Retraction: using Big Data to determine potential dropouts in higher education
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...
| Autores: | , , , , |
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| Formato: | artículo |
| Estado: | Versión aceptada para publicación |
| Fecha de publicación: | 2020 |
| País: | Colombia |
| Recursos: | Corporación Universidad de la Costa |
| Repositorio: | Repositorio REDICUC |
| Idioma: | inglés |
| OAI Identifier: | oai:repositorio.cuc.edu.co:11323/7616 |
| Acesso em linha: | https://hdl.handle.net/11323/7616 https://repositorio.cuc.edu.co/ |
| Access Level: | acceso abierto |
| Palavra-chave: | Big Data Higher education Dropouts |
| Resumo: | 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. |
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