Applying Predictive Data Mining to Discover Factors Associated to the Language Skill Performance from Elementary School Students

In this paper, predictive data mining techniques are applied to determine the academic performance from fifth grade students in the Saber 5° tests Language skill at Colombian elementary schools in 2017. We employed the CRISP-DM methodology.  Socioeconomic, academic, and institutional information was available at the ICFES databases. A minable dataset was obtained using data cleaning and transformation techniques. A decision tree was built with the Weka tool J48 algorithm. Some of the predictors of the discovered patterns are the nature and location of the school, whether or not students failed a school year, the age group, the mother's educational attainment, and the rates of ICTs and household appliances. The findings of this research serve as quality information for the decision-making at the Ministry of National Education (MEN) and the secretaries of education, and for the directors of elementary educational institutions to define improvement plans that result in the quality of elementary school education in Colombia.

• Fecha de publicación: 2022
• Institución: Universidad Pedagógica y Tecnológica de Colombia
• Repositorio: RiUPTC: Repositorio Institucional UPTC
• Idioma: eng
• OAI Identifier: oai:repositorio.uptc.edu.co:001/14350
• Acceso en línea: https://revistas.uptc.edu.co/index.php/ingenieria/article/view/14814; https://repositorio.uptc.edu.co/handle/001/14350
• Palabra clave: Data Mining; Classification; Decision Trees; Predictive Model; Performance Patterns; Saber 5 Tests; Minería de Datos; Clasificación; Árboles de Decisión; Modelo Predictivo; Patrones de Desempeño; Pruebas Saber 5
• License: Copyright (c) 2022 Ricardo Timarán-Pereira, Javier Caicedo-Zambrano, Andrea Timarán-Buchely