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...

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Main Authors:	Timarán-Pereira, Ricardo, Caicedo-Zambrano, Javier, Timarán-Buchely, Andrea
Format:	Online
Language:	eng
Published:	Universidad Pedagógica y Tecnológica de Colombia 2022
Subjects:	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
Online Access:	https://revistas.uptc.edu.co/index.php/ingenieria/article/view/14814
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author	Timarán-Pereira, Ricardo Caicedo-Zambrano, Javier Timarán-Buchely, Andrea
author_facet	Timarán-Pereira, Ricardo Caicedo-Zambrano, Javier Timarán-Buchely, Andrea
author_sort	Timarán-Pereira, Ricardo
collection	OJS
description	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.
format	Online
id	oai:oai.revistas.uptc.edu.co:article-14814
institution	Revista Facultad de Ingeniería
language	eng
publishDate	2022
publisher	Universidad Pedagógica y Tecnológica de Colombia
record_format	ojs
spelling	oai:oai.revistas.uptc.edu.co:article-148142023-05-31T16:24:07Z Applying Predictive Data Mining to Discover Factors Associated to the Language Skill Performance from Elementary School Students Minería predictiva aplicada al descubrimiento de factores asociados al desempeño en la competencia de lenguaje de los estudiantes de básica primaria Timarán-Pereira, Ricardo Caicedo-Zambrano, Javier Timarán-Buchely, Andrea 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 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. En este artículo se aplican técnicas predictivas de minería de datos para descubrir patrones de desempeño académico en la competencia de Lenguaje de las pruebas Saber 5° que presentaron los estudiantes de las instituciones educativas colombianas de básica primaria en el año 2017. Para tal fin, se utilizó la metodología CRISP-DM y se tuvo en cuenta la información socioeconómica, académica e institucional de las bases de datos del ICFES. Se obtuvo un conjunto de datos minable utilizando técnicas de limpieza y transformación de datos y se construyó un árbol de decisión con el algoritmo J48 de la herramienta Weka. Entre los factores predictores de los patrones descubiertos están la naturaleza y la ubicación del colegio, si los estudiantes reprobaron o no algún grado, el grupo etario, la educación de la madre y los índices de TICs y electrodomésticos en los hogares. El conocimiento producido en esta investigación es información de calidad para la toma de decisiones en el MEN y las secretarías de educación y para que las directivas de las instituciones educativas de básica primaria definan planes de mejoramiento que redunden en la calidad de la educación en Colombia. Universidad Pedagógica y Tecnológica de Colombia 2022-12-31 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion application/pdf text/xml https://revistas.uptc.edu.co/index.php/ingenieria/article/view/14814 10.19053/01211129.v31.n62.2022.14814 Revista Facultad de Ingeniería; Vol. 31 No. 62 (2022): October-December 2022 (Continuous Publication); e14814 Revista Facultad de Ingeniería; Vol. 31 Núm. 62 (2022): Octubre-Diciembre 2022 (Publicación Continua) ; e14814 2357-5328 0121-1129 eng https://revistas.uptc.edu.co/index.php/ingenieria/article/view/14814/12535 https://revistas.uptc.edu.co/index.php/ingenieria/article/view/14814/12575 Copyright (c) 2022 Ricardo Timarán-Pereira, Javier Caicedo-Zambrano, Andrea Timarán-Buchely http://creativecommons.org/licenses/by/4.0
spellingShingle	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 Timarán-Pereira, Ricardo Caicedo-Zambrano, Javier Timarán-Buchely, Andrea Applying Predictive Data Mining to Discover Factors Associated to the Language Skill Performance from Elementary School Students
title	Applying Predictive Data Mining to Discover Factors Associated to the Language Skill Performance from Elementary School Students
title_alt	Minería predictiva aplicada al descubrimiento de factores asociados al desempeño en la competencia de lenguaje de los estudiantes de básica primaria
title_full	Applying Predictive Data Mining to Discover Factors Associated to the Language Skill Performance from Elementary School Students
title_fullStr	Applying Predictive Data Mining to Discover Factors Associated to the Language Skill Performance from Elementary School Students
title_full_unstemmed	Applying Predictive Data Mining to Discover Factors Associated to the Language Skill Performance from Elementary School Students
title_short	Applying Predictive Data Mining to Discover Factors Associated to the Language Skill Performance from Elementary School Students
title_sort	applying predictive data mining to discover factors associated to the language skill performance from elementary school students
topic	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
topic_facet	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
url	https://revistas.uptc.edu.co/index.php/ingenieria/article/view/14814
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