Modeling the student in intelligent tutoring sytems: The promise of a new psychometrics

Abstract

This paper reviews a number of relatively new and promising psychometric approaches to the problem of modeling student achievement (the student model) within intelligent tutoring systems (ITS). A shared characteristic of most ITSs is their need to estimate a model of the student's understanding of the domain, and use this model to modify and adapt subsequent instructional content and sequence. Sound cognitive diagnosis and the need to advance ITS technology require the development of student models that are integrated with cognitive theory and instructional science. A number of cognitively oriented psychometric approaches — including latent-trait models, statistical pattern recognition methods, and causal probabilistic networks — are described and discussed within the current ITS framework. As measurement-based student models are refined, we anticipate their compatibility with future generations of intelligent tutoring systems.