Predictive models assessment based on CRISP-DM methodology for students performance in Colombia - Saber 11 Test
The purpose of this paper is to evaluate several machine learning models under the CRISP-DM methodology in order to determine, through its metrics, the best model for predicting the performance of high school students in the Colombian Caribbean region in the Saber 11º test, while proposing a new met...
- Autores:
-
Acosta-Solano, Jairo
Lancheros Cuesta, Diana Janeth
Umaña Ibáñez, Samir F.
Coronado-Hernandez, Jairo R.
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2022
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/9221
- Acceso en línea:
- https://hdl.handle.net/11323/9221
https://doi.org/10.1016/j.procs.2021.12.278
https://repositorio.cuc.edu.co/
- Palabra clave:
- CRISP-DM methodology
Education
Learning models
National education system
Predictive models
- Rights
- openAccess
- License
- © 2021 The Authors. Published by Elsevier B.V
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Predictive models assessment based on CRISP-DM methodology for students performance in Colombia - Saber 11 Test |
| title |
Predictive models assessment based on CRISP-DM methodology for students performance in Colombia - Saber 11 Test |
| spellingShingle |
Predictive models assessment based on CRISP-DM methodology for students performance in Colombia - Saber 11 Test CRISP-DM methodology Education Learning models National education system Predictive models |
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Predictive models assessment based on CRISP-DM methodology for students performance in Colombia - Saber 11 Test |
| title_full |
Predictive models assessment based on CRISP-DM methodology for students performance in Colombia - Saber 11 Test |
| title_fullStr |
Predictive models assessment based on CRISP-DM methodology for students performance in Colombia - Saber 11 Test |
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Predictive models assessment based on CRISP-DM methodology for students performance in Colombia - Saber 11 Test |
| title_sort |
Predictive models assessment based on CRISP-DM methodology for students performance in Colombia - Saber 11 Test |
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Acosta-Solano, Jairo Lancheros Cuesta, Diana Janeth Umaña Ibáñez, Samir F. Coronado-Hernandez, Jairo R. |
| dc.contributor.author.spa.fl_str_mv |
Acosta-Solano, Jairo Lancheros Cuesta, Diana Janeth Umaña Ibáñez, Samir F. Coronado-Hernandez, Jairo R. |
| dc.subject.proposal.eng.fl_str_mv |
CRISP-DM methodology Education Learning models National education system Predictive models |
| topic |
CRISP-DM methodology Education Learning models National education system Predictive models |
| description |
The purpose of this paper is to evaluate several machine learning models under the CRISP-DM methodology in order to determine, through its metrics, the best model for predicting the performance of high school students in the Colombian Caribbean region in the Saber 11º test, while proposing a new methodology for evaluating the results of the test by regions in order to take into account the socioeconomic particularities of each one of them. The CRISP-DM methodology is taken as a basis due to its maturity, this methodology allows the extraction of business and data knowledge, offers a guide for data preparation, modeling and validation of the models; it is expected that the proposed methodology will be implemented by the Colombian Institute for the Promotion of Higher Education (ICFES), departmental education secretariats and educational institutions. A variety of techniques and tools were used to develop ETL processes to obtain a data set with the most relevant attributes, in order to evaluate four machine learning models developed with the J48 (C4.5), LMT, PART and Multilayer Perceptron algorithms; obtaining that the best data set and the best learning model is obtained using the InfoGain attribute selection method and the LMT decision tree algorithm, respectively. Therefore, this project will facilitate the actors of the National Education System to make decisions for the benefit of students and the quality of education in the country, especially in the Caribbean region. |
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2022 |
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2022 |
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https://hdl.handle.net/11323/9221 |
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eng |
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Procedia Computer Science |
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[1] Isis Gómez López, Desarrollo sostenible. Elearning, 2020. [2] ICFES, “ICFES. (2019b). Guía de orientación Saber 11.o 2020-1.” . [3] R. Ricardo Timarán-Pereira, J. Caicedo-Zambrano, and A. Hidalgo-Troya, “Árboles de decisión para predecir factores asociados al desempeño académico de estudiantes de bachillerato en las pruebas Saber 11°,” Rev. Investig. Desarro. E Innovación, vol. 9, no. 2, 2019. [4] W. Y. Ayele, “Adapting CRISP-DM for idea mining a data mining process for generating ideas using a textual dataset,” Int. J. Adv. Comput. Sci. Appl., vol. 11, no. 6, pp. 20–32, 2020. [5] R. Wirth, “CRISP-DM : Towards a Standard Process Model for Data Mining,” Proc. Fourth Int. Conf. Pract. Appl. Knowl. Discov. Data Min., no. 24959, pp. 29–39, 2000. [6] F. Martinez-Plumed et al., “CRISP-DM Twenty Years Later: From Data Mining Processes to Data Science Trajectories,” IEEE Trans. Knowl. Data Eng., pp. 1–1, 2019. [7] R. C. Q. Jordi Gironés Roig, Jordi Casas Roma, Julià Minguillón Alfonso, Minería de datos. Editorial UOC, 2017. [8] ICFES, “Icfes Instituto Colombiano para la Evaluación de la Educación - Portal Icfes.” [9] S. U. Ibáñez and Jairo R. Coronado-Hernández, “Código desarrollado en R para el proceso ETL.” [10] C. L. Corso, “Aplicación de algoritmos de clasificación supervisada usando Weka,” Univ. Tecnológica Nac. Fac. Reg. Córdoba, p. 11, 2009. [11] J. R. (2021): Umaña, Samir; Coronado-Hernández, “Métricas de evaluación de los modelos. figshare. Dataset.” [12] J. R. Umaña, Samir; Coronado-Hernández, “Estructura del Logistic Model Tree. figshare. Dataset.” [13] J. R. (2021): Umaña, Samir; Coronado-Hernández, “Código en R y gráfica del árbol generado por el algoritmo rpart. figshare. Dataset.” |
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Acosta-Solano, JairoLancheros Cuesta, Diana JanethUmaña Ibáñez, Samir F.Coronado-Hernandez, Jairo R.2022-06-08T13:28:14Z2022-06-08T13:28:14Z20221877-0509https://hdl.handle.net/11323/9221https://doi.org/10.1016/j.procs.2021.12.27810.1016/j.procs.2021.12.278Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/The purpose of this paper is to evaluate several machine learning models under the CRISP-DM methodology in order to determine, through its metrics, the best model for predicting the performance of high school students in the Colombian Caribbean region in the Saber 11º test, while proposing a new methodology for evaluating the results of the test by regions in order to take into account the socioeconomic particularities of each one of them. The CRISP-DM methodology is taken as a basis due to its maturity, this methodology allows the extraction of business and data knowledge, offers a guide for data preparation, modeling and validation of the models; it is expected that the proposed methodology will be implemented by the Colombian Institute for the Promotion of Higher Education (ICFES), departmental education secretariats and educational institutions. A variety of techniques and tools were used to develop ETL processes to obtain a data set with the most relevant attributes, in order to evaluate four machine learning models developed with the J48 (C4.5), LMT, PART and Multilayer Perceptron algorithms; obtaining that the best data set and the best learning model is obtained using the InfoGain attribute selection method and the LMT decision tree algorithm, respectively. Therefore, this project will facilitate the actors of the National Education System to make decisions for the benefit of students and the quality of education in the country, especially in the Caribbean region.6 páginasapplication/pdfengElsevier BVNetherlands© 2021 The Authors. Published by Elsevier B.VAtribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)https://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Predictive models assessment based on CRISP-DM methodology for students performance in Colombia - Saber 11 TestArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARThttp://purl.org/coar/version/c_970fb48d4fbd8a85https://www.sciencedirect.com/science/article/pii/S1877050921025175ColombiaProcedia Computer Science[1] Isis Gómez López, Desarrollo sostenible. Elearning, 2020.[2] ICFES, “ICFES. (2019b). Guía de orientación Saber 11.o 2020-1.” .[3] R. Ricardo Timarán-Pereira, J. Caicedo-Zambrano, and A. Hidalgo-Troya, “Árboles de decisión para predecir factores asociados al desempeño académico de estudiantes de bachillerato en las pruebas Saber 11°,” Rev. Investig. Desarro. E Innovación, vol. 9, no. 2, 2019.[4] W. Y. Ayele, “Adapting CRISP-DM for idea mining a data mining process for generating ideas using a textual dataset,” Int. J. Adv. Comput. Sci. Appl., vol. 11, no. 6, pp. 20–32, 2020.[5] R. Wirth, “CRISP-DM : Towards a Standard Process Model for Data Mining,” Proc. Fourth Int. Conf. Pract. Appl. Knowl. Discov. Data Min., no. 24959, pp. 29–39, 2000.[6] F. Martinez-Plumed et al., “CRISP-DM Twenty Years Later: From Data Mining Processes to Data Science Trajectories,” IEEE Trans. Knowl. Data Eng., pp. 1–1, 2019.[7] R. C. Q. Jordi Gironés Roig, Jordi Casas Roma, Julià Minguillón Alfonso, Minería de datos. Editorial UOC, 2017.[8] ICFES, “Icfes Instituto Colombiano para la Evaluación de la Educación - Portal Icfes.”[9] S. U. Ibáñez and Jairo R. Coronado-Hernández, “Código desarrollado en R para el proceso ETL.”[10] C. L. Corso, “Aplicación de algoritmos de clasificación supervisada usando Weka,” Univ. Tecnológica Nac. Fac. Reg. Córdoba, p. 11, 2009.[11] J. R. (2021): Umaña, Samir; Coronado-Hernández, “Métricas de evaluación de los modelos. figshare. Dataset.”[12] J. R. Umaña, Samir; Coronado-Hernández, “Estructura del Logistic Model Tree. figshare. Dataset.”[13] J. R. (2021): Umaña, Samir; Coronado-Hernández, “Código en R y gráfica del árbol generado por el algoritmo rpart. figshare. Dataset.”517512198CRISP-DM methodologyEducationLearning modelsNational education systemPredictive modelsPublicationORIGINALPredictive models assessment based on CRISP-DM methodology.pdfPredictive models assessment based on CRISP-DM methodology.pdfapplication/pdf654350https://repositorio.cuc.edu.co/bitstreams/a9454371-e22e-43bb-a060-338f5546e37a/downloadb58d40d1ff88f69d4086e73aafdfc428MD51LICENSElicense.txtlicense.txttext/plain; charset=utf-83196https://repositorio.cuc.edu.co/bitstreams/1832ee46-0268-4d8b-8f83-bd0609f2b072/downloade30e9215131d99561d40d6b0abbe9badMD52TEXTPredictive models assessment based on CRISP-DM methodology.pdf.txtPredictive models assessment based on CRISP-DM methodology.pdf.txttext/plain37714https://repositorio.cuc.edu.co/bitstreams/e4f52938-75b9-4701-9900-c39f67d2fa9f/download6a379a12c88fd1755e39f143833fa857MD53THUMBNAILPredictive models assessment based on CRISP-DM methodology.pdf.jpgPredictive models assessment based on CRISP-DM methodology.pdf.jpgimage/jpeg13306https://repositorio.cuc.edu.co/bitstreams/d3d92039-5e7f-42b5-88a4-8b4bfcc01d71/download850f0e8726eb685eece7b5a602513619MD5411323/9221oai:repositorio.cuc.edu.co:11323/92212024-09-17 14:24:20.622https://creativecommons.org/licenses/by-nc-nd/4.0/© 2021 The Authors. 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