Discovering the anatomy of school dropouts with data science: case study, Technical Professional Education in Mexico

Abstract

School dropouts are a major educational problem that influences the economic development of a country, social well-being, and individual growth. This article uses data science techniques to identify the determinants of dropout in career technical education in Mexico. To do this, we consider students' academic achievements and information related to socioeconomic and psychological aspects. The results indicate the importance of exploring factors beyond academic performance to understand the causes of school dropout in CONALEP.