A Decision Support System for Predicting Student Performance

In recent years data mining has been successfully implemented in the business world. Evaluating students' academic success is becoming increasingly challenging, its use is intended for identification and extraction of new and potentially valuable knowledge from the data. Predicting educational outcome is a practical alternative heterogeneous environment. Performance prediction models can be built by applying data mining techniques to enrolment data. In this paper we present an Naive Bayes algorithm (NB) approach to predict graduating cumulative Grade Point Average based on applicant data collected from the surveys conducted during the summer semester at the University of Tuzla, the Faculty of Economics, academic year 2010-2011, among first year students and the data taken during the enrolment. The Naive Bayes algorithm is used to discover the most suited way to predict student's success.

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