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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- Complex artificial intelligence models for energy sustainability in educational buildings(Springer Nature, 2024-07-01) Tariq, Rasikh; Mohammed, Awsan; Alshibani, Adel; Ramírez Montoya, María Soledad; https://ror.org/03ayjn504; https://ror.org/03yez3163Energy consumption of constructed educational facilities significantly impacts economic, social and environment sustainable development. It contributes to approximately 37% of the carbon dioxide emissions associated with energy use and procedures. This paper aims to introduce a study that investigates several artificial intelligence‑based models to predict the energy consumption of the most important educational buildings; schools. These models include decision trees, K‑nearest neighbors, gradient boosting, and long‑term memory networks. The research also investigates the relationship between the input parameters and the yearly energy usage of educational buildings. It has been discovered that the school sizes and AC capacities are the most impact variable associated with higher energy consumption. While ’Type of School’ is less direct or weaker correlation with ’Annual Consumption’. The four developed models were evaluated and compared in training and testing stages. The Decision Tree model demonstrates strong performance on the training data with an average prediction error of about 3.58%. The K‑Nearest Neighbors model has significantly higher errors, with RMSE on training data as high as 38,429.4, which may be indicative of overfitting. In contrast, Gradient Boosting can almost perfectly predict the variations within the training dataset. The performance metrics suggest that some models manage this variability better than others, with Gradient Boosting and LSTM standing out in terms of their ability to handle diverse data ranges, from the minimum consumption of approximately 99,274.95 to the maximum of 683,191.8. This research underscores the importance of sustainable educational buildings not only as physical learning spaces but also as dynamic environments that contribute to informal educational processes. Sustainable buildings serve as real‑world examples of environmental stewardship, teaching students about energy efficiency and sustainability through their design and operation. By incorporating advanced AI‑driven tools to optimize energy consumption, educational facilities can become interactive learning hubs that encourage students to engage with concepts of sustainability in their everyday surroundings.
- Health professionals’ competencies in the framework of complexity: Digital training model for education 4.0(Editorial CSIC, 2024-06-12) Fernández Luque, Antonia María; Ramírez Montoya, María Soledad; https://ror.org/01mqsmm97; https://ror.org/03ayjn504Complex environments require changes in training models for health professionals to support access to knowledge for teaching and clinical research decision-making. These changes require digital competency training to successfully address knowledge acquisition and lifelong learning in the digital health ecosystem. This research aims to analyse the habits of use of information resources by healthcare professionals in professional practices and to propose a model for training in digital competence. An exploratory and explanatory research method was chosen. Data collection techniques included pre-test and post-test questionnaires for the training activities. The program designed to develop digital competency under the model proposed in this study had significant acceptance among users who valued all the contents, resources, and teachers and considered it effective. Training in digital competencies is one of the drivers of digital transformation. Teaching-learning models in virtual environments pose challenges for 21st-century libraries.
- Identification of complex thinking related competencies: The building blocks of reasoning for complexity(SOLAR Society for Learning Analytics Research, 2024-03-06) Talamás Carvajal, Juan Andrés; Ceballos Cancino, Héctor Gibrán; Ramírez Montoya, María Soledad; https://ror.org/03ayjn504Complex thinking competency enhances the high cognitive capacities necessary for the future of education. This study aimed to analyze these capacities through its sub-competencies (critical, systemic, and scientific thinking). We worked with the Cross Industry Standard Process for Data Mining methodology, with an original database of class data of 33,319 unique students, 46 different variables, and a random identification number. The variables were sociodemographic information, academic information, subject admission, competencies, and activities. Statistical analyses identified correlations between competency and sub-competencies. The findings show that 1) critical thinking is strategic in the development of complex thinking and its sub-competencies; 2) Development of Critical Thinking skills early in the curriculum can lead to a cascade effect, enhancing competence and sub-competence development; and 3) an overall performance encompasses the semester results. The study is of value to the academic, technological, and social communities to provide opportunities for the design and implementation of challenging scenarios for the future of education
- Towards the development of complex thinking in university students: Mixed methods with ideathon and artificial intelligence(Elsevier, 2023-12-03) Castillo Martínez, Isolda Margarita; Argüelles Cruz, Amadeo José; Piñal Ramírez, Octavio Elías; Glasserman Morales, Leonardo David; Ramírez Montoya, María Soledad; Carreon Hermosillo, Alejandra; https://ror.org/03ayjn504; https://ror.org/059sp8j34; https://ror.org/05fj8cf83This article examines the capacity of an artificial intelligence (AI) platform to assess university students' mastery of complex thinking. Central questions addressed include the identification of essential technical elements that contribute to the platform's effectiveness, and how the AI platform can boost student motivation and cultivate complex thinking. The research employed a mixed-methods approach, anchored by an Ideathon centered on the Sharing Economy. Qualitative insights were drawn from semi-structured interviews with nine university students. The results indicate that the platform effectively evaluates complex thinking proficiency, differentiating between systemic, scientific, and innovative thinking. Notably, the AI-driven personalization of the platform was found to significantly enhance student motivation. This study highlights the capabilities of AI platforms in competency assessment and suggests that future research should investigate how such data can inform strategies to develop complex reasoning among diverse university student populations.
- Research competencies in university students: Intertwining complex thinking and Education 4.0(2023-10-09) George Reyes, Carlos Enrique; López Caudana, Edgar Omar; Ramírez Montoya, María Soledad; https://ror.org/03ayjn504Research competencies are skills that university students must develop to create and socialize scientific products during their academic live. In this research, an experience was implemented to improve the students’ competency levels through its imbrication with complex thinking and the use of Education 4.0 applications, such as remote team workflow development apps, web-based virtual reality, and social robotics. The study was sequential-quantitative and descriptive. A questionnaire was applied before and after the experience to know the perception of 105 Mexican university students, later a rubric was implemented for the teacher’s assessment. The results indicate that the students perceived an improvement in their research skills, however, the evaluation showed a difference between the student’s perception and the teacher’s regarding improvement in said skills. The experience can be scaled to other scenarios, where disruptive teaching strategies can support research skills development.
- 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.
- Complex thinking through a Transition Design-guided Ideathon: testing an AI platform on the topic of sharing economy(2023-05-30) Sanabria Zepeda, Jorge Carlos; Castillo Martínez, Isolda Margarita; González Pérez, Laura Icela; Ramírez Montoya, María Soledad; https://ror.org/03ayjn504This proof-of-concept study of an AI-based platform aimed to integrate a sequence of activities into the design of an online platform to assess the development of complex thinking competency in higher education students.
- 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.
- Lifelong learning and metacognition in the assessment of pre-service teachers in practice-based teacher education(Frontiers, 2022-05) Matsumoto Royo, Kiomi; Ramírez Montoya, María Soledad; Glasserman Morales, Leonardo David; Instituto Tecnológico y de Estudios Superiores de Monterrey; Faculty of Education, Universidad del Desarrollo; Institute for the Future of Education; School of Humanities and Education, Tecnologico de MonterreyInitial teacher education should prepare pre-service teachers to develop effective teaching and lifelong learning tendencies. This study aimed to identify the component to consider in pre-service teachers’ assessment processes that promote lifelong learning and develop metacognition skills. For this, it analyzed how the planned and implemented actions by the teacher educators in Practice-based Teacher Education programs promoted metacognition and lifelong learning in the pre-service teachers. The method was a mixed explanatory sequential design. Quantitative and qualitative instruments were applied. Information was obtained from the learning and assessment resources (72 syllabi and 14 assessment tasks) and pre-service teachers’ opinions (survey: n = 231, interviews: n = 8). The findings identified three main components: (i) authentic and relevant assessment tasks, (ii) prior communication of instructions and evaluation criteria, and (iii) frequent performance-focused feedback from peers and teacher educators during and at the end of assignments. The study results can be valuable in teacher education programs to strengthen assessment processes, promote lifelong learning tendencies, and develop metacognitive skills among the teachers in training.
- Systematic mapping: educational and social entrepreneurship innovations (2015–2020)(Emerald, 2021-11) Montes Martínez, Ruth; Ramírez Montoya, María Soledad; Instituto Tecnológico y de Estudios Superiores de MonterreyPurpose – This study aims to analyze recent publications (2015–2020) that refer to educational and social entrepreneurship to identify the primary emerging themes and gaps of entrepreneurship research and management that would be helpful for future studies and entrepreneurial ventures. Design/methodology/approach – The authors used systematic mapping to review 92 research articles that address educational innovation and social entrepreneurship. All the articles were published between 2015 and January 2020 and were found in the Web of Science (WoS) and Scopus databases. Findings – The data analysis identified the following: the articles most frequently cited, the journals that published the highest number of relevant articles, the geographical distribution of these publications and their authors, the context of the research, the lines and themes that emerged, and the gaps in the literature regarding the research and management of educational and social entrepreneurship. Research limitations/implications – The search for articles was limited to educational and social entrepreneurship innovations and the English language; thus, studies published in other languages were not analyzed. Originality/value – The analysis of this research allowed us to review concepts and identify research methods employed and thematic lines analyzed. Therefore, the work is of value for educational and social entrepreneurs and researchers who wish to examine such concepts or focus on areas not yet fully explored.