Computerized Adaptive Testing for the Random Weights Linear Logistic Test Model

This paper discusses four item selection rules to design efficient individualized tests for the random weights linear logistic test model: minimum posterior weighted (DB) and minimum expected posterior weighted (EDB) D-error, maximum expected Kullback-Leibler divergence between subsequent posteriors (KLP) and maximum mutual information (MUI). The random weights linear logistic test model decomposes test items into a set of subtasks or cognitive features and assumes individual-specific effects of the features on the difficulty of the items. In contrast to a single ability score, the individual effects provide a more profound profile of a test taker's proficiency, giving one's strengths and weaknesses with respect to the item features. Simulations show how the design efficiency of the different criteria appears to be equivalent. However, KLP and MUI are given preference over DB and EDB due to their lower complexity, highly reducing the computational intensity.