Artículo
Permanent URI for this collectionhttps://hdl.handle.net/11285/345284
Artículo científico o editorial en una publicación periódica académica sujeto a revisión de pares. Cumple con los índices internacionales o bases de datos de amplia cobertura, como el listado del Current Contents, ISI WEB of Knowledge (http://isiknowledge.com/) e índice de revistas mexicanas de CONACYT (www.conacyt.mx/dac/revistas). Éstos indizan y resumen los artículos de revistas seleccionadas, en todas las áreas del saber.
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- Forecasting gender in open education competencies: A machine learning approach(IEEEXplore, 2023-11-29) Ibarra Vázquez, Gerardo; Ramírez Montoya, María Soledad; https://ror.org/03ayjn504This article aims to study the performance of machine learning models in forecasting gender based on the students' open education competency perception. Data were collected from a convenience sample of 326 students from 26 countries using the eOpen instrument. The analysis comprises 1) a study of the students' perceptions of knowledge, skills, and attitudes or values related to open education and its sub-competencies from a 30-item questionnaire using machine learning models to forecast participants' gender, 2) validation of performance through cross-validation methods, 3) statistical analysis to find significant differences between machine learning models, and 4) an analysis from explainable machine learning models to find relevant features to forecast gender. The results confirm our hypothesis that the performance of machine learning models can effectively forecast gender based on the student's perceptions of knowledge, skills, and attitudes or values related to open education competency.
- 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/04znhwb73This 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/03ayjn504This 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.
- Stories about gender inequalities and influence factors: a science club case study(Routledge, 2023-08-03) Buenestado Fernández, Mariana; Ibarra Vázquez, Gerardo; Patiño Zúñiga, Irma Azeneth; Ramírez Montoya, María Soledad; https://ror.org/03ayjn504This article explores the perception of gender inequality in science and the influencing factors. Data was collected through in-depth interviews with students belonging to a science club; we present it as a case study. This research sheds light on what high school and university students studying Science, Technology, Engineering, and Mathematics (STEM) perceive in Mexico, where the gender gap is the highest of all scientific disciplines, considering the relationship between science and gender through their study experiences and perspectives. The findings mainly revealed two positions: (1) denial of gender inequalities; and (2) recognition of gender inequalities associated with biological, psychological, and social factors. It is precisely this last factor that is based on a feminist position. How students define and label inequalities varies according to their participation in previous formative experiences linked to gender and contextual influences. Science education activities with a gender perspective are necessary in non-formal education spaces such as science clubs. In this sense, this work offers recommendations that can stimulate the design of training actions for a better-balanced integration of science and gender.
- Trends and research outcomes of technology-based interventions for complex thinking development in higher education: A review of scientific publications(2023-06-19) Patiño Zúñiga, Irma Azeneth; Ramírez Montoya, María Soledad; Ibarra Vázquez, Gerardo; https://ror.org/03ayjn504Complex thinking is a desired competency in 21st-century university students, so technology-based teaching and learning strategies must be carefully considered when training them in complex reasoning skills. This systematic review aims to map research on the use of teaching and learning strategies supported by technology to enhance complex thinking skills in university students. This review reports results according to PRISMA (preferred reporting items for systematic reviews and meta-analyses) guidelines. The search strategy was performed in June 2022 in Scopus and Web of Science databases. Of 151 records initially identified between 2018 and 2022, 32 papers were included in the final synthesis per the inclusion and exclusion criteria. The results of this review indicate that (1) tech-based strategies for complex thinking development are based on active learning approaches including problem-based learning, case-based learning, collaboration-driven and discussion-based learning, project-based learning, assessment- and feedback-oriented activities, and mind mapping techniques; (2) most of the documented strategies were implemented in hybrid contexts; (3) traditional instructional materials commonly used for promoting higher order thinking skills such as reading assignments, videos, and eliciting/reflexive questions are still effective in fostering complex thinking when delivered through technology; and (4) custom-built technological development for complex thinking development software that incorporates emerging technologies is scarce at present. Further research is needed to document the interventions that train students interactively in complex thinking skills using Education 4.0 technologies.
- Gender prediction through complex thinking competence using machine learning(Springer, 2023-06-13) Ibarra Vázquez, Gerardo; Ramírez Montoya, María Soledad; Terashima Marín, Hugo; https://ror.org/03ayjn504This article aims to study machine learning models to determine their performance in classifying students by gender based on their perception of complex thinking competency. Data were collected from a convenience sample of 605 students from a private university in Mexico with the eComplexity instrument. In this study, we consider the following data analyses: 1) predict students’ gender based on their perception of complex thinking competency and sub-competencies from a 25 items questionnaire, 2) analyze models’ performance during training and testing stages, and 3) study the models’ prediction bias through a confusion matrix analysis. Our results confirm the hypothesis that the four machine learning models (Random Forest, Support Vector Machines, Multi-layer Perception, and One-Dimensional Convolutional Neural Network) can find sufficient differences in the eComplexity data to classify correctly up to 96.94% and 82.14% of the students’ gender in the training and testing stage, respectively. The confusion matrix analysis revealed partiality in gender prediction among all machine learning models, even though we have applied an oversampling method to reduce the imbalance dataset. It showed that the most frequent error was to predict Male students as Female class. This paper provides empirical support for analyzing perception data through machine learning models in survey research. This work proposed a novel educational practice based on developing complex thinking competency and machine learning models to facilitate educational itineraries adapted to the training needs of each group to reduce social gaps existing due to gender.
- Digital competency as a key to the financial inclusion of young people in complex scenarios: A focus groups study(Sage, 2023-04-19) Buenestado Fernández, Mariana; Ramírez Montoya, María Soledad; Ibarra Vázquez, Gerardo; Patiño Zúñiga, Irma Azeneth; Instituto Tecnológico y de Estudios Superiores de MonterreyYoung people’s financial and digital literacy have been studied independently and in-depth during the last decades. However, digital financial literacy as a compound concept is novel and still needs to be explored in the scientific literature. This work investigated young people’s perception of their digital financial culture, identified factors that hinder or facilitate it, and explored their preferences in the training modalities for improvement. Twenty-two focus groups were carried out in different Mexican educational institutions based on diversity criteria. The evidence shows that: (1) young people perceive the need for digital financial education linked mainly to the understanding of critical concepts, the use of mobile applications, online financial operations, and digital financial security; (2) some voluntarily exclude themselves from online finance, this being one of the main obstacles in the development of digital financial culture; (3) the digital financial culture gap is accentuated more among young people in public educational institutions and upper secondary education; (4) they have a preference for emerging technological and digital resources for their training in digital finance. These findings make it possible to contextualize training proposals that favor the financial inclusion of young people in complex scenarios brought about by the digital transformation of the economy and society.