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Student Desertion: What Is and How Can It Be Detected on Time?

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Data Science and Digital Business

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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Authors and Affiliations

  1. Department of Management Control and Information Systems, School of Economics and Business, University of Chile, Av. Diagonal Paraguay 257, 8330015, Santiago, Chile

    Jonathan Vásquez & Jaime Miranda

Authors
  1. Jonathan Vásquez
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  2. Jaime Miranda
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Corresponding author

Correspondence to Jaime Miranda .

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Editors and Affiliations

  1. ETSI Industriales de Ciudad Real, University of Castilla-La Mancha, Ciudad Real, Spain

    Fausto Pedro García Márquez

  2. LeBow College of Business, Drexel University, Philadelphia, PA, USA

    Benjamin Lev

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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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