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Predicting open education competency level: A machine learning approach

Abstract: This 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.

 Autoría: Ibarra-Vazquez G., Ramírez-Montoya M.S., Buenestado-Fernández M., Olague G.,

 Fuente: Heliyon, 2023, 9, e20597

 Editorial: Elsevier

 Año de publicación: 2023

 Nº de páginas: 15

 Tipo de publicación: Artículo de Revista

 DOI: 10.1016/j.heliyon.2023.e20597

 ISSN: 2405-8440

 Url de la publicación: https://doi.org/10.1016/j.heliyon.2023.e20597