Estudo exploratório do uso de classificadores para a predição de desempenho e abandono em universidade
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2016-10-20
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Universidade Federal de Goiás
Resumo
Educational Data Mining, by the triad of quality improvement, cost reduction and educational
effectiveness, acts and seeks to better understand the teaching and learning process.
In this context, the aim of this work is an exploratory study of classification methods to
predict student performance and dropout from data in university academic databases. In
this study we used demographic, socio-economic and academic results, obtained from the
Vestibular and the university database to analyze several classification techniques, as well
as balancing and attribute selection techniques, identified through a systematic review of
the literature. Following a trend found in the selected articles, we chose to use decision
trees as the primary classification algorithm, although comparative studies showed better
results with logistic regression techniques and Bayesian networks. This is because decision
trees allow an analysis of the attributes used in the generated models while maintaining
acceptable levels of accuracy, while other techniques work as a black box. Through
the tests we found that you get better results using balanced sets. In this sense, the Resample
technique that selects a balanced subset of the data showed better results than SMOTE
technique that generates synthetic data for balancing the dataset. Regarding the use of
attribute selection techniques, these did not bring significant advantages. Among the attributes
used, grades and economic factors often appear as nodes in the generated models.
An attempt to predict performance for each subject based on data from previous courses
was less successful, maybe because of the use of ternary predictive classes. Nevertheless,
the analysis carried out showed that the use of classifiers is a promising way to predict
performance and dropout, but further studies are still needed.
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MOTTA, P. R. A. Estudo exploratório do uso de classificadores para a predição de desempenho e abandono em universidade. 2016. 156 f. Dissertação (Mestrado em Ciência da Computação) - Universidade Federal de Goiás, Goiânia, 2016.