Utilizing Structured Equation Modeling and Machine Learning in Investigating Digital Competency in Public School Teachers in Bukidnon, Philippines

The rapid transition to digital education in the Philippines, accelerated by the COVID-19 pandemic, has highlighted significant integration challenges for public school teachers in rural provinces like Bukidnon. While digital proficiency is essential, existing studies often rely on either purely descriptive analytics or standalone machine learning models, which frequently fail to validate the complex, latent relationships between competency factors in terms of Digital Competency. To address these gaps, this research employs a two-phased hybrid analytical architecture. Phase I utilizes Structural Equation Modeling (SEM) to confirm the factor structure and establish the causal pathways of digital competency, ensuring that the theoretical framework is psychometrically sound. Phase II transitions these validated constructs into an optimized Machine Learning (ML) pipeline, incorporating SMOTE-ENN resampling to handle imbalanced regional data. Results from 1,275 participants demonstrate that "Professional Engagement" acts as the foundational engine of the digital competency system, while "Digital Pedagogy in Teaching" emerges as the most critical predictive determinant of teacher proficiency. The Random Forest algorithm achieved a high predictive accuracy of 89% and a Macro F1-Score of 85%, significantly outperforming traditional models. These findings indicate that the digital divide in this context is a pedagogical, rather than purely technical, bottleneck. The study provides a blueprint for the Department of Education to move from descriptive reporting toward Predictive Diagnostic Systems that can facilitate targeted, data-driven interventions.