Cohesion and Learning in a Tutorial Spoken Dialog System

Two measures of lexical cohesion were developed and applied to a corpus of human-computer tutoring dialogs. For both measures, the amount of cohesion in the tutoring dialog was found to be significantly correlated to learning for students with below-mean pretest scores, but not for those with above-mean pre-test scores, even though both groups had similar amounts of cohesion. We also find that only cohesion between tutor and student is significant: the cohesiveness of tutor, or of student, utterances is not. These results are discussed in light of previous work in textual cohesion and recall.

• Type: Conference
• Year: 2006
• Where: FLAIRS
• Authors: Arthur Ward, Diane J. Litman
• Keywords: Artificial Intelligence, FLAIRS 2006, Human-computer Tutoring Dialogs, Lexical Cohesion, Tutoring Dialog