A Framework for Predicting Academic Success using Classification Method through Filter-Based Feature Selection

Students academic success is still a serious problem faced by higher education institutions worldwide. A strategy is needed to increase the students' academic performance and prevent students from failing. The need to get early accurate information about poor academic performance is a must and could achieved by constructing a prediction model. Therefore, an effective technique is required to provide the accurate information and improve the accuracy of the prediction model. This study evaluates the filter-based feature selection especially the filter-based feature ranking techniques for predicting academic success. It provides a comparative study of filter-based feature selection techniques for determining the type of features (redundant, irrelevant, relevant) that affect the accuracy of the prediction models. Furthermore, this study proposes a novel feature selection technique based on attribute dependency for improving the performance of the prediction model through a framework. The experimental results show that the proposed technique significantly improved the accuracy of the prediction models from 2-8%, outperforming the existing techniques, and the Decision Tree classifier performs best for predicting with an accuracy score of 92.64%.