This study investigates the integration of Machine Learning (ML) into a Local Government Unit (LGU) educational policymaking to enhance educational outcomes, streamline administrative processes, and enable data-driven decision-making. It identifies challenges including lack of technical expertise, inadequate infrastructure, funding limitation, and ethical concerns such as data privacy and bias mitigation. A literature review identifies ML algorithms applicable to education policymaking, including decision trees, neural networks, clustering, and random forest algorithm. The proposed framework emphasizes the need for high-quality datasets, robust data management systems, computational resources, and trained expertise. Stakeholder engagement, involving educators, policymakers, and community representatives, ensures alignment of ML solutions with educational goals. Upgrading data infrastructure storage solutions and comprehensive data governance frameworks is essential for efficient ML deployment. Additionally, establishing ethical guidelines for responsible data handling and bias mitigation, as well as regular performance evaluations, will ensure the fair and effective use of ML. By leveraging ML, LGUs can enhance educational outcomes, streamline administrative processes, and create data-driven, equitable educational policies. Further research is encouraged to expand data collection, apply the proposed ML system, conduct longitudinal studies, and continuously refine the framework, empowering LGUs to harness the transformative potential of ML in education policymaking.
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