Decision trees for predicting factors associated with academic performance of high school students in Saber 11 tests

This article is obtained by applying the classification model based on decision trees in order to detect factors associated with the academic performance of Colombian eleven grade students, who presented the Saber 11 tests in 2015 and 2016. The research was of a descriptive type under the quantitative approach, applying a non-experimental design. Following the CRISP-DM methodology, the socio-economic, academic and institutional information of these students was selected from the ICFES databases. A data repository was built, cleaned and transformed and, using the WEKA data mining tool, decision trees were generated that allowed the identification of patterns associated with the good or poor academic performance of the students in the Saber 11 tests. The patterns discovered will help in the decision-making processes of the Ministerio de Educación Nacional, together with institutions that ensure the quality of education in Colombia.

• Tipo de recurso: http://purl.org/coar/resource_type/c_6772
• Fecha de publicación: 2019
• Institución: Universidad Pedagógica y Tecnológica de Colombia
• Repositorio: RiUPTC: Repositorio Institucional UPTC
• Idioma: spa
• OAI Identifier: oai:repositorio.uptc.edu.co:001/10301
• Acceso en línea: https://revistas.uptc.edu.co/index.php/investigacion_duitama/article/view/9184; https://repositorio.uptc.edu.co/handle/001/10301
• Palabra clave: data mining; associated patterns; academic performance; Saber 11 tests; decision trees; minería de datos; patrones asociados; desempeño académico; pruebas Saber 11°; árboles de decisión
• License: Derechos de autor 2019 REVISTA DE INVESTIGACIÓN, DESARROLLO E INNOVACIÓN