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Identifying Students at Risk of Academic Failure Within the Educational Data Mining Framework

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  • Annalina Sarra

    (University “G.d’Annunzio” of Chieti-Pescara)

  • Lara Fontanella

    (University “G.d’Annunzio” of Chieti-Pescara)

  • Simone Zio

    (University “G.d’Annunzio” of Chieti-Pescara)

Abstract

Data mining is widely considered a powerful instrument for searching and acquiring essential relationships among different variables/attributes in a database. Data mining applied in the educational framework is referred to as educational data mining (EDM). EDM enables to get insights into various higher education phenomena, such as students’ academic paths, learning behaviours and determinants of academic success or dropout. In this paper, we aim at evaluating the usefulness of a particular latent class model, the Bayesian Profile Regression, for the identification of students more likely to drop out. Considering students’ performance, motivation and resilience, this technique allows to draw the profiles of students with a higher risk of academic failure. The working example is based on real data collected through an online questionnaire filled in by undergraduate students of an Italian University.

Suggested Citation

  • Annalina Sarra & Lara Fontanella & Simone Zio, 2019. "Identifying Students at Risk of Academic Failure Within the Educational Data Mining Framework," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 146(1), pages 41-60, November.
    • Handle: RePEc:spr:soinre:v:146:y:2019:i:1:d:10.1007_s11205-018-1901-8
    DOI: 10.1007/s11205-018-1901-8
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    References listed on IDEAS

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    1. Jeremy P. Smith & Robin A. Naylor, 2001. "Dropping out of university: A statistical analysis of the probability of withdrawal for UK university students," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 164(2), pages 389-405.
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    3. Liverani, Silvia & Hastie, David I. & Azizi, Lamiae & Papathomas, Michail & Richardson, Sylvia, 2015. "PReMiuM: An R Package for Profile Regression Mixture Models Using Dirichlet Processes," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 64(i07).
    4. Scott L. Thomas, 2000. "Ties That Bind," The Journal of Higher Education, Taylor & Francis Journals, vol. 71(5), pages 591-615, September.
    5. J. -P. Vandamme & N. Meskens & J. -F. Superby, 2007. "Predicting Academic Performance by Data Mining Methods," Education Economics, Taylor & Francis Journals, vol. 15(4), pages 405-419.
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    Cited by:

    1. Rafaella L. S. Nascimento & Roberta A. de A. Fagundes & Renata M. C. R. Souza, 2022. "Statistical Learning for Predicting School Dropout in Elementary Education: A Comparative Study," Annals of Data Science, Springer, vol. 9(4), pages 801-828, August.
    2. Malte Sandner & Alexander Patzina & Silke Anger & Sarah Bernhard & Hans Dietrich, 2023. "The COVID-19 pandemic, well-being, and transitions to post-secondary education," Review of Economics of the Household, Springer, vol. 21(2), pages 461-483, June.
    3. Usala, Cristian & Primerano, Ilaria & Santelli, Francesco & Ragozini, Giancarlo, 2024. "The more the better? How degree programs’ variety affects university students’ churn risk," Socio-Economic Planning Sciences, Elsevier, vol. 94(C).
    4. Sahar Saeed Rezk & Kamal Samy Selim, 2024. "Comparing nine machine learning classifiers for school-dropouts using a revised performance measure," Journal of Computational Social Science, Springer, vol. 7(2), pages 1555-1597, October.

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