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This paper uses educational data mining techniques to identify the variables that can help educational managers to detect students that present low performance or are in risk to dropout their undergraduate education. We investigated real world academic data of students of the largest Public Federal Brazilian University. We established three categories of students with different academic trajectory in order to investigate their performance and the dropout rates. This study shows that even analyzing three different classes of 14.000 students it was possible to have a global precision above 80% for several classification algorithms. The results of Naive Bayes model were used to support the quantitative analysis. In this work, we stress that even few differences between the three classes of students that can be perceived on the basis of qualitative information.

The Impact of High Dropout Rates in a Large Public Brazilian University - A Quantitative Approach Using Educational Data Mining