Predicting evasion candidates in higher education institutions

Authors:
Remis Balaniuk;Hercules Antonio do Prado;Renato da Veiga Guadagnin;Edilson Ferneda;Paulo Roberto Cobbe
Affiliations:
Graduate Program on Knowledge and IT Management, Catholic University of Brasilia, Brasília, DF, Brazil;Graduate Program on Knowledge and IT Management, Catholic University of Brasilia, Brasília and Embrapa - Management and Strategy Secretariat, Parque Estação Biológica, Bras ...;Graduate Program on Knowledge and IT Management, Catholic University of Brasilia, Brasília, DF, Brazil;Graduate Program on Knowledge and IT Management, Catholic University of Brasilia, Brasília, DF, Brazil;Graduate Program on Knowledge and IT Management, Catholic University of Brasilia, Brasília and Information Technology Department, UniCEUB College, Brasília, DF, Brazil
Venue:
MEDI'11 Proceedings of the First international conference on Model and data engineering
Year:
2011

Citing 3
Cited 0

Principles of data mining

Principles of data mining
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)

Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
The lack of a priori distinctions between learning algorithms

Neural Computation

Quantified Score

Hi-index	0.02

Visualization

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.