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Experimenting with soft robotics in education: A systematic literature review from 2006 to 2022
(IEEEXplore, 2024-03-05) Cayetano Jiménez, Israel Ulises; Martinez Rios, Erick Axel; Bustamante Bello, Rogelio; Ramírez Mendoza, Ricardo Ambrocio; Ramírez Montoya, María Soledad; https://ror.org/03ayjn504
Educational robotics (ER) is a discipline of applied robotics focused on teaching robot design, analysis, application, and operation. Traditionally, ER has favored rigid robots, overlooking the potential of soft robots (SRs). While rigid robots offer insights into dynamics, kinematics, and control, they have limitations in exploring the depths of mechanical design and material properties. In this regard, SRs present an opportunity to expand educational topics and activities in robotics through their unique bioinspired properties and accessibility. Despite their promise, there is a notable lack of research on SRs as educational tools, limiting the identification of research avenues that could promote their adoption in educational settings. This study conducts a Systematic Literature Review (SLR) to elucidate the impact of SRs across academic levels, pedagogical strategies, prevalent artificial muscles, educational activities, and assessment methods. The findings indicate a significant focus on K-12 workshops utilizing soft pneumatic actuators. Furthermore, SRs have fostered the development of fabrication and mechanical design skills beyond mere programming tasks. However, there is a shortage of studies analyzing their use in higher education or their impact on learning outcomes, suggesting a critical need for comprehensive evaluations to determine their effectiveness, rather than solely relying on surveys for student feedback. Thus, there is an opportunity to explore and evaluate the use of SRs in more advanced settings and multidisciplinary activities, urging for rigorous assessments of their influence on learning outcomes. By undertaking this, we aim to provide a foundation for integrating SRs into the educational robotics curriculum, potentially transforming teaching methodologies and enriching students’ learning experiences.
Forecasting gender in open education competencies: A machine learning approach
(IEEEXplore, 2023-11-29) Ibarra Vázquez, Gerardo; Ramírez Montoya, María Soledad; https://ror.org/03ayjn504
This 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.

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