, Volume 18, Issue 4, pp 349-382
Date: 30 Jan 2008

A multifactor approach to student model evaluation

Rent the article at a discount

Rent now

* Final gross prices may vary according to local VAT.

Get Access

Abstract

Creating student models for Intelligent Tutoring Systems (ITS) in novel domains is often a difficult task. In this study, we outline a multifactor approach to evaluating models that we developed in order to select an appropriate student model for our medical ITS. The combination of areas under the receiver-operator and precision-recall curves, with residual analysis, proved to be a useful and valid method for model selection. We improved on Bayesian Knowledge Tracing with models that treat help differently from mistakes, model all attempts, differentiate skill classes, and model forgetting. We discuss both the methodology we used and the insights we derived regarding student modeling in this novel domain.