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Author: search.filters.author.Francisco Sebastián Vaca-Pinto

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    Beyond Linear Statistics: A Machine Learning Ecosystem for Early Screening of School Bullying
    (2026)
    Espinosa Pinos, Carlos Alberto  
    ;
    Paúl Bladimir Acosta-Pérez
    ;
    Aitor Larzabal-Fernández
    ;
    Francisco Sebastián Vaca-Pinto
    This study developed and validated a Machine Learning (ML) ecosystem for the early screening of school victimization among Ecuadorian adolescents, a phenomenon that poses a critical barrier to educational equity. Addressing previous methodological limitations, this research intentionally eliminated circular reasoning by excluding all internal psychometric items from the feature set, focusing strictly on sixteen socio-environmental and demographic predictors. A quantitative study was conducted with 1413 students in the province of Tungurahua, utilizing the Synthetic Minority Over-sampling Technique (SMOTE) to correct class imbalance. Supervised classification algorithms, including SVM, Random Forest, and XGBoost, were compared. The results demonstrated that the Random Forest model achieved the most balanced performance, reaching an Accuracy of 60.3% and a Macro F1-score of 0.382. Feature importance analysis identified household structure (Living_With_Monoparental) and Family_Coping_Capacity as the most significant predictors of high-risk profiles. These findings provided a statistically honest and ecologically valid tool for Student Counseling Departments (DECE), enabling a transition toward proactive risk identification grounded in observable social vulnerability rather than reactive symptom reporting.
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    Intervention against school bullying through emerging technologies: a literature review
    (2025)
    Acosta Pérez, Paul Bladimir  
    ;
    Espinosa Pinos, Carlos Alberto  
    ;
    Saa, Fernando  
    ;
    Francisco Sebastián Vaca-Pinto
    School bullying remains a persistent issue that negatively affects students' well-being and academic performance. Private educational institutions face unique challenges in addressing this problem due to limited resources and teacher training. This literature review explores the use of emerging technologies - such as virtual reality (VR), mobile applications, and artificial intelligence (AI) - as innovative tools to prevent and mitigate school bullying. Recent studies that implement these technologies in educational settings were analyzed to assess their effectiveness and applicability. The findings suggest that such tools can foster empathy, facilitate anonymous reporting, and enable early detection of incidents, contributing to the development of safer and more supportive school environments. © 2025 IEEE.
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