Addressing Data Availability And Quality Issues In LGU Educational Systems For Effective Machine Learning Integration

Abstract

This study addresses the challenges of data availability and quality in Local Government Unit (LGU) educational systems. Local Government Units (LGUs) have major difficulties in implementing Machine Learning (ML) because of the presence of inconsistent, incomplete, and invalid data. The study used both quantitative and qualitative data gathering from the LGU stakeholders to evaluate existing data practices, establish standardized data collection procedures, develop a data management tool, and analyze the impact of technological solutions on enhancing data quality. According to study results, participants recognize the value of data in making decisions. However, due to inadequate technology infrastructure and a lack of standard operating procedures, data handling is still inefficient. The paper outlines a suggested architecture and information system to improve communication between schools and Local Government Units (LGUs), automate processes, and provide standardized data formats in order to improve data gathering. The results of the evaluation show that the suggested technical solution, which includes automated data collection systems and standardized data formats, made data easier to find and significantly reduced gaps and discrepancies. The proposed recommendations aim to lay the foundation for more effective, data-driven educational planning in LGUs, ensuring better outcomes for resource management and policy development. Future research should focus on long-term implementation strategies for ML integration, addressing both technical and organizational barriers. Future research should prioritize the development of long-term implementation strategies for machine learning integration, specifically targeting technological and organizational obstacles.