A Case Study in Knowledge Discovery and Elicitation in an Intelligent Tutoring Application
This is an incremental study that addresses the challenge of knowledge engineering for domain-specific intelligent tutoring applications.
The paper tackled the problem of constructing Bayesian networks for intelligent tutoring systems by comparing expert elicitation with automated knowledge discovery methods, specifically in the context of decimal misconceptions, but did not report concrete numerical results.
Most successful Bayesian network (BN) applications to datehave been built through knowledge elicitation from experts.This is difficult and time consuming, which has lead to recentinterest in automated methods for learning BNs from data. We present a case study in the construction of a BN in anintelligent tutoring application, specifically decimal misconceptions. Wedescribe the BN construction using expert elicitation and then investigate how certainexisting automated knowledge discovery methods might support the BN knowledge engineering process.