Computerized Adaptive Testing for Classifying Examinees into three Categories

Abstract

The objective of this study was to explore the possibilities for using computerized adaptive testing in situations in which examinees are to be classified into one of three categories. Testing algorithms with two different statistical computation procedures are described and evaluated. The first computation procedure is based on statistical testing and the other on statistical estimation. Item selection methods based on maximum information (MI) considering content and exposure control are considered. The measurement quality of the proposed testing algorithms is reported. The results of the study are that a reduction of at least 22% in the mean number of items can be expected in a computerized adaptive test (CAT) compared to an existing paper-and-pencil placement test. Furthermore, statistical testing is a promising alternative to statistical estimation. Finally, it is concluded that imposing constraints on the MI selection strategy does not negatively affect the quality of the testing algorithms.