01373nas a2200133 4500008003900000245008400039210006900123300001200192490000700204520092900211100002201140700002401162856005301186 2014 d00aComputerized Adaptive Testing for the Random Weights Linear Logistic Test Model0 aComputerized Adaptive Testing for the Random Weights Linear Logi a415-4310 v383 a
This article discusses four-item selection rules to design efficient individualized tests for the random weights linear logistic test model (RWLLTM): minimum posterior-weighted -error minimum expected posterior-weighted -error maximum expected Kullback–Leibler divergence between subsequent posteriors (KLP), and maximum mutual information (MUI). The RWLLTM 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. The model extends and improves the well-known linear logistic test model in which feature effects are only estimated at the aggregate level. Simulations show that the efficiencies of the designs obtained with the different criteria appear to be equivalent. However, KLP and MUI are given preference over and due to their lesser complexity, which significantly reduces the computational burden.
1 aCrabbe, Marjolein1 aVandebroek, Martina uhttp://apm.sagepub.com/content/38/6/415.abstract