Artificial Intelligence in Industry 4.0 and 5G Technology

11Machine Learning and MCDM Approach to Characterize Student Attrition in Higher Education

Arrieta‐M Luisa F¹ and Lopez‐I Fernando²

¹Simon Bolivar University, Barranquilla, Atlántico, Colombia

²Autonomous University of Nuevo León, Department of Mechanical and Electric Engineering, San Nicolás de los Garza, Nuevo León, México

11.1 Introduction

The definition of student attrition, according to the Colombian Ministry of Education that attrition rates are defined by periods and by cohort. Attrition by periods takes place when a student abandons studies for two consecutive semesters or one year of academic activity [1]; cohort attrition refers to the difference between the number of students starting in a cohort and the number of students graduated from that cohort.

Impact of student attrition: Student attrition has adverse effects not only on educational institutions but also on the life trajectory of those who claudicate in their professional aspiration, in their family, in the educational system as a whole, and in society [2].

Student attrition in the region and the world: 47% of students drop out of in Colombian universities, 57.5% in Latin America, and between 7.4% and 6.1% in the United States [3]. Furthermore, European countries on average have a dropout rate that is even lower than in the United States, while in Africa, only 6% of the population of those that should enter higher education enroll in universities. In China, university attrition is approximately 10% [4].