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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.
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/03ayjn504
This 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.

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