Ability estimation in CAT with fuzzy logic

Computerized adaptive testing attempts to provide the most suitable question for an examinee depending on the examinee's ability to achieve the best result. Maximum likelihood estimation (MLE) and Bayesian likelihood estimation (BLE) have been provided to solve ability estimation and have good results in the literature. The situation when the answer of an item does not conform with the examinee's ability as expected nor standard derivation changes of the ability estimation was not study intensively. We propose that the fuzzy inference system can be used to infer flexible examinee's ability estimation by analyzing the relevant data of the examinee in a CAT test. This article introduce the theoretical starting point in affirming that a CAT (the CAT version of MAB-II), that has the scoring algorithm based on fuzzy predicts better candidates' abilities (200 engineers) than the same CAT classically scored as well as the traditional test MAB-II.