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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References
Hand, D., Mannila, H., Smyth, P.: Principles of Data Mining. The MIT Press, Cambridge (2001)
National Institute for Educational Research Anisio Teixeira (Inep): Higher Education Statistical Synopsis (2008), http://portal.inep.gov.br
National Institute for Educational ResearchAnisio Teixeira (Inep): Technical Summary – Higher Education Census 2008, Preliminary Data (2009), http://portal.inep.gov.br
UNESCO Institute for Statistics: International Standard Classification of Education Key Statistics (2011), http://www.uis.unesco.org
Cardoso, J.: Brazilian GDP expected to grow 7,5% in 2010 and 4,3% in 2011, forecasts OCDE. Jornal Valor Online, São Paulo, November 18 (2010)
Lima, M.C.: The WTO and the “Educational Market”. Reasons Behind the Interest and Possible Consequences. In: VI International Colloquium on Higher Education Management in South America, Blumenau (2006)
Campos, E.: Dollar Closes at R$ 1,740 and Negates Losses for the Year. Jornal Valor Online, São Paulo, November 16 (2010)
Bergamo, F., Farah, O.E., Giuliani, A.C.: Loyalty and Higher Education: Stategic Tool in Client Retention. Revista Gerenciais 6(1), 55–62 (2007)
Gaioso, N.P.L.: Student Evasion in Higher Education: Student and Management Perspectives. UniversidadeCatólica de Brasília, Brasília (2005)
Wolpert, D.: The Lack of a Priori Distinctions between Learning Algorithms. Neural Computation 8(7), 1341–1390 (1996)
Tresp, V.: Committee Machines. In: Hu, Y.H., Hwang, J.-N. (eds.) Handbook for Neural Network Signal Processing. CRC Press, Boca Raton (2001)
Chapman, P., Clinton, J., Kerber, R., Khabaza, T., Reinartz, T., Shearer, C., Wirth, R.: CRISP-DM 1.0: Step-By-Step Data Mining Guide (2000), http://www.crisp-dm.org
Bouckaert, R.R., Frank, E., Hall, M., Kirkby, R., Reutemann, P., Seewald, A., Scuse, D.: Weka Manual for Version 3-6-2. University of Waikato, Hamilton (2010)
Witten, I.H., Frank, E.: Data mining: practical machine learning tools and techniques. Elsevier, San Francisco (2005)
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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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