Predictive models assessment based on CRISP-DM methodology for students performance in Colombia - Saber 11 Test

dc.creatorAcosta-Solano, Jairo
dc.creatorLancheros Cuesta, Diana Janeth
dc.creatorUmaña Ibáñez, Samir F.
dc.creatorCoronado-Hernandez, Jairo R.
dc.date2022-06-08T13:28:14Z
dc.date2022-06-08T13:28:14Z
dc.date2022
dc.date.accessioned2023-10-03T20:09:18Z
dc.date.available2023-10-03T20:09:18Z
dc.identifier1877-0509
dc.identifierhttps://hdl.handle.net/11323/9221
dc.identifierhttps://doi.org/10.1016/j.procs.2021.12.278
dc.identifier10.1016/j.procs.2021.12.278
dc.identifierCorporación Universidad de la Costa
dc.identifierREDICUC - Repositorio CUC
dc.identifierhttps://repositorio.cuc.edu.co/
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/9174590
dc.descriptionThe 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.
dc.format6 páginas
dc.formatapplication/pdf
dc.formatapplication/pdf
dc.languageeng
dc.publisherElsevier BV
dc.publisherNetherlands
dc.relationProcedia Computer Science
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dc.relation[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.
dc.relation[7] R. C. Q. Jordi Gironés Roig, Jordi Casas Roma, Julià Minguillón Alfonso, Minería de datos. Editorial UOC, 2017.
dc.relation[8] ICFES, “Icfes Instituto Colombiano para la Evaluación de la Educación - Portal Icfes.”
dc.relation[9] S. U. Ibáñez and Jairo R. Coronado-Hernández, “Código desarrollado en R para el proceso ETL.”
dc.relation[10] C. L. Corso, “Aplicación de algoritmos de clasificación supervisada usando Weka,” Univ. Tecnológica Nac. Fac. Reg. Córdoba, p. 11, 2009.
dc.relation[11] J. R. (2021): Umaña, Samir; Coronado-Hernández, “Métricas de evaluación de los modelos. figshare. Dataset.”
dc.relation[12] J. R. Umaña, Samir; Coronado-Hernández, “Estructura del Logistic Model Tree. figshare. Dataset.”
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dc.relation517
dc.relation512
dc.relation198
dc.rights© 2021 The Authors. Published by Elsevier B.V
dc.rightsAtribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)
dc.rightshttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rightshttp://purl.org/coar/access_right/c_abf2
dc.sourcehttps://www.sciencedirect.com/science/article/pii/S1877050921025175
dc.subjectCRISP-DM methodology
dc.subjectEducation
dc.subjectLearning models
dc.subjectNational education system
dc.subjectPredictive models
dc.titlePredictive models assessment based on CRISP-DM methodology for students performance in Colombia - Saber 11 Test
dc.typeArtículo de revista
dc.typehttp://purl.org/coar/resource_type/c_6501
dc.typeText
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:eu-repo/semantics/publishedVersion
dc.typehttp://purl.org/redcol/resource_type/ART
dc.typeinfo:eu-repo/semantics/publishedVersion
dc.typehttp://purl.org/coar/version/c_ab4af688f83e57aa
dc.coverageColombia


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