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.
|Title of host publication||Proceedings of the 2nd International Conference on Educational Data Mining, EDM 2009, July 1-3, 2009. Cordoba, Spain|
|Editors||T. Barnes, M. Desmarais, C. Romero, S. Ventura|
|Publication status||Published - 2009|