Adaptive Testing Based on Bayesian Decision Theory

Abstract

To propose a new CAT(Computerized Adaptive Testing) algorithm, we regard selecting an item from an item bank as a decision making in Bayesian theory and propose a new item selection criterion we call “expected value of test information” (EVTI). The unique features of EVTI are that it 1) maximizes the prediction utility of an examinee’s ability estimation and 2) generates a decision tree with an item selection order based on the examinee’s responses. The CAT references the tree and then instantaneously selects and presents the optimal item from an item bank. Simulation results showed that the proposed method performed better than conventional methods.