(60pp.) Recent developments in cognitive psychology suggest models for knowledge and learning that often fall outside the realm of standard test theory. This paper concerns probability-based inference in terms of such models. An approach utilizing Bayesian inference networks is outlined. Basic ideas of structure and computation in inference networks are discussed and illustrated with an example from the domain of mixed- number subtraction.