Success Is Hidden in the Students' Data

The contribution of data mining to education as well as research in this area is done on a variety of levels and can affect the instructors’ approach to learning. This particular study focuses on problems associated with classification and attribute selection. An effort to forecast the results takes place before the educational process ends in order to prevent a potential learning failure. The methodology used during the experiments excluded the case of overfitting and ensured the completion of the study. Particular emphasis was placed on analyzing the results, which demonstrated the superiority of the Pearson VII function kernel using the Support Vector Machines algorithm to the Bagging meta-learning method. We also determined the appropriate point in the course timeline in order to get reliable results regarding students’ outcome and finally, attribute selection gave us interesting results, in terms of students’ data.