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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.
Active learning and education 4.0 for complex thinking training: analysis of two case studies in open education
(Springer, 2023-01-25) Ramírez Montoya, María Soledad; Patiño Zúñiga, Irma Azeneth; Buenestado Fernández, Mariana; Instituto Tecnológico y de Estudios Superiores de Monterrey
This article focuses on empirically analyzing the final products designed by 147 academics from 11 countries who participated in an international open education movement workshop by answering the research questions “What are the technopedagogical components of the products designed by the participants to encourage the open educational movement? and what practice of the open educational movement is being executed?” The article starts with a conceptual basis that describes the concepts of Active learning, Education 4.0, Complex Thinking and Open Education. It presents (1) the case study methodology on which this research is based, (2) two case studies on open education, (3) a game‑based intervention proposal to support instructors in training university students in complex thinking skills based on Education 4.0 technologies and game‑based learning principles, (4) and a discussion of the findings and opportunities for further work in the area. The findings of this study reveal that (A) the use of emerging and 4.0 technologies in initiatives of the open education movement continue to increase; (B) most of the open education initiatives designed by academics participating in the workshops were focused on the production of OER; and (C) inclusive access to education and continuing professional development of teachers is a constant concern addressed in open education initiatives. The results of this research suggest that training and development interventions implying the creation or design of open education initiatives should focus on encouraging all kinds of open education practices (i.e. use, production, dissemination and mobilization)

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