Identifying complex causal patterns in students' performance using machine learning

Today, in every academic institution as well as the university system assessing students' performance, identifying the uniqueness of each student and finding solutions to performance problems have become challenging issues. The main purpose of the study is to predict how student performance changes as a result of their behaviours, hobbies, extracurricular activities and different university activities. This study collected data from graduates via the online and supervised machine learning algorithms used to solve the problem. After pre-processing data, classification algorithms were applied, namely Random Forest, Multi-Layer Perceptron, Support Vector Machine, Naive Bayes and Decision Tree. The results show that the Multi-Layer Perceptron is the best algorithm considering the highest accuracy and lowest error values. An ensemble learning algorithm was then applied by combining those five algorithms. The best results were obtained using it, and according to the final results, ensemble learning increases the accuracy rather than each classifier.