Intelligent Management of Education Quality Based on Big Data and Predictive Analytics in Higher and Secondary Specialized Vocational Education Institutions

This article proposes a data-informed framework for managing education quality in higher education and secondary specialized vocational institutions through Big Data and predictive analytics. It considers how learning management systems (LMS), student information systems, assessment records, and engagement logs can be combined to identify learning difficulties earlier and support timely interventions. The proposed framework links data governance, feature engineering, interpretable predictive modelling, human review, and evaluation of intervention outcomes. Rather than treating model accuracy as a sufficient indicator of success, the article emphasizes calibration, subgroup fairness, privacy, pedagogical relevance, and measurable educational outcomes. The framework is conceptual and does not report a newly conducted experiment or claim validated prediction rates. It offers a structured basis for future institutional pilots, including integration with platforms such as Moodle and HEMIS, subject to local data-protection rules and institutional approval.