Leveraging Machine Learning to Predict ICT Proficiency levels among Public School Teachers in Bukidnon

This study utilizes the Digital Competence Framework for Educators (DigCompEdu) and machine learning (ML) techniques to evaluate and predict the ICT proficiency levels of public school teachers in Bukidnon. Analyzing a dataset of 1,275 responses and addressing data imbalances, several classification models were evaluated to identify the most reliable predictor of teacher competence. The findings indicate that the majority of teachers currently operate at the 'Integrator' (B1) level. Key predictors of proficiency include skills in online safety, collaborative learning, and the creative use of digital tools. Among the tested algorithms, Random Forest emerged as the most effective model for accurately classifying teacher skill levels. This research provides a data-driven roadmap for educational policymakers, offering actionable insights for designing targeted professional development programs that foster transformative teaching and improved student outcomes.