Student dropout is a serious problem in education, there are many factors that can influence student dropout so it is not an easy issue to resolve. The scope of this research is to examine the accuracy of the ensemble techniques for predicting the st
udent dropout, particularly for primary school students in the Syrian Arab Republic.
The new classifier is designed based on the ensemble techniques “Stacking” and application of techniques Feature Selection where the database suffers from the problem of imbalance.
This new classifier has been compared with individual ones by using the Cross-Validation technique, the study concluded that the proposed classifier is the best among the others that have been compared to predict the student dropout.
