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Improving an intelligent tutor’s comprehension of students with Latent Semantic Analysis

By Peter Wiemer-hastings, Katja Wiemer-hastings and Arthur C. Graesser

Abstract

AutoTutor is an intelligent tutor that interacts smoothly with the student using natural language dialogue. This type of interaction allows us to extend the domains of tutoring. We are no longer restricted to areas like mathematics and science where interaction with the student can be limited to typing in numbers or selecting possibilities with a button. Others have tried to implement tutors that interact via natural language in the past, but because of the difficulty of understanding language in a wide domain, their best results came when they limited student answers to single words. Our research directly addresses the problem of understanding what the student naturally says. One solution to this problem that has recently emerged is Latent Semantic Analysis (LSA). LSA is a statistical, corpus-based natural language understanding technique that supports similarity comparisons between texts. The success of this technique has been described elsewhere [3, 5, for example]. In this paper, we give an overview of LSA and how it is used in our tutoring system. Then we focus on an important issue fo

Publisher: IOS Press
Year: 1999
OAI identifier: oai:CiteSeerX.psu:10.1.1.417.9556
Provided by: CiteSeerX
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