Predicting Academic Achievement with Machine Learning Algorithms

Education systems produce a large number of valuable data for all stakeholders. The processing of these educational data and making studies on the future of education based on the data reveal highly meaningful results. In this study, an insight was tried to be developed on the educational data collected from ninth-grade students by using data mining methods. The data contains demographic information about students and their families, studying routines, behaviours of attending learning activities, and their epistemological beliefs about science. Thus, this research aimed to solve a classification problem, two-class (successful or unsuccessful according to the exam result) was tried to be estimated from the collected data. In the study, the prediction accuracy of the supervised classification algorithms were compared and it was defined which variables were effective in the formation of classes. When the prediction accuracy of machine learning algorithms was compared, the findings indicated that the Neural Network algorithm (98.6%) had the highest score. The information gain coefficient of the variables was examined to determine the factors affecting the prediction accuracy. It was re