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Early Detection of Students at Risk – Predicting Student Dropouts Using Administrative Student Data and Machine Learning Methods

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  • Johannes Berens
  • Kerstin Schneider
  • Simon Görtz
  • Simon Oster
  • Julian Burghoff

Abstract

To successfully reduce student attrition, it is imperative to understand what the underlying determinants of attrition are and which students are at risk of dropping out. We develop an early detection system (EDS) using administrative student data from a state and a private university to predict student success as a basis for a targeted intervention. The EDS uses regression analysis, neural networks, decision trees, and the AdaBoost algorithm to identify student characteristics which distinguish potential dropouts from graduates. Prediction accuracy at the end of the first semester is 79% for the state university and 85% for the private university of applied sciences. After the fourth semester, the accuracy improves to 90% for the state university and 95% for the private university of applied sciences.

Suggested Citation

  • Johannes Berens & Kerstin Schneider & Simon Görtz & Simon Oster & Julian Burghoff, 2018. "Early Detection of Students at Risk – Predicting Student Dropouts Using Administrative Student Data and Machine Learning Methods," CESifo Working Paper Series 7259, CESifo.
  • Handle: RePEc:ces:ceswps:_7259
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    References listed on IDEAS

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    1. Ralph Stinebrickner & Todd Stinebrickner, 2014. "Academic Performance and College Dropout: Using Longitudinal Expectations Data to Estimate a Learning Model," Journal of Labor Economics, University of Chicago Press, vol. 32(3), pages 601-644.
    2. Ralph Stinebrickner & Todd R. Stinebrickner, 2014. "A Major in Science? Initial Beliefs and Final Outcomes for College Major and Dropout," Review of Economic Studies, Oxford University Press, vol. 81(1), pages 426-472.
    3. Ralph Stinebrickner & Todd Stinebrickner, 2008. "The Effect of Credit Constraints on the College Drop-Out Decision: A Direct Approach Using a New Panel Study," American Economic Review, American Economic Association, vol. 98(5), pages 2163-2184, December.
    4. John Bound & Michael F. Lovenheim & Sarah Turner, 2010. "Why Have College Completion Rates Declined? An Analysis of Changing Student Preparation and Collegiate Resources," American Economic Journal: Applied Economics, American Economic Association, vol. 2(3), pages 129-157, July.
    5. Tyler Ransom & Esteban Aucejo & Arnaud Maurel & Peter Arcidiacono, 2014. "College Attrition and the Dynamics of Information Revelation," 2014 Meeting Papers 529, Society for Economic Dynamics.
    6. Arulampalam, Wiji & Naylor, Robin A. & Smith, Jeremy P., 2005. "Effects of in-class variation and student rank on the probability of withdrawal: cross-section and time-series analysis for UK university students," Economics of Education Review, Elsevier, vol. 24(3), pages 251-262, June.
    7. Todd Stinebrickner & Ralph Stinebrickner, 2012. "Learning about Academic Ability and the College Dropout Decision," Journal of Labor Economics, University of Chicago Press, vol. 30(4), pages 707-748.
    8. Danilowicz-Gösele, Kamila & Meya, Johannes & Schwager, Robert & Suntheim, Katharina, 2014. "Determinants of students' success at university," Center for European, Governance and Economic Development Research Discussion Papers 214, University of Goettingen, Department of Economics.
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    Cited by:

    1. Behr Andreas & Giese Marco & Teguim K Herve D. & Theune Katja, 2020. "Early Prediction of University Dropouts – A Random Forest Approach," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 240(6), pages 743-789, December.
    2. Contini, Dalit & Salza, Guido, 2020. "Too few university graduates. Inclusiveness and effectiveness of the Italian higher education system," Socio-Economic Planning Sciences, Elsevier, vol. 71(C).

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    More about this item

    Keywords

    student attrition; machine learning; administrative student data; AdaBoost;
    All these keywords.

    JEL classification:

    • I23 - Health, Education, and Welfare - - Education - - - Higher Education; Research Institutions
    • H42 - Public Economics - - Publicly Provided Goods - - - Publicly Provided Private Goods
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics

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