Using Decision Trees to Predict Critical Reading Performance.

In Colombia, all undergraduate students, regardless of the professional training program they take, must complete the general competencies sections of the Saber Pro exam that include Critical Reading, Quantitative Reasoning, Citizen Competencies, Written Communication, and English. This paper presen...

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Autores:
Tipo de recurso:
Fecha de publicación:
2021
Institución:
Universidad Pedagógica y Tecnológica de Colombia
Repositorio:
RiUPTC: Repositorio Institucional UPTC
Idioma:
eng
OAI Identifier:
oai:repositorio.uptc.edu.co:001/14322
Acceso en línea:
https://revistas.uptc.edu.co/index.php/ingenieria/article/view/13792
https://repositorio.uptc.edu.co/handle/001/14322
Palabra clave:
academic performance
critical reading
decision trees
J48 algorithm
Saber Pro
algoritmo J48
árboles de decisión
desempeño académico
lectura crítica
Saber Pro
Rights
License
Copyright (c) 2021 Andrea Timaran-Buchely, Silvio-Ricardo Timarán-Pereira, Arsenio Hidalgo-Troya
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network_acronym_str REPOUPTC2
network_name_str RiUPTC: Repositorio Institucional UPTC
repository_id_str
dc.title.en-US.fl_str_mv Using Decision Trees to Predict Critical Reading Performance.
dc.title.es-ES.fl_str_mv Aplicación de árboles de decisión para predecir el desempeño en lectura crítica
title Using Decision Trees to Predict Critical Reading Performance.
spellingShingle Using Decision Trees to Predict Critical Reading Performance.
academic performance
critical reading
decision trees
J48 algorithm
Saber Pro
algoritmo J48
árboles de decisión
desempeño académico
lectura crítica
Saber Pro
title_short Using Decision Trees to Predict Critical Reading Performance.
title_full Using Decision Trees to Predict Critical Reading Performance.
title_fullStr Using Decision Trees to Predict Critical Reading Performance.
title_full_unstemmed Using Decision Trees to Predict Critical Reading Performance.
title_sort Using Decision Trees to Predict Critical Reading Performance.
dc.subject.en-US.fl_str_mv academic performance
critical reading
decision trees
J48 algorithm
Saber Pro
topic academic performance
critical reading
decision trees
J48 algorithm
Saber Pro
algoritmo J48
árboles de decisión
desempeño académico
lectura crítica
Saber Pro
dc.subject.es-ES.fl_str_mv algoritmo J48
árboles de decisión
desempeño académico
lectura crítica
Saber Pro
description In Colombia, all undergraduate students, regardless of the professional training program they take, must complete the general competencies sections of the Saber Pro exam that include Critical Reading, Quantitative Reasoning, Citizen Competencies, Written Communication, and English. This paper presents the application of the classification technique based on decision trees in the prediction of the performance in the Critical Reading section presented by the students of the Pontificia Universidad Javeriana Cali in the years 2017 and 2018. The CRISP methodology was used. From the socioeconomic, academic and institutional data stored in the ICFES databases, a data repository was built, cleaned and transformed. A mineable view composed of 2052 records and 17 attributes was obtained. The J48 algorithm of the Weka tool was used to build the decision tree. The score obtained in the Critical Reading section of the Saber Pro exam was taken as a class. According to the results obtained, the Philosophy, Applied Mathematics, and Medicine programs stood out for having the best performance in this test. Among the predictive variables associated with performance in the Critical Reading skill are the faculty, the age group and the student's transportation index, as three important variables related to the good or low academic performance of the students of the Universidad Javeriana Cali. The knowledge generated in this research is constituted in quality information to support the decision-making process of the university directives in order to improve the quality of the higher education offered in this institution.
publishDate 2021
dc.date.accessioned.none.fl_str_mv 2024-07-05T19:11:59Z
dc.date.available.none.fl_str_mv 2024-07-05T19:11:59Z
dc.date.none.fl_str_mv 2021-12-01
dc.type.none.fl_str_mv info:eu-repo/semantics/article
dc.type.coar.fl_str_mv http://purl.org/coar/resource_type/c_2df8fbb1
dc.type.coarversion.fl_str_mv http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.version.spa.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.coarversion.spa.fl_str_mv http://purl.org/coar/version/c_970fb48d4fbd8a451
status_str publishedVersion
dc.identifier.none.fl_str_mv https://revistas.uptc.edu.co/index.php/ingenieria/article/view/13792
10.19053/01211129.v30.n58.2021.13792
dc.identifier.uri.none.fl_str_mv https://repositorio.uptc.edu.co/handle/001/14322
url https://revistas.uptc.edu.co/index.php/ingenieria/article/view/13792
https://repositorio.uptc.edu.co/handle/001/14322
identifier_str_mv 10.19053/01211129.v30.n58.2021.13792
dc.language.none.fl_str_mv eng
dc.language.iso.spa.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv https://revistas.uptc.edu.co/index.php/ingenieria/article/view/13792/11203
https://revistas.uptc.edu.co/index.php/ingenieria/article/view/13792/11301
dc.rights.en-US.fl_str_mv Copyright (c) 2021 Andrea Timaran-Buchely, Silvio-Ricardo Timarán-Pereira, Arsenio Hidalgo-Troya
http://creativecommons.org/licenses/by/4.0
dc.rights.coar.fl_str_mv http://purl.org/coar/access_right/c_abf2
dc.rights.coar.spa.fl_str_mv http://purl.org/coar/access_right/c_abf368
rights_invalid_str_mv Copyright (c) 2021 Andrea Timaran-Buchely, Silvio-Ricardo Timarán-Pereira, Arsenio Hidalgo-Troya
http://creativecommons.org/licenses/by/4.0
http://purl.org/coar/access_right/c_abf368
http://purl.org/coar/access_right/c_abf2
dc.format.none.fl_str_mv application/pdf
text/xml
dc.publisher.en-US.fl_str_mv Universidad Pedagógica y Tecnológica de Colombia
dc.source.en-US.fl_str_mv Revista Facultad de Ingeniería; Vol. 30 No. 58 (2021): October-December 2021 (Continuous Publication); e13792
dc.source.es-ES.fl_str_mv Revista Facultad de Ingeniería; Vol. 30 Núm. 58 (2021): Octubre-Diciembre 2021 (Publicación Continua) ; e13792
dc.source.none.fl_str_mv 2357-5328
0121-1129
institution Universidad Pedagógica y Tecnológica de Colombia
repository.name.fl_str_mv Repositorio Institucional UPTC
repository.mail.fl_str_mv repositorio.uptc@uptc.edu.co
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spelling 2021-12-012024-07-05T19:11:59Z2024-07-05T19:11:59Zhttps://revistas.uptc.edu.co/index.php/ingenieria/article/view/1379210.19053/01211129.v30.n58.2021.13792https://repositorio.uptc.edu.co/handle/001/14322In Colombia, all undergraduate students, regardless of the professional training program they take, must complete the general competencies sections of the Saber Pro exam that include Critical Reading, Quantitative Reasoning, Citizen Competencies, Written Communication, and English. This paper presents the application of the classification technique based on decision trees in the prediction of the performance in the Critical Reading section presented by the students of the Pontificia Universidad Javeriana Cali in the years 2017 and 2018. The CRISP methodology was used. From the socioeconomic, academic and institutional data stored in the ICFES databases, a data repository was built, cleaned and transformed. A mineable view composed of 2052 records and 17 attributes was obtained. The J48 algorithm of the Weka tool was used to build the decision tree. The score obtained in the Critical Reading section of the Saber Pro exam was taken as a class. According to the results obtained, the Philosophy, Applied Mathematics, and Medicine programs stood out for having the best performance in this test. Among the predictive variables associated with performance in the Critical Reading skill are the faculty, the age group and the student's transportation index, as three important variables related to the good or low academic performance of the students of the Universidad Javeriana Cali. The knowledge generated in this research is constituted in quality information to support the decision-making process of the university directives in order to improve the quality of the higher education offered in this institution.En Colombia, todos los estudiantes de pregrado, sin importar el programa de formación profesional que cursen, deben presentar las pruebas de competencias genéricas del examen Saber Pro que incluyen: Lectura Crítica, Razonamiento Cuantitativo, Competencias Ciudadanas, Comunicación Escrita e inglés. En este artículo se presenta la aplicación de la técnica de clasificación basada en árboles de decisión para predecir el desempeño en la prueba de Lectura Crítica del examen Saber Pro que presentaron los estudiantes de la Pontificia Universidad Javeriana Cali en los años 2017 y 2018. Se utilizó la metodología CRISP-DM. A partir de los datos socioeconómicos, académicos e institucionales almacenados en las bases de datos del ICFES, se construyó, limpio y transformó un repositorio de datos. Se obtuvo una vista minable compuesta por 2052 registros y 17 atributos. Se utilizó el algoritmo J48 de la herramienta Weka para construir el árbol de decisión. De acuerdo con los resultados obtenidos, se destacaron los programas de Filosofía, Matemáticas Aplicadas y Medicina por tener el mejor desempeño en esta prueba. Entre las variables predictoras asociadas al desempeño en la competencia de Lectura Crítica, están la facultad, el grupo etario y el índice de transporte del estudiante, como tres variables importantes relacionadas al buen o bajo desempeño académico de los estudiantes de la Universidad Javeriana Cali. El conocimiento generado en esta investigación, se constituye en información de calidad para soportar la toma de decisiones de las directivas universitarias en vía del mejoramiento de la calidad de la educación superior que se brinda en esta institución.application/pdftext/xmlengengUniversidad Pedagógica y Tecnológica de Colombiahttps://revistas.uptc.edu.co/index.php/ingenieria/article/view/13792/11203https://revistas.uptc.edu.co/index.php/ingenieria/article/view/13792/11301Copyright (c) 2021 Andrea Timaran-Buchely, Silvio-Ricardo Timarán-Pereira, Arsenio Hidalgo-Troyahttp://creativecommons.org/licenses/by/4.0http://purl.org/coar/access_right/c_abf368http://purl.org/coar/access_right/c_abf2Revista Facultad de Ingeniería; Vol. 30 No. 58 (2021): October-December 2021 (Continuous Publication); e13792Revista Facultad de Ingeniería; Vol. 30 Núm. 58 (2021): Octubre-Diciembre 2021 (Publicación Continua) ; e137922357-53280121-1129academic performancecritical readingdecision treesJ48 algorithmSaber Proalgoritmo J48árboles de decisióndesempeño académicolectura críticaSaber ProUsing Decision Trees to Predict Critical Reading Performance.Aplicación de árboles de decisión para predecir el desempeño en lectura críticainfo:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_2df8fbb1info:eu-repo/semantics/publishedVersionhttp://purl.org/coar/version/c_970fb48d4fbd8a451http://purl.org/coar/version/c_970fb48d4fbd8a85Timaran-Buchely, AndreaTimarán-Pereira, Silvio-RicardoHidalgo-Troya, Arsenio001/14322oai:repositorio.uptc.edu.co:001/143222025-07-18 11:53:51.351metadata.onlyhttps://repositorio.uptc.edu.coRepositorio Institucional UPTCrepositorio.uptc@uptc.edu.co