Learning Analytics’ Privacy in the Fog and Edge Computing: A Systematic Mapping Review

The educational context that integrates Learning Analytics processes presents a high fragility in the data processing. In addition, using analytical technologies in cloud computing adds new drawbacks that increase such fragility and sensitivity in educational environments. However, there are alternatives to reduce fragility in Learning Analytics processes while processing data in the cloud but closer to the local context of the analysed roles. The cloud computing approach presents variations such as Fog computing or Edge computing that set intermediate distances more private and secure for data processing. Before adopting these in-between positions of data computation, it is compulsory to recognize the possibilities offered in terms of privacy. We aim to review the current literature regarding Learning Analytics and data privacy in Fog and Edge computing. Using the PRISMA methodology, we present a systematic mapping review of the literature in progress based on articles resulting from a search in the Web of Science and Scopus indexing databases.