When to Intervene: Toward a Markov Decision Process Dialogue Policy for Computer Science Tutoring.
Christopher M. Mitchell, Kristy Elizabeth Boyer, James C. Lester · 2013
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 when to intervene, capturing the most effective tutor turn-taking behaviors in a task-oriented learning environment with textual dialogue. This framework is developed as a part of the JavaTutor tutorial dialogue project and will contribute to data-driven development of a tutorial dialogue system for introductory computer science education.