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

Annalina Sarra (), Lara Fontanella () and Simone Zio ()
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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

Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, 2019, vol. 146, issue 1, No 4, 60 pages

Abstract: 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 (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1007/s11205-018-1901-8

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