Application of Data Mining for the Detection of Variables that Cause University Desertion
Xavier Palacios-Pacheco, William Villegas-Ch, Sergio Luján-Mora
Proceedings of the 4th International Conference on Technology Trends (CITT 2018), p. 510-520, Babahoyo (Ecuador), August 29-31 2018. ISBN: 978-3-030-05532-5. https://doi.org/10.1007/978-3-030-05532-5_38
(CITT'18) Congreso internacional / International conference
College desertion is one of the problems currently addressed by most higher education institutions throughout Latin America. From different investigations, it is known that a large percentage of students do not complete their studies, with the consequent social cost associated with this phenomenon. Some countries have begun to design deep improvement processes to increase retention in the first years of university studies. The process considered for the improvement of the desertion is through the data mining, the use of its algorithms allows discovering patterns in the students that help to explain this effect. The algorithms also identify the independent variables that influence the desertion and analyze them according to a level of depth previously established by the interested parties. The purpose of this study is to determine a model that explains the desertion of undergraduate students at the university and design actions that tend towards the decrease of the desertion.