An Estimation Method of Item Difficulty Index Combined with the Particle Swarm Optimization Algorithm for the Computerized Adaptive Testing

Abstract

The computerized adaptive testing is to provide items that are consistent with the current ability of testee, and to decide the difficulty for the next selected item according to the correctness of the testee’s answer. It achieves the goals of adaptive learning through the mechanisms of dynamic adjustment of item difficulty to accelerate the test process or to shorten the number of items in a test. A prerequisite of computerized adaptive testing is to estimate the difficulties of items correctly. In this study, we describe the parameters of items by an adjusted approach. It considers each knowledge block as an independent dimension and gives a value for each dimension of the difficulty. Combined with the particle swarm optimization algorithm, a dynamic item selection strategy is proposed to develop an adaptive testing system. Therefore, it adopts the multiple assessment methods for the abilities by giving a value for each dimension of the ability. By way of the the dynamic item selection in computerized adaptive testing, all the selected items will be highly correlated and more consistent with the current actual abilities of the testees.