Bayesian network approach to computerized adaptive testing

Abstract

For the personalized learning, a good testing method, which can effectively estimate a learner's proficiency, is required. In this paper, we propose a novel testing method, Bayesian network-based approach to Computerized Adaptive Testing (CAT). Our novel approach can estimate proficiency of the examinee effectively and efficiently because it reflects complicated relationships between all items and their categories, and can estimate detailed proficiency about each specific category. In experimental results, we show that our approach can improve accuracy and speed of estimating examinee's proficiency as compared with classical testing methods like paper-based test and conventional IRT-based CAT.