Early Prediction of College Attrition Using Data Mining

Abstract

College attrition is a chronic problem for institutions of higher education. In Brazilian public universities, attrition also accounts for the significant waste of public resources desperately needed in other sectors of society. Thus, given the severity and persistence of this problem, several studies have been conducted in an attempt to mitigate undergraduate dropout rates. Using H2O software as a data mining tool, our study employed parameter tuning to train 321 of three classification algorithms, and with Deep Learning, it was possible to predict 71.1% of the cases of dropout given these characteristics. With this result, it will be possible to identify the attrition profiles of students and implement corrective measures on initiating their studies.