Abstract
Student attrition is a voluntary/involuntary failure or early dropout to complete a program in which an individual enrolled. For voluntary desertions, detection is more complex due to a variety of factors related to the program and individual context. National academics have complained of a research shortage about desertion student investigations in the Chilean context. We applied data-mining techniques in order to reduce lack of studies and identify key factors and predict desertions for first 6 semesters in a program of Business School at Universidad de Chile; 288 hybrid models were built and the 6 final best models are composed of techniques of clustering, optimal-threshold classifiers, and SVM and Logistic Regression algorithms. In addition, they showed most important variables are related to University Selection Test (PSU in Spanish) score, followed by the educational level of parents and academic performances. On the second level of importance are funding and family configuration.
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Acknowledgements
Authors would like to appreciate the help provided in the data access for this investigation by Ariel La Paz, Director of Information Systems and Business School, Cesar Ortega, Chair of the Student Records Unit, and Marcia Oyarce, Social Assistant.
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Vásquez, J., Miranda, J. (2019). Student Desertion: What Is and How Can It Be Detected on Time?. In: García Márquez, F., Lev, B. (eds) Data Science and Digital Business. Springer, Cham. https://doi.org/10.1007/978-3-319-95651-0_13
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DOI: https://doi.org/10.1007/978-3-319-95651-0_13
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