Using Big Data to Determine Potential Dropouts in Higher Education

Year: 2020

Type: conference-paper

Abstract: In higher education, student dropout is a relevant problem, not just in Latin America but also in developed countries. Although there is no consensus to measure the education quality, one of the important indicators of university success is the time to graduation (TTG), which is directly related to student dropout [1]. Global estimates put this dropout rate at 42% [2]. In the United States, this rate is around 30% and represents a loss of 9 billion dollars in the education of these students [3]. However, desertion not only affects the quality of education and the economy of a country, but also has effects on the development of society, since society demands the contributions derived from the population with higher education such as: innovation, knowledge production and scientific discovery [4]. Using basic statistical learning techniques, this paper presents a simple way to predict possible dropouts based on their demographic and academic characteristics.

Source: Journal of Physics Conference Series

DOI: 10.1088/1742-6596/1432/1/012077

Authors: Amelec Viloria, Alexa Senior Naveda, Hugo Hernández Palma, William Niebles Núñez, Leonardo Niebles Núñez

Institutions: Universidad de la Costa, Universidad del Atlántico, Universidad de Sucre

Language: English

Topic: Online Learning and Analytics

Subfield: Computer Science Applications

Field: Computer Science

Domain: Physical Sciences

SDG: Quality education

Open Access status: diamond

OpenAlex ID: W3000409003

FWCI: 2.613

Cites: 14

Cited by: 14

Related to: 10