Predicting students' performance using mutli-criteria classification: A case study
Source of Publication
Proceedings of the 28th International Business Information Management Association Conference - Vision 2020: Innovation Management, Development Sustainability, and Competitive Economic Growth
One of the educational data mining goals is to predict students' performance, and analyzing their behavior. Several studies have been conducted to make use of different classifiers to reach this goal. In this research we describe our experience of applying multi-criteria decision aid (MCDA) in the educational data mining domain. 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 that is used to predict students' performance is based several criteria such as: age, school, address, family size, evaluation in previous grades, and activities. Based on the data used used in this paper, we found that some criteria are more influential than others in predicting students' performance. To check the performance, our proposed method was compared with a decision tree classifier, and a comparative and analytical study is conducted on well-known students' data.
International Business Information Management Association, IBIMA
Decision tree, Multi-criteria selection, Pre-processing, Students' assessment, Students' performance
Al-Obeidat, Feras and Tubasihat, Abdallah, "Predicting students' performance using mutli-criteria classification: A case study" (2016). All Works. 2748.
Indexed in Scopus