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2010
Springer

Characterizing the Effectiveness of Tutorial Dialogue with Hidden Markov Models

10 years 9 months ago
Characterizing the Effectiveness of Tutorial Dialogue with Hidden Markov Models
Identifying effective tutorial dialogue strategies is a key issue for intelligent tutoring systems research. Human-human tutoring offers a valuable model for identifying effective tutorial strategies, but extracting them is a challenge because of the richness of human dialogue. This paper addresses that challenge through a machine learning approach that 1) learns tutorial strategies from a corpus of human tutoring, and 2) identifies the statistical relationships between student outcomes and the learned strategies. We have applied hidden Markov modeling to a corpus of annotated task-oriented tutorial dialogue to learn one model for each of two effective human tutors. We have identified significant correlations between the automatically extracted tutoring modes and student learning outcomes. This work has direct applications in authoring data-driven tutorial dialogue system behavior and in investigating the effectiveness of human tutoring.
Kristy Elizabeth Boyer, Robert Phillips, Amy Ingra
Added 19 Jul 2010
Updated 19 Jul 2010
Type Conference
Year 2010
Where ITS
Authors Kristy Elizabeth Boyer, Robert Phillips, Amy Ingram, Eunyoung Ha, Michael D. Wallis, Mladen A. Vouk, James C. Lester
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