Machine learning prediction and analysis of students’ academic performance

Abstract

The aims of this research were to develop a machine learning prediction Decision Tree classification model and analyze the success of engineering students based on their performances during secondary school education. The success of students was analyzed and measured as a binomial response to whether students successfully finished the first and the second study years. The developed model examined general success, number of awards obtained at competitions, special awards, average grades in mathematics, physics, and one of the official state languages during secondary school as predictor variables. General success was defined by summing up students’ grade point averages (GPA) of each school year. The number of courses transferred from the first into the second study year and students’ GPA obtained during the first study year were added as predictor variables in the analysis and development of a prediction model for the student’s success during the second study year and their enrollment in the third study year. Data showed that majority of the students enrolled in the first study year were gymnasium or technical high school graduates. Developed machine learning prediction model showed that for the success of enrolled students in the first study year General Success of students during secondary school is the most important predictor variable, followed by mathematics and physics grades. However, for the success of the students enrolled in the second study year the most important predictor variable was number of the courses transferred from the first into the second study year, followed by students’ GPA obtained during the first study year and General Success. Machine learning Decision Tree classification modeling was shown to be an adequate tool for the prediction of the success of engineering students during the first and second study years.