Abstract
Designing dialogue systems that engage in rich tutorial dialogue has long been a goal of the intelligent tutoring systems community. A key challenge for these systems is determining when to intervene during student problem solving. Although intervention strategies have historically been hand-authored, utilizing machine learning to automatically acquire corpus-based intervention policies that maximize student learning holds great promise. To this end, this paper presents a Markov Decision Process (MDP) framework to learn an intervention policy capturing the most effective tutor turn-taking behaviors in a task-oriented learning environment with textual dialogue. The model and its learned policy highlight important design considerations, including maintaining tutor engagement during student problem solving and avoiding multiple consecutive interventions.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Bloom, B.: The 2 sigma problem: The search for methods of group instruction as effective as one-to-one tutoring. Educational Researcher 13, 4–16 (1984)
VanLehn, K., Graesser, A.C., Jackson, G.T., Jordan, P., Olney, A., Rosé, C.P.: When Are Tutorial Dialogues More Effective Than Reading? Cognitive Science 30, 3–62 (2007)
Chi, M., VanLehn, K., Litman, D.: Do Micro-Level Tutorial Decisions Matter: Applying Reinforcement Learning to Induce Pedagogical Tutorial Tactics. In: Aleven, V., Kay, J., Mostow, J. (eds.) ITS 2010, Part I. LNCS, vol. 6094, pp. 224–234. Springer, Heidelberg (2010)
Tetreault, J.R., Litman, D.J.: A Reinforcement Learning Approach to Evaluating State Representations in Spoken Dialogue Systems. Speech Communication 50(8), 683–696 (2008)
Grafsgaard, J.F., Fulton, R.M., Boyer, K.E., Weibe, E.N., Lester, J.L.: Multimodal Analysis of the Implicit Affective Channel in Computer-Mediated Textual Communication. In: Proceedings of the International Conference on Multimodal Interaction, pp. 145–152 (2012)
Sutton, R., Barto, A.: Reinforcement Learning. MIT Press, Cambridge (1998)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Mitchell, C.M., Boyer, K.E., Lester, J.C. (2013). A Markov Decision Process Model of Tutorial Intervention in Task-Oriented Dialogue. In: Lane, H.C., Yacef, K., Mostow, J., Pavlik, P. (eds) Artificial Intelligence in Education. AIED 2013. Lecture Notes in Computer Science(), vol 7926. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39112-5_123
Download citation
DOI: https://doi.org/10.1007/978-3-642-39112-5_123
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-39111-8
Online ISBN: 978-3-642-39112-5
eBook Packages: Computer ScienceComputer Science (R0)