A Blueprint for a Blockchain-Based Architecture to Power a Distributed Network of Tamper-Evident Learning Trace Repositories

Abstract

The need to ensure privacy and data protection in educational contexts is driving a shift towards new ways of securing and managing learning records. Although there are platforms available to store educational activity traces outside of a central repository, no solution currently guarantees that these traces are authentic when they are retrieved for review. This paper presents a blueprint for an architecture that employs blockchain technology to sign and validate learning traces, allowing them to be stored in a distributed network of repositories without diminishing their authenticity. Our proposal puts participants in online learning activities at the center of the design process, granting them the option to store learning traces in a location of their choice. Using smart contracts, stakeholders can retrieve the data, securely share it with third parties and ensure it has not been tampered with, providing a more transparent and reliable source for learning analytics. Nonetheless, a preliminary evaluation found that only 56% of teachers surveyed considered tamper-evident storage a useful feature of a learning trace repository. These results motivate further examination with other end users, such as learning analytics researchers, who may have stricter expectations of authenticity for data used in their practice.