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

Daniel Amo-Filva, David Fonseca, Francisco José García-Peñalvo, Marc Alier Forment, Maria José Casany Guerrero, Guillem Godoy

Research output: Indexed journal article Articlepeer-review

Abstract

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.

Original languageEnglish
Article number108303
JournalComputers in Human Behavior
Volume158
DOIs
Publication statusPublished - Sept 2024

Keywords

  • Edge computing
  • Education
  • Fog computing
  • Learning analytics
  • Privacy

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