The influence of data protection and privacy frameworks on the design of learning analytics systems
AbstractLearning analytics open up a complex landscape of privacy and policy issues, which, in turn, influence how learning analytics systems and practices are designed. Research and development is governed by regulations for data storage and management, and by research ethics. Consequently, when moving solutions out the research labs implementers meet constraints defined in national laws and justified in privacy frameworks. This paper explores how the OECD, APEC and EU privacy frameworks seek to regulate data privacy, with significant implications for the discourse of learning, and ultimately, an impact on the design of tools, architectures and practices that now are on the drawing board. A detailed list of requirements for learning analytics systems is developed, based on the new legal requirements defined in the European General Data Protection Regulation, which from 2018 will be enforced as European law. The paper also gives an initial account of how the privacy discourse in Europe, Japan, South-Korea and China is developing and reflects upon the possible impact of the different privacy frameworks on the design of LA privacy solutions in these countries. This research contributes to knowledge of how concerns about privacy and data protection related to educational data can drive a discourse on new approaches to privacy engineering based on the principles of Privacy by Design. For the LAK community, this study represents the first attempt to conceptualise the issues of privacy and learning analytics in a cross-cultural context. The paper concludes with a plan to follow up this research on privacy policies and learning analytics systems development with a new international study.
Hoel, T., Griffiths, David ORCID: 0000-0002-6863-2456 <http://orcid.org/0000-0002-6863-2456> and Chen, Weiqin (2017) The influence of data protection and privacy frameworks on the design of learning analytics systems. In: LAK '17 Proceedings of the Seventh International Learning Analytics & Knowledge Conference. ACM International Conference Proceeding Series . ACM, pp. 243-252. ISBN 978-1-4503-4870-6