2013/01/16 by David M. Williamson, Williamson, David M., Russell G. Almond +3
Computer Science · #AI-based Problem Solving and Planning #Applications (stat.AP) #Artificial Intelligence (cs.AI) #Bayesian Modeling and Causal Inference #FOS: Computer and information sciences #Intelligent Tutoring Systems and Adaptive Learning #Methodology (stat.ME)
paper · pdf · doi:10.48550/arxiv.1301.3902
openalex publication_date 2013/01/16 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The application of Bayesian networks (BNs) to cognitive assessment and intelligent tutoring systems poses new challenges for model construction. When cognitive task analyses suggest constructing a BN with several latent variables, empirical model criticism of the latent structure becomes both critical and complex. This paper introduces a methodology for criticizing models both globally (a BN in its entirety) and locally (observable nodes), and explores its value in identifying several kinds of misfit: node errors, edge errors, state errors, and prior probability errors in the latent structure. The results suggest the indices have potential for detecting model misfit and assisting in locating problematic components of the model.