Learning From Students To Improve An Intelligent Tutoring System

Abstract

SIFT, a Self-Improving Fractions Tutor, is an intelligent tutoring system which learns from its interactions with the students it tutors. Its learning module takes transcripts of tutoring sessions as input. It analyzes the results of its work and modifies its knowledge bases in domainspecific ways, creating new tutorial rules and extending its domain knowledge. To produce its transcripts, SIFT tutors student-simulations which interact with the tutor to solve problems. SIFT generates a rule for each hypothesis that could possibly explain why it took inappropriate or wrong actions, although not all hypotheses are ultimately valid. It initially assumes that new rules have equal correctness probability. It then continues to tutor (using its probabilistic conflict-resolution rule-selection algorithm) and uses feedback from its rule applications to modify probabilities with the Dempster-Shafer theory of evidence. Initial results show that (1) SIFT learns more rules than it uses, but eventual..