This seventh in a series of articles discussing expert system construction focuses on two ways to create a structure that determines a decision: (1) rule-based, or deductive, implementation; and (2) example-based, or inductive, implementation. Probability factors and confidence levels are discussed, and an example is given for selecting an…

A Markov model for predicting performance on criterion-referenced tests is presented,. The model is expressed mathematically as a function of transition matrix, a current state vector, and a future state vector. The matrix is defined in terms of conditional probabilities, i.e., the probability of making a transition to a specific future…

The Mastery-Learning test model is extended. Methods for estimating prior probabilities are described. The use of an adjustment matrix to transform a probability of mastery measure and empirical methods for estimating adjustment matrix parameters are derived. Adjustment matrices are interpreted as indicators of instructional effectiveness and as…