Design and Development of an Intelligent Education and Teaching Management System Based on Cloud Computing

The application of cloud computing in education has made resource management efficient, access instantaneous, and learning platforms scalable. Conventional education and teaching management systems are usually not automated, flexible, or smartly analytical. The current research proposes a cloud-based education management system augmented with a hybrid Decision Tree-K-Means algorithm with novel implementations optimized for cloud computing. The Decision Tree is expanded to include temporal learning patterns and dynamic learning indicators, enhancing the early identification of vulnerable students. In the meantime, the K-Means algorithm is augmented by cloud-aware adaptive clustering, providing real-time dynamic adjustment of groupings according to changing behavior and load in students and the system, thereby optimizing individualized learning pathways. These algorithmic extensions are placed within a cloud-native architecture facilitating elastic scaling, parallel processing, and transparent cross-platform accessibility. The system not only attains enhanced prediction accuracy (94.6 %) for predicting student performance but is also more responsive and user-friendly. In contrast to conventional rule-based systems, the proposed method provides a smarter, adaptive, and data-driven solution. This work adds a domain-specific and scalable framework combining machine learning and cloud computing to address the sophisticated needs of contemporary digital learning.