Students performance prediction using hybrid classifier technique in incremental learning Online publication date: Tue, 30-Jul-2019
by Roshani Ade
International Journal of Business Intelligence and Data Mining (IJBIDM), Vol. 15, No. 2, 2019
Abstract: The performance in higher education is a turning point in the academics for all students. This academic performance is influenced by many factors, therefore, it is essential to develop predictive data mining model for student's performance so as to identify the difference between high learners and slow learners student. The knowledge is hidden among the educational data set and it is extractable through data mining techniques. In our paper, we used the hybrid classifier approach for the prediction of student's performance using fuzzy ARTMAP and Bayesian ARTMAP classifier. Sensitivity analysis was performed and irrelevant inputs were eliminated. The performance measures used to determine the performance of the techniques include Matthews correlation coefficient (MCC), accuracy rate, true positive, false positive and percentage correctly classified instances. The combined result gives the good accuracy for predicting students' performance while using this approach. Thus, an enhanced prediction method for students is obtained.
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