Visible to the public The Influence of Data Protection and Privacy Frameworks on the Design of Learning Analytics Systems

TitleThe Influence of Data Protection and Privacy Frameworks on the Design of Learning Analytics Systems
Publication TypeConference Paper
Year of Publication2017
AuthorsHoel, Tore, Griffiths, Dai, Chen, Weiqin
Conference NameProceedings of the Seventh International Learning Analytics & Knowledge Conference
PublisherACM
Conference LocationNew York, NY, USA
ISBN Number978-1-4503-4870-6
KeywordsCollaboration, data protection, data protection by default, data protection by design, learning analytics, learning analytics process requirements, learning analytics systems design, personal information, policy, policy-based governance, privacy by design, privacy frameworks, pubcrawl, Security by Default
Abstract

Learning 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.

URLhttp://doi.acm.org/10.1145/3027385.3027414
DOI10.1145/3027385.3027414
Citation Keyhoel_influence_2017