Predicting Students Drop Out a Case Study
Predicting Students Drop Out: A Case Study
Gerben W. Dekker1, Mykola Pechenizkiy2 and Jan M. Vleeshouwers1 g.w.dekker@student.tue.nl, {m.pechenizkiy, j.m.vleeshouwers}@tue.nl 1 Department of Electrical Engineering, Eindhoven University of Technology, the Netherlands 2 Department of Computer Science, Eindhoven University of Technology, the Netherlands
Abstract. The monitoring and support of university freshmen is considered very important at many educational institutions. In this paper we describe the results of the educational data mining case study aimed at predicting the Electrical Engineering (EE) students drop out after the first semester of their studies or even before they enter the study program as well as identifying success-factors specific to the EE program. Our experimental results show that rather simple and intuitive classifiers (decision trees) give a useful result with accuracies between 75 and 80%. Besides, we demonstrate the usefulness of cost-sensitive learning and thorough analysis of misclassifications, and show a few ways of further prediction improvement without having to collect additional data about the students.
1
Introduction
The monitoring and support of the first year students is a topic that is considered very important at many educational institutions. At some of the faculties yearly student enrollment for a bachelor program can be lower than desired, and when coupled with a high drop out rate of freshmen the need in effective approaches for predicting student drop out as well as identifying the factors affecting it speaks for itself. At the Electrical Engineering (EE) department of Eindhoven University of Technology (TU/e), the drop out rate of freshmen is about 40%. Apart from the department’s aim to enforce an upper bound to the drop-out rate, there are other reasons to want to identify successful and unsuccessful students in an early stage. In the Netherlands, there is the legal obligation that universities have to
Gerben W. Dekker1, Mykola Pechenizkiy2 and Jan M. Vleeshouwers1 g.w.dekker@student.tue.nl, {m.pechenizkiy, j.m.vleeshouwers}@tue.nl 1 Department of Electrical Engineering, Eindhoven University of Technology, the Netherlands 2 Department of Computer Science, Eindhoven University of Technology, the Netherlands
Abstract. The monitoring and support of university freshmen is considered very important at many educational institutions. In this paper we describe the results of the educational data mining case study aimed at predicting the Electrical Engineering (EE) students drop out after the first semester of their studies or even before they enter the study program as well as identifying success-factors specific to the EE program. Our experimental results show that rather simple and intuitive classifiers (decision trees) give a useful result with accuracies between 75 and 80%. Besides, we demonstrate the usefulness of cost-sensitive learning and thorough analysis of misclassifications, and show a few ways of further prediction improvement without having to collect additional data about the students.
1
Introduction
The monitoring and support of the first year students is a topic that is considered very important at many educational institutions. At some of the faculties yearly student enrollment for a bachelor program can be lower than desired, and when coupled with a high drop out rate of freshmen the need in effective approaches for predicting student drop out as well as identifying the factors affecting it speaks for itself. At the Electrical Engineering (EE) department of Eindhoven University of Technology (TU/e), the drop out rate of freshmen is about 40%. Apart from the department’s aim to enforce an upper bound to the drop-out rate, there are other reasons to want to identify successful and unsuccessful students in an early stage. In the Netherlands, there is the legal obligation that universities have to