Item exposure control for multidimensional computer adaptive testing under maximum likelihood and expected a posteriori estimation.

Abstract

Item bank stratification has been shown to be an effective method for combating item overexposure in both uni- and multidimensional computer adaptive testing. However, item bank stratification cannot guarantee that items will not be overexposed-that is, exposed at a rate exceeding some prespecified threshold. In this article, we propose enhancing stratification for multidimensional computer adaptive tests by combining it with the item eligibility method, a technique for controlling the maximum exposure rate in computerized tests. The performance of the method was examined via a simulation study and compared to existing methods of item selection and exposure control. Also, for the first time, maximum likelihood (MLE) and expected a posteriori (EAP) estimation of examinee ability were compared side by side in a multidimensional computer adaptive test. The simulation suggested that the proposed method is effective in suppressing the maximum item exposure rate with very little loss of measurement accuracy and precision. As compared to MLE, EAP generates smaller mean squared errors of the ability estimates in all simulation conditions.