Leveraging Cloud Data Integration for Enhanced Learning Analytics in Higher Education

Abstract

As higher education institutions increasingly adopt digital tools and technologies, the role of cloud data integration in enhancing learning analytics has gained significant importance. This paper explores the transformative potential of cloud-based data integration in the context of higher education, focusing on how the integration of diverse educational platforms can improve student outcomes and academic decision-making. The study examines the integration of Learning Management Systems (LMS), student information systems (SIS), data warehouses, and other academic and administrative platforms into a unified cloud infrastructure, enabling seamless access to real-time, multi-source data. The paper highlights the importance of cloud data integration in addressing the challenges associated with fragmented data systems that often hinder the effective use of learning analytics. By consolidating data from multiple sources—including student performance metrics, attendance records, online assessments, and engagement data—the proposed cloud infrastructure enables educators and administrators to gain comprehensive insights into student behavior and academic progress. These insights are critical for offering personalized learning paths that cater to individual student needs, promoting more adaptive and student-centered educational experiences. A key focus of the paper is the impact of real-time data processing and integration on improving learning outcomes. The research discusses how cloud-based integration facilitates the timely analysis of student data, allowing educators to identify at-risk students early and implement targeted interventions. The system's ability to process and analyze data