Abstract
Since the nineties, Data Mining (DM) has shown to be a privileged partner in business by providing the organizations a rich set of tools to extract novel and useful knowledge from databases. In this paper, a DM application in the highly competitive market of educational services is presented. A model was built by combining a set of classifiers into a committee machine to predict the likelihood that a student who completed his/her second term will remain in the institution until graduation.The model was applied to undergraduate student records in a higher education institution in Brasília, the capital of Brazil, and has shown to be predictive for evasion in a high accuracy. The unbiased selection of students with elevated evasion risk affords the institution the opportunity to devise mitigation strategies and preempt a decision by the student to evade.
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Balaniuk, R., Antonio do Prado, H., da Veiga Guadagnin, R., Ferneda, E., Cobbe, P.R. (2011). Predicting Evasion Candidates in Higher Education Institutions. In: Bellatreche, L., Mota Pinto, F. (eds) Model and Data Engineering. MEDI 2011. Lecture Notes in Computer Science, vol 6918. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24443-8_16
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DOI: https://doi.org/10.1007/978-3-642-24443-8_16
Publisher Name: Springer, Berlin, Heidelberg
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