ALICAT: a customized approach to item selection process in computerized adaptive testing

Computerized adaptive testing (CAT) based on item response theory allows more accurate assessments with fewer questions than the classic paper and pencil (P&P) test. Nonetheless, the CAT construction involves some key questions that, when done properly, can further improve the accuracy and efficiency in estimating the examinees’ abilities. One of the main questions is in regard to choosing the item selection rule (ISR). The classic CAT makes exclusive use of one ISR. However, these rules have differences depending on the examinees’ ability level and on the CAT stage. Thus, the objective of this work is to reduce the dichotomous test size which is inserted in a classic CAT with no significant loss of accuracy in the estimation of the examinee’s ability level. For this purpose, we analyze the ISR performance and then build a personalized item selection process in CAT considering the use of more than one rule. The case study in Mathematics and its Technologies test of the ENEM 2012 shows that the Kullback-Leibler information with a posterior distribution ( KLP ) has better performance in the examinees’ ability estimation when compared with Fisher information ( F ), Kullback-Leibler information ( KL ), maximum likelihood weighted information ( MLWI ), and maximum posterior weighted information ( MPWI ) rules. Previous results in the literature show that CAT using KLP was able to reduce this test size by 46.6 % from the full size of 45 items with no significant loss of accuracy in estimating the examinees’ ability level. In this work, we observe that the F and the MLWI rules performed better on early CAT stages to estimate examinees’ proficiency level with extreme negative and positive values, respectively. With this information, we were able to reduce the same test by 53.3 % using the personalized item selection process, called ALICAT, which includes the best rules working together.