A decision support system for predicting students’ performance

Ioannis E. Livieris, Tassos A. Mikropoulos, Panagiotis Pintelas


Educational data mining is an emerging research field concerned with developing methods for exploring the unique types of data that come from educational context. These data allow the educational stakeholders to discover new, interesting and valuable knowledge about students. In this paper, we present a new user-friendly decision support tool for predicting students’ performance concerning the final examinations of a school year. Our proposed tool is based on a hybrid predicting system incorporating a number of possible machine learning methods and achieves better performance than any examined single learning algorithm. Furthermore, significant advantages of the presented tool are that it has a simple interface and it can be deployed in any platform under any operating system. Our objective is that this work may be used to support student admission procedures and strengthen the service system in educational institutions.


Educational data mining, machine learning, decision support tool, prediction, student evaluation system

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