Models for Conditional Probability Tables in Educational Assessment

Experts in educational assessment can often identify the skills needed to provide a solution for a test item and which patterns of those skills produce better expected performance. The method described here combines judgements about the structure of the conditional probability table (e.g., conjunctive, or compensatory) with Item Response Theory methods for partial credit scoring (Samejima, 1969) to produce a conditional probability table or a prior distribution for a learning algorithm. The structural judgements induce a projection of each con guration of parent skill variables onto a single latent responsepropensity . This is then used to calculate a probability for each cell in the table.