Intelligent System to Predict University Students Dropout

Abstract

The objective of this research is to reduce the dropout rate of students in the Faculty of Systems Engineering and Informatics of the Universidad Nacional Mayor de San Marcos (FISI-UNMSM), through the implementation of an intelligent system with a data mining approach and the autonomous learning algorithm (decision trees) that predicts which students are at risk of dropping out. It was developed in Python and the free software Weka. For this, the data of the students who entered the faculty from 2004 to 2014 have been considered. This solution increases the availability and the level of satisfaction of the faculty; in the learning process, an accuracy percentage of 90.34% and precision of 95.91% was obtained, so the data mining model is considered valid. In addition, it was found that the variables that most influenced students in making the decision to abandon their studies are the historical weighted average their grades, the weighted average their grades of the last cycle, and the number of credits of their approved courses