Open AccessTowards Association Rule-based Item Selection Strategy in Computerized Adaptive Testing
Author(s) -
Josué Pacheco Ortiz,
Lisbeth Rodríguez-Mazahua,
Jezreel Mejía,
Isaac Machorro-Cano,
Giner Alor-Hernández,
Ulises Juárez-Martínez
Publication year - 2020
Publication title -
revista perspectiva empresarial
Language(s) - English
Resource type - Journals
eISSN - 2389-8194
pISSN - 2389-8186
DOI - 10.16967/23898186.666
Subject(s) - association rule learning , apriori algorithm , computer science , data mining , a priori and a posteriori , selection (genetic algorithm) , set (abstract data type) , association (psychology) , machine learning , artificial intelligence , philosophy , epistemology , programming language
One of the most important stages of Computerized Adaptive Testing is the selection of items, in which various methods are used, which have certain weaknesses at the time of implementation. Therefore, in this paper, it is proposed the integration of Association Rule Mining as an item selection criterion in a CAT system. We present the analysis of association rule mining algorithms such as Apriori, FP-Growth, PredictiveApriori and Tertius into two data set with the purpose of knowing the advantages and disadvantages of each algorithm and choose the most suitable. We compare the algorithms considering number of rules discovered, average support and confidence, and velocity. According to the experiments, Apriori found rules with greater confidence, support, in less time.