Exploring the landscape of learning analytics privacy in fog and edge computing: A systematic literature review

The study systematically reviews the integration of Fog and Edge Computing within Learning Analytics to enhance data privacy and security in educational settings that use cloud computing. Employing the PRISMA methodology, we analyze current literature from Web of Science and Scopus databases to examine how these decentralized computing technologies can mitigate the risks associated with centralized cloud storage by processing data closer to its source. Our findings highlight the significant potential of Fog and Edge Computing to transform Learning Analytics by enabling real-time, context-aware data analysis that supports personalized learning while ensuring stringent data privacy. This approach challenges conventional data management practices, advocating for privacy by design in developing new strategies and frameworks. The research underscores the need for collaborative efforts in establishing standards and guidelines for secure and effective technology use in education, pointing towards the necessity of addressing technical, operational, and ethical challenges to maximize the benefits of fog and edge computing in Learning Analytics. •Fog and Edge computing improve data privacy for Learning Analytics and minimizes dependence on centralized cloud storage.•Nearer analytical technologies to users promotes a personalized and secure learning environment.•Future research should focus on creating secure architectures and policies to leverage Fog and Edge Computing in education.