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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.
Engagement and social impact in tech-based citizen science initiatives for achieving the SDGs: A systematic literature review with a perspective on complex thinking
(Sustainability Editorial Office, 2022-09-02) Sanabria Zepeda, Jorge Carlos; Alfaro Ponce, Berenice; González Peña, Omar Israel; Terashima Marín, Hugo; Ortiz Bayliss, José Carlos; Institute for the Future of Education, Tecnologico de Monterrey; School of Architecture, Art and Design, Tecnologico de Monterrey
Recent years have witnessed significant achievements and technological advances in Citizen Science (CS) projects; nevertheless, significant global challenges are present. Proof of this is the joint efforts of international organisations to achieve the 2030 SDG agenda in a complex environment. Thus, UNESCO has recognised CS among the initiatives that could bridge the Science, Technology, and Innovation gap as a substantial resource given its power to bring the general public closer. Although tech-based CS projects keep rising, there is limited knowledge about which type of projects might allow participants to develop higher-complex thinking skills. To this end, this study describes a Systematic Literature Review (SLR) and analysis of 49 CS projects over the last 5 years concerning the technology utilised, the level of citizen involvement, and the intended social impact. The results of the analysis evidenced a) broad implementation in Europe on issues of the built environment, disaster risk, and environmental and animal monitoring; b) prevalence of helix configurations other than the Triple, Quadruple, and Quintuple Helix innovation models; c) a focus on technological developments to improve living conditions in the city; d) an opportunity to develop applied native technologies; e) limited development of participants’ complex thinking, constrained to low levels of involvement; and f) an opportunity to develop native technologies and promote a higher level of citizen participation, leading to more significant impact whilst developing complex thinking.

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