Prediction analysis of student dropout in a Computer Science course using Educational Data Mining

Educational Management Systems store a large amount of data from interaction of not only students and professors but also of students and the educational environment. Analyze and find patterns manually from a huge amount of data is hard, so Educational Data Mining (EDM) is widely used. This work presents a model that can predict the student's risk of dropout using data from the first three semesters attended by Computer Science Undergraduate students (N=1516) from Federal University of Pelotas. This work uses the CRISP-DM methodology e data from Cobalto Management System. The results are shown for three algorithms and for the RandomForest algorithm a precision of 95.12% and a Recall of 91.41% is presented indicating that it is possible to use a prediction model using only the data from the first three semesters of the course.