Elizabeth Owen Bratt

Advantages of Spoken Language Interaction in Dialogue-Based Intelligent Tutoring Systems

The ability to lead collaborative discussions and appropriately scaffold learning has been identified as one of the central advantages of human tutorial interaction [6]. In order to reproduce the effectiveness of human tutors, many developers of tutorial dialogue systems have taken the approach of identifying human tutorial tactics and then incorporating them into their systems. Equally important as understanding the tactics themselves is understanding how human tutors decide which tactics to use. We argue that these decisions are made based not only on student actions and the content of student utterances, but also on the meta-communicative information conveyed through spoken utterances (e.g. pauses, disfluencies, intonation). Since this information is less frequent or unavailable in typed input, tutorial dialogue systems with speech interfaces have the potential to be more effective than those without. This paper gives an overview of the Spoken Conversational Tutor (SCoT) that we have built and describes how we are beginning to make use of spoken language information in SCoT.

Contextualizing learning in a reflective conversational tutor

Contextualizing learning in an intelligent tutoring system is difficult for many reasons. Goals such as presenting material in an understandable manner, minimizing confusion and frustration, and helping the student reason about their actions all need to be balanced. Previous research has shown reflective discussions (with human tutors) occurring after problem-solving to be effective in helping students reason about their own actions (S. Katz et. al, 2003). However, leading a reflective discussion makes it difficult to present information in an understandable manner, and without contextualization, it is easy to create student confusion and frustration. This raises the question: how can intelligent tutoring systems effectively contextualize learning in a reflective discussion? In this paper, we describe the tutorial architecture of SCoT, a spoken conversational tutor that uses flexible, adaptive planning and multi-modal task modeling to support the contextualization of learning in reflective dialogues.

Spoken dialogue for simulation control and conversational tutoring

This demonstration shows a flexible tutoring system for studying the effects of different tutoring strategies enhanced by a spoken language interface. The hypothesis is that spoken language increases the effectiveness of automated tutoring. The domain is Navy damage control.