ABSTRACT
One-on-one tutoring that encourages students to explain their answers has long been known to be an effective means of increasing student performance, even when the tutors are far from experts in the field concerned (e.g., Chi, de Leeuw, Chiu, and LaVancher, 1994; Bloom, 1984). The design of effective Intelligent Tutoring Systems (ITS) is an area of active research that attempts to take advantage of the benefits of this type of tutoring with the added convenience of automated, just-in-time teaching interventions. Validating ITS dialogues requires comparison with human tutors constrained to conditions similar to those found in ITS interfaces. A basic assumption in the design of ITS is that student productions (questions, statements, and side comments) can be categorized in a way that permits selection of an appropriate tutor response. Advanced ITSs attempt to use Natural Language Processing (NLP) components to give the student an intervention tailored to their specific needs. For these systems to work, a detailed modeling of the conversations that occur during a domain specific tutoring session is desirable.
