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Comparison of Neural Network with Gradient Boosted Trees, Random Forest, Logistic Regression and SVM in predicting student achievement

Student success is paramount at all levels of education, especially for universities. Improving the success and quality of enrolled students is one of the most important concerns. It is important to observe the initial symptoms of students at risk and implement earlier preventive measures to determine the cause of the student dropout rate. In this research, we will use data mining techniques to identify the factors that affect student success. We will use the data consisting of log data and grades from students from a course at the University of Zagreb, Faculty of Organization and Informatics in Croatia. We will use the data that consists of log data and grades of students from a course at the University of Zagreb, Faculty of Organization and Informatics in Croatia. In this study, machine learning methods are used to evaluate the performance of deep learning compared to traditional machine learning methods in the task of binary classification of whether a student fails or passes the exam. The results show that the deep neural network has a very good performance, the second-best, and with the optimizations, there are many opportunities for even better generalization.