Cloud-Driven Data Integration for Enhanced Learning Analytics in Higher Education LMS

Abstract

The rapid growth of online education has increased the demand for robust Learning Management Systems (LMS) that provide both scalability and advanced learning analytics. This paper explores the development of a cloud-driven data integration system aimed at optimizing LMS platforms used in higher education. The system facilitates the seamless integration of diverse datasets, including student demographics, engagement metrics, and course content, to deliver comprehensive learning analytics and support personalized education strategies. The cloud-based architecture proposed in this paper offers scalability to manage the ever-growing volume of student and academic data, ensuring that institutions can provide uninterrupted services even during peak usage times. The integration layer within the system enables real-time data collection from various educational tools and platforms, such as digital libraries, student portals, and assessment systems. By processing this data in the cloud, the system provides educators and administrators with actionable insights through advanced analytics and reporting dashboards. One of the key contributions of this research is the incorporation of machine learning algorithms into the LMS to predict student performance and engagement trends. By using historical and real-time data, the system delivers personalized recommendations to students, supporting differentiated learning paths based on individual progress. The paper also addresses data security and privacy concerns by leveraging encryption and access control mechanisms within the cloud infrastructure. This cloud-driven data integration solution enhances the capabilities of LMS platforms in higher education, enabling institutions to offer data-driven, adaptive learning experiences. It supports institutional goals of improving student retention and success rates while reducing operational costs associated with maintaining disparate data systems.