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Ability Estimation in CAT with Fuzzy Logic

Domenii publicaţii > Stiinte ingineresti + Tipuri publicaţii > Articol în volumul unei conferinţe

Autori: Dana V. Balas-Timar, Valentina E. Balas

Editorial: IEEE Catalog Number: CFP0936C-CDR, ISBN: 978-1-4244-5382-5, Library of Congress: 2009909581, ISCIII’09 Proceedings, 4th International Symposium on Computational Intelligence and Intelligent Informatics, 21-25 October 2009, Egypt, p.55-62, 2009.


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.

Cuvinte cheie: Fuzzy logic, Computerized adaptive testing (CAT), Fuzzy Inference System