, Robert Phillips, Mladen A. Vouk, James C. Lester
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