Mateo

School dropout is one of the most intriguing and crucial problems in Education. This problem permeates the various levels and modalities of education and generates social economic political academic and financial losses for all those involved in the educational process. Therefore the development of efficient methods to predict the risk of student dropout is essential. The approach was validated using data from former students of the higher education course of Computer Science classification models were used one of the techniques of Artificial Intelligence which enables continuous learning. In the approach validation five machine learning models were employed and two models obtained better accuracy indices (SVM and Adaboost).