Emotional development in postgraduate students through the application of Machine Learning

Jenniffer Sobeida Moreira-Choez^1*Wellington Remigio Villota-Oyarvide²Danny Meliton Meza Arguello³Regla Cristina Valdés Cabodevilla⁴Marlene Ruth Elena Loor Rivadeneira⁵Verónica Monserrate Mendoza Fernández⁵Miguel Ángel Lapo Palacios⁵Ángel Ramón Sabando García⁶

• ¹State University of Milagro, Milagro, Ecuador
• ²Catholic University of Santiago de Guayaquil, Guayaquil, Guayas, Ecuador
• ³Universidad Técnica Luis Vargas Torres, Esmeraldas, Esmeraldas, Ecuador
• ⁴Technical University of Ambato, Ambato, Tungurahua, Ecuador
• ⁵Technical University of Manabi, Portoviejo, Manabí, Ecuador
• ⁶Pontificia Universidad Católica del Ecuador, Santo Domingo, Ecuador

Emotional development is a central component in the academic formation and wellbeing of students, particularly at the postgraduate level, where academic, professional, and personal demands are considerable. This study aimed to analyze the emotional development of postgraduate students at the State University of Milagro through the application of machine learning. Methodology:The approach was quantitative, with a non-experimental and cross-sectional design. The TMMS-24 scale was employed to measure perceived emotional intelligence across dimensions such as attention, clarity, and emotional regulation. The sample, composed of 1,412 participants, was analyzed using various machine learning models, including AdaBoost, Random Forest, SVM, logistic regression, and KNN, evaluated through metrics such as AUC, accuracy, and recall. Results:AdaBoost and Random Forest were the most effective models, with AUC values of 0.996 and 0.972, respectively. AdaBoost achieved the highest F1-score (0.974), while Random Forest reached perfect recall (1.000) in students over 30. Both models showed strong predictive capacity across age groups. In contrast, logistic regression and SVM displayed limited performance, with AUCs below 0.56. These results confirm the superiority of ensemble methods in modeling emotional patterns. Conclusion: It is concluded that ensemble algorithms such as AdaBoost and Random Forest are effective tools for analyzing emotions in educational contexts. However, the study's scope was restricted to an academic setting. As a practical implication, the findings support the integration of emotionally focused interventions in higher education programs to enhance students' emotional development according to their specific needs.