Responding to Student Uncertainty in Spoken Tutorial Dialogue Systems

In designing and building tutorial dialogue systems it is important not only to understand the tactics employed by human tutors but also to understand how tutors decide when to use various tactics. We argue that these decisions are based not only on student problem-solving steps 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 often infrequent 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. Specifically, we describe a study aimed at using meta-communicative information to gauge student uncertainty and respond accordingly. In this study, we identify linguistic devices used by human tutors when responding to utterances containing signals of uncertainty, integrate these response strategies into two versions of SCoT, and evaluate their relative effectiveness. Our main hypothesis—that tutors are more effective if they use these linguistic devices in response to student uncertainty—was not confirmed, but our secondary hypothesis—that tutors using these linguistic devices are more effective than tutors that do not use them—was supported by the results.