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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 competencies for leader education: artificial intelligence analysis in student achievement profiling(Taylor @ Francis Online, 2024-07-21) Ramírez Montoya, María Soledad; Morales Menendez, Ruben; Tworek, Michael; Escobar Díaz, Carlos Alberto; Tariq, Rasikh; Tenorio Sepúlveda, Gloria Concepción; https://ror.org/03ayjn504; https://ror.org/03vek6s52Future education requires fostering high-level competencies to enhance student talent, and artificial intelligence (AI) can help in profile analysis. The aim was to determine the variables that predict the GPA of students in the ‘Leaders of Tomorrow’ program through an integrated methodology of data analytics, machine learning modeling, and feature engineering in order to generate knowledge about the application of AI in social impact programs. This research focused on 466 graduates of a ‘Leaders of Tomorrow’. A regression analysis was performed to model the relationship between the dependent variable and multiple independent variables. The findings revealed: (a) Analysis of variance (ANOVA) demonstrated exceptional model fit for predicting ‘student.term_Grade Academic Performance (GPA)_program’ with an R-squared of 0.999; (b) Visual analysis showed that significant variables like age and origin-school Grade-Point Average (GPA) affect term GPA; (c) Kendall tau correlation revealed a positive correlation of origin-school GPA with term GPA and a slightly negative one with age; (d) Support Vector Machine (SVM) regression aligned actual and predicted GPAs closely, indicating high accuracy; and (e) Recursive Feature Elimination (RFE) identified ‘student_originSchool.gpa’ as the most predictive feature. This study is intended to be of value to academic communities interested in enhancing the academic profiles of students with complex competencies, as well as communities interested in applying AI in education for predictions that contribute to trajectories for training.
- 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.