Discovering Tutorial Dialogue Strategies with Hidden Markov Models

Authors:
Kristy Elizabeth Boyer;Eun Young Ha;Michael D. Wallis;Robert Phillips;Mladen A. Vouk;James C. Lester
Affiliations:
Department of Computer Science, North Carolina State University, Raleigh, North Carolina, USA;Department of Computer Science, North Carolina State University, Raleigh, North Carolina, USA;Department of Computer Science, North Carolina State University, Raleigh, North Carolina, USA and Applied Research Associates, Inc., Raleigh, North Carolina, USA;Department of Computer Science, North Carolina State University, Raleigh, North Carolina, USA and Applied Research Associates, Inc., Raleigh, North Carolina, USA;Department of Computer Science, North Carolina State University, Raleigh, North Carolina, USA;Department of Computer Science, North Carolina State University, Raleigh, North Carolina, USA
Venue:
Proceedings of the 2009 conference on Artificial Intelligence in Education: Building Learning Systems that Care: From Knowledge Representation to Affective Modelling
Year:
2009

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Abstract

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.