Towards Association Rule-Based Item Selection Strategy in Computerized Adaptive Testing

Abstract

One of the most important stages of Computerized Adaptive Testing (CAT) is the selection of items, in which various methods are used, which have certain weaknesses at the time of implementation. Therefore, in this chapter, the integration of Association Rule Mining is proposed as an item selection criterion in a CAT system. Specifically, we present the analysis of association rule mining algorithms such as Apriori, FPGrowth, PredictiveApriori, and Tertius into three data sets obtained from the subject Databases, to know the advantages and disadvantages of each algorithm and choose the most suitable one to employ in an association rule-based CAT system that is being developed as a Ph.D. project. We compare the algorithms considering the number of rules discovered, average support and confidence, lift, and velocity. According to the experiments, Apriori found rules with greater confidence, support, lift, and in less time.