Evaluating Bayesian modeling to personalize teaching strategies for students with special needs

• Autores: More Calle, Alexander, Cunya Moreno, Zully Vicky, Lozano Rivera, Martin Wilson
• Formato: artículo
• Fecha de Publicación: 2025
• Institución: Universidad Cesar Vallejo
• Repositorio: UCV-Institucional
• Lenguaje: inglés
• OAI Identifier: oai:repositorio.ucv.edu.pe:20.500.12692/176958
• Enlace del recurso: https://hdl.handle.net/20.500.12692/176958
• Nivel de acceso: acceso abierto
• Materia: Inclusion; Random forests; Bayesian Modeling; Special Educational Needs (SEN); https://purl.org/pe-repo/ocde/ford#5.03.01

This study investigates the ability of Bayesian modeling to personalize teaching strategies for solving shape and movement problems for students with Special Educational Needs (SEN). Using an experimental design with a control and intervention group, an Intelligent Tutoring System (ITS) based on Bayesian Knowledge Tracking (BKT) was implemented, adapted to different SEN profiles. The results showed a statistically significant difference and a very large effect size (Cohen's d = 2.68) in favor of the intervention group, evidencing a substantial improvement in learning gain. Comparative model analysis indicated that Multiple Linear Regression (R² = 0.797) slightly outperformed Random Forest (R² = 0.739) in predictive ability. Simulation of BKT parameters revealed differential patterns according to SEN type, with dyslexia showing the highest probability of learning transition (P(T) = 0.308). The study concludes that Bayesian modeling is a viable and effective tool for personalizing teaching strategies for students with special educational needs (SEN), highlighting the need to consider continuous learning dimensions beyond diagnostic categories.