Integrating Machine Learning Algorithms into Educational Management to Enhance Decision-Making Capabilities

To address the need for precision in educational management decision-making, this study integrates machine learning algorithms into higher education management practices. A framework of 35 core indicators, including learning behavior, course participation, and ability development, is constructed to analyze and support decision-making. The system has been applied in practice, assisting educational administrators in analyzing the learning trajectories of 8,752 students and making management decisions with a warning accuracy rate of 93.5%. The system has successfully guided the improvement of 485 learning problems. Over the 12 months of deployment, user satisfaction has reached 92.5%, management decision-making efficiency has increased by 47%, and annual management costs have been reduced by 1.2 million yuan, providing a practical reference for the digital transformation of educational management.