Recognizing Effective and Student-Adaptive Tutor Moves in Task-Oriented Tutorial Dialogue

One-on-one tutoring is significantly more effective than traditional classroom instruction. In recent years, automated tutoring systems are approaching that level of effectiveness by engaging students in rich natural language dialogue that contributes to learning. A promising approach for further improving the effectiveness of tutorial dialogue systems is to model the differential effectiveness of tutorial strategies, identifying which dialogue moves or combinations of dialogue moves are associated with learning. It is also important to model the ways in which experienced tutors adapt to learner characteristics. This paper takes a corpus- based approach to these modeling tasks, presenting the results of a study in which task-oriented, textual tutorial dialogue was collected from remote one-on-one human tutoring sessions. The data reveal patterns of dialogue moves that are correlated with learning, and can directly inform the design of student-adaptive tutorial dialogue management systems.