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Predicting open education competency level: A machine learning approach
(Science Direct, 2023-11) Ibarra Vázquez, Gerardo; Ramírez Montoya, María Soledad; Buenestado Fernández, Mariana; Olague, Gustavo; https://ror.org/03ayjn504; https://ror.org/046ffzj20; https://ror.org/04znhwb73
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.
Data analysis in factors of social entrepreneurship to design planning tools in complex thinkin
(Science Direct, 2023-08-22) Ibarra Vázquez, Gerardo; Ramírez Montoya, María Soledad; Miranda Mendoza, Jhonattan; https://ror.org/03ayjn504
This work presents the results of an exploratory pilot that analyzes the factors that influence self-reported social entrepreneurship competency and previous family backgrounds that might positively influence the development of the set of sub-competencies of complex thinking. It has been observed that individuals who put into practice the competencies that make up complex thinking perform better in overcoming the challenges of generating social value in contemporary society. Data were collected from a convenience sample of 47 students attending a private Higher Education Institution in Mexico using the “Profile of the Social Entrepreneur Instrumentóó. The data analysis comprised 1) validation of the instruments reliability, 2) tendencies and the frequency distribution of the data, 3) grouping by entrepreneurial family background, 4) the principal component analysis and 5) a clustering analysis. Our results support that forming Complex Thinking competencies for social entrepreneurship is not directly influenced by previous family experiences. Still, they recognize that these experiences are relevant in helping students become familiar with entrepreneurship-related issues. This paper empirically supports the hypotheses that social entrepreneurship experiences affect the correlations between social innovations and resolving complex global public problems.

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