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Social Indicators Research

, Volume 146, Issue 1–2, pp 41–60 | Cite as

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

  • Annalina SarraEmail author
  • Lara Fontanella
  • Simone Di Zio
Article

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.

Keywords

Educational data mining Bayesian Profile Regression Dropout Higher education 

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Copyright information

© Springer Science+Business Media B.V., part of Springer Nature 2018

Authors and Affiliations

  • Annalina Sarra
    • 1
    Email author
  • Lara Fontanella
    • 1
  • Simone Di Zio
    • 1
  1. 1.Department of Legal and Social SciencesUniversity “G.d’Annunzio” of Chieti-PescaraPescaraItaly

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