Machine learning application in university management: Classification model Dropping out of engineering students in Peru (#1332)

Authors

Tocto Inga, Paul Miller

Huamaní Huamaní, Gloria Teresita

Zuloaga Rotta, Luis Alberto

Abstract

We apply artificial intelligence to various cases of university management, with a proactive approach. In this study, we apply machine learning to classify whether a student will drop out or not, considering certain variables from the SITUATION DATA, based on the relevant attributes of the students. The results of this study would help to decision makers to inquire about the high cost of not finishing the degree and adopt retention strategies. There are various studies on the causes of dropout, such as economic, work-related, family, personal, and perceptions of the educational offering related to the quality they provide. At the same time, students can perceive the benefits of education, especially in engineering careers have expectations, and value. The model developed is a neural network with 8 internal layers, in addition to the input and output layers, 225 training iterations have been considered, obtaining as a result an accuracy of 67.10%.