Empirical Study on Precision Education Leadership Driven by Educational Big Data on Students' Academic Quality and School Teaching Effectiveness

Abstract

Digital transformation is fundamentally reshaping the core logic of educational management. As a pivotal production element in educational activities, educational big data is deeply integrated with data mining and machine learning technologies, providing technical support
for the precise transformation of educational leadership. The stability of student academic performance and the efficiency of school teaching
processes are key indicators of high-quality education development. Traditional educational leadership, relying on experience-based decisionmaking, struggles to accurately capture dynamic learning situations and teaching deficiencies, resulting in insufficiently targeted interventions
and limited effectiveness. This paper systematically examines the core concepts of educational big data and precision-oriented educational
leadership, analyzes the application pathways of data mining and machine learning in learning situation diagnosis and decision optimization,
explores their impact mechanisms on student academic quality and school teaching efficiency, and provides theoretical support and practical
references for the transformation and upgrading of educational leadership in the digital era.