Supervised machine learning models for student performance prediction

Abstract

Educational Data Mining has turned into an effective technique for revealing relationships hidden in educational data and predicting students’ learning outcomes. One can analyze data extracted from the students’ activity, educational and social behavior, and academic background. The outcomes which are produced are, the following: A personalized learning procedure, a feasible engagement with students’ behavior, a predictable interaction of the students with the learning processes and data. In the current work, we apply several supervised methods aiming at predicting the students’ academic performance. We prove that the use of the default parameters of learning algorithms on a voting generalization procedure of the three most accurate classifiers, can produce better results than any single tuned learning algorithm.