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dc.contributor.authorIbarra Vázquez, Gerardo
dc.contributor.authorRamírez Montoya, María Soledad
dc.contributor.authorBuenestado Fernández, Mariana
dc.contributor.authorOlague, Gustavo
dc.date.accessioned2024-04-04T22:42:45Z
dc.date.available2024-04-04T22:42:45Z
dc.date.issued2023-11
dc.identifier.citationIbarra-Vazquez, G., Ramírez-Montoya, M. S., Buenestado-Fernández, M., & Olague, G. (2023). Predicting open education competency level: A machine learning approach. Heliyon, 9(11). https://doi.org/10.1016/j.heliyon.2023.e2059es_MX
dc.identifier.doihttps://doi.org/10.1016/j.heliyon.2023.e20597
dc.identifier.urihttps://hdl.handle.net/11285/652380
dc.description.abstractThis article aims to study open education competency data through machine learning models to determine whether models can be built on decision rules using the features from the students' perceptions and classify them by the level of competency. Data was collected from a convenience sample of 326 students from 26 countries using the eOpen instrument. Based on a quantitative research approach, we analyzed the eOpen data using two machine learning models considering these findings: 1) derivation of decision rules from students' perceptions of knowledge, skills, and attitudes or values related to open education to predict their competence level using Decision Trees and Random Forests models, 2) analysis of the prediction errors in the machine learning models to find bias, and 3) description of decision trees from the machine learning models to understand the choices that both models made to predict the competency levels. The results confirmed our hypothesis that the students' perceptions of their knowledge, skills, and attitudes or values related to open education and its sub-competencies produced satisfactory data for building machine learning models to predict the participants' competency levels.es_MX
dc.format.mediumTextoes_MX
dc.language.isoenges_MX
dc.publisherScience Directes_MX
dc.relation.isFormatOfpublishedVersiones_MX
dc.relation.urlhttps://www.sciencedirect.com/science/article/pii/S2405844023078052es_MX
dc.rightsopenAccesses_MX
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0es_MX
dc.subjectHUMANIDADES Y CIENCIAS DE LA CONDUCTA::PEDAGOGÍA::TEORÍA Y MÉTODOS EDUCATIVOSes_MX
dc.subject.lcshEducationes_MX
dc.titlePredicting open education competency level: A machine learning approaches_MX
dc.typeArtículo/Articlees_MX
dc.identifier.journalHeliyones_MX
dc.identifier.orcidhttps://orcid.org/0000-0002-0782-5369es_MX
dc.identifier.orcidhttps://orcid.org/0000-0002-1274-706Xes_MX
dc.identifier.orcidhttps://orcid.org/0000-0002-3242-5332es_MX
dc.identifier.orcidhttps://orcid.org/0000-0001-5773-9517es_MX
dc.subject.keywordopen educationes_MX
dc.subject.keywordcompetency leveles_MX
dc.subject.keywordmachine learninges_MX
dc.subject.keywordeducational innovationes_MX
dc.subject.keywordhigher educationes_MX
dc.identifier.volume9es_MX
dc.identifier.issue11es_MX
dc.contributor.affiliationhttps://ror.org/03ayjn504es_MX
dc.contributor.affiliationhttps://ror.org/046ffzj20es_MX
dc.contributor.affiliationhttps://ror.org/04znhwb73es_MX
dc.subject.countryEstados Unidos de América / United Stateses_MX
dc.identificator4||58||5801es_MX


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