Item Response Theory (IRT); Computerized Adaptive Test (CAT); Weighted information.
The development of computerized adaptive tests was only possible due to the technological advances of the last decades, allowing this methodology to obtain estimates for the ability of the examinees based on a reduced number of items selected specifically for each respondent from their estimated latent trait. Its difficulties arise when a small group of items is exposed frequently, jeopardizing the security of the test. Thus, this research aims to propose a method for the item selection step, based on the use of information weighted by a power of order alpha of the current proportion of respondents not exposed to each item, in order to reduce the exposure rate of the items, so that they do not have very high exposure rates or items that have never been exposed even with a degree of difficulty close to the respondent’s real skill theta. The results demonstrate the advantages of the proposed methodology in relation to those already used, presenting better performance in the proportion of overexposed items with all values of alfa for random weighted information and increasing the proportion of exposed items for higher values of alpha in the weighted maximum information method, for the simulated item bank. The weighted maximum information method with random alpha presented the best performance among all the methods discussed here when applied to the real item bank. Other advantages related to the choice of alpha values are also mentioned.