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.
- Systemic thinking and gender: an exploratory study of mexican female university students(Springer Nature, 2023-11-10) Cruz Sandoval, Marco Antonio; Carlos Arroyo, Martina; De los Ríos Berjillos, Araceli; Instituto para el Futuro de la Educación, Tecnológico de Monterrey; https://ror.org/03ayjn504; https://ror.org/026p1jb43; https://ror.org/0075gfd51The purpose of this article is to present the results of a study conducted on a population of students from two educational institutions in western Mexico. The intention is to identify how students perceive their level of systemic thinking, focusing primarily on women. Thus, this article seeks to identify differences not only on the basis of gender (men–women) but also on the basis of social status (public and private institutions). Methodologically, a descriptive statistical analysis was carried out with which it was possible to conclude that, although statistically significant differences between men and women are not identified, they are found between groups of women in public and private institutions. This article invites reflection on the need to study possible gender gaps from an intersectional perspective, which considers the differences between genders and the various dimensions and relations of women in their educational process.
- Improving the attention span of elementary school children for physical education through an NAO robotics platform in developed countries(Springer Nature, 2022-03-17) Ponce Cruz, Pedro; Molina Gutiérrez, Arturo; Baltazar Reyes, Germán Eduardo; Mazon Parra, Nancy; https://ror.org/03ayjn504Education today faces a powerful enemy: the lack of interest from students, who, even if attending class, find themselves distracted. This enemy has been in our schools for a long time, but it has never been as strong as it is now. Technology has made it strong; phone-bearing children have become its ally. This investigation intends to return technology to our side as educators by proposing the use of an assistive robot, proving that it attracts the attention of students and motivates them to enjoy Physical Education (PE) class, where children learn how to live a healthy life, avoiding diseases and conditions such as obesity, diabetes, and heart problems. To prove this, we measured the attention levels and motivation of the students in two primary school classes, one, a traditional class, and the other, a robot-assisted class. The data was analyzed from both engineering and psychological perspectives. This study concludes that the attention span of children improves, and their motivation increases as a result of using an NAO robot. Consequently, a robot-assisted PE class can decrease diabetes, obesity, and strengthen heart functions when the children learn how to live a healthy life effectively.