Abstract:

This research describes our experience of applying multi-criteria decision aid (MCDA) in the educational data mining domain. One of the educational data mining goals is to predict the final student's performance, and analyzing their behavior. Several studies have been conducted to make use of different classifiers to reach this goal. We are contributing to this field by studying data analytic techniques applied to real-case studies to predict students’ performance according to their past academic experience. Hence, the aim of this research is to utilize MCDA in the education domain. The classification tool is used to predict students’ performance based several criteria such as: age, school, address, family size, evaluation in previous grades, and activities. Based on the case study used in this paper, we found that some attributes are more influential than others in predicting students’ performance. To check the performance, our proposed method was compared with other well-known classifiers, and a comparative and analytical study is conducted on well-known students’ data.