Big data, a strategy to prevent academic dropout in HEIs

Abstract

The diversification of educational modalities such as e-learning and b-learning has made it possible to increase coverage rates in higher education. However, as more students enter universities, more of them fail to complete their undergraduate studies, thus detonating dropout rates, a problem that not only has high economic and social costs at the national and international level, but also generates conditions of exclusion and poverty. The following article analyzes the negative effects of academic dropout, as well as the characteristics of Big Data, which is a viable and pertinent technological solution to provide answers to this problem. On the other hand, the authors also present the main characteristics of the implementation of the Big Data Analytical Model of the Universidad Autónoma de Tamaulipas (UAT), as well as its results, which allowed to identify causes and factors that influence university students' desertion.