Machine learning methods as auxiliary tool for effective mathematics teaching

Abstract Seeing mathematics teaching as a very demanding and responsible process while having in mind the importance of mathematical knowledge for students of technical faculties, this paper aims to present heuristics for student classification according to their predicted mathematical success. Over the last few decades, the process of informatization of universities has resulted in new challenges universities are faced with. Due to the widespread use of educational databases, which opens new possibilities for educational data mining and analyses, machine learning algorithms have become a very popular tool for predicting students' academic performance. The decision tree algorithm is used in this paper for the classification and prediction of students' mathematical performance and it is trained on the data collected from the educational information system. The experimental results show that the model accuracy is 72% with an error rate of 0.28. The implementation of the Decision Tree Model to predict whether a student will pass, fail or be conditional in mathematical courses is important for both teachers and students, as well as for universities. Students' performance is one of the major keys in evaluating the quality of the teaching process, but also for evaluating the overall success of the university itself. As mathematics is considered a basic and important discipline, it is clear why predicting students' mathematical achievement is crucial for all levels of university organization.