Predicting students performance using decision trees: Case of an Algerian University

The data produced from educational activities could be exploited in order to extract useful knowledge, assist educational decision makers in making better decisions and help students achieve better results. In this study, we report our findings about the application of a data mining technique following the CRISP-DM model at the department of Computer Science at the University of Jijel, Algeria. Our proposed system is able to classify undergraduate and post-graduate students according to their results and to predict their performance for the coming years based on their current results and on history data. The system can also be used as an early-warning tool for students at risk and to help graduates in choosing the appropriate Master's disciplines to pursue their studies.