A teaching model exploiting cognitive conflict driven by a bayesian network

Abstract

This paper describes the design and construction of a teaching model in an adaptive tutoring system designed to supplement normal instruction and aimed at changing students' conceptions of decimal numbers. The teaching model exploits cognitive conflict, incorporating a model of student misconceptions and task performance, represented by a Bayesian network. Preliminary evaluation of the implemented system shows that the misconception diagnosis and performance prediction performed by the BN reasoning engine supports the item sequencing and help presentation strategies required for teaching based on cognitive conflict. Field trials indicate the system provokes good long term learning in students who would otherwise be likely to retain misconceptions.