  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.
