Leveraging Hidden Dialogue State to Select Tutorial Moves
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Leveraging Hidden Dialogue State to Select Tutorial Moves


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Leveraging Hidden Dialogue State to Select Tutorial Moves Kristy Michael Robert Eun Young Mladen A. James C. Elizabeth D. ab a a a Phillips Ha Vouk Lestera ab Boyer Wallis aDepartment of Computer Science, North Carolina State University bApplied Research Associates Raleigh, NC, USA {keboyer, rphilli, eha, mdwallis, vouk, lester}@ncsu.edu derived from observing human tutors (e.g., Aleven, McLaren, Roll, & Koedinger, 2004; Evens & Abstract Michael, 2006; Graesser, Chipman, Haynes, & A central challenge for tutorial dialogue Olney, 2005; Jordan, Makatchev, Pappuswamy, systems is selecting an appropriate move VanLehn, & Albacete, 2006). While these systems given the dialogue context. Corpus-based can achieve results on par with unskilled human approaches to creating tutorial dialogue tutors, tutorial dialogue systems have not yet management models may facilitate more matched the effectiveness of expert human tutors flexible and rapid development of tutorial (VanLehn et al., 2007). dialogue systems and may increase the A more flexible model of strategy selection may effectiveness of these systems by allowing enable tutorial dialogue systems to increase their data-driven adaptation to learning contexts effectiveness by responding adaptively to a broader and to individual learners. This paper presents a family of models, including first-order range of contexts. A promising method for Markov, hidden Markov, and hierarchical deriving ...



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66 Proceedings of the NAACL HLT 2010 Fifth Workshop on Innovative Use of NLP for Building Educational Applications, pages 66–73, Los Angeles, California, June 2010. c2010 Association for Computational Linguistics