Privacy for learning analytics in the age of big data – exploring conditions for design of privacy solutions

Abstract

Introduction of learning analytics to education opened up the can of worms related to privacy issues that come with big data. Privacy issues are increasingly ‘wicked problems’ that call for a rethinking of the key artefacts involved. Global information systems make privacy a challenge that go to the center of solution design and information science research. In this dissertation research we exemplify the long and winding process from capturing questions of concern, to constructing conceptual artefacts to begin discussing the concerns, to proposing the first constructs that could lead to technical solutions—all within the context of technology enhanced learning and education. Learning analytics is a new discipline based on an increasing access to data, which will be extended by introduction of more and more sensors that are part of smart classrooms and intelligent campus projects. There is a gap between people’s online sharing of personal data and their concern about privacy. However, online practices are volatile, which make action design research and design science research an appropriate approach to explore conditions for design of privacy solutions. The research has been carried out taking part in two practice communities, the learning analytics knowledge community, and the learning technologies standards community. The contributions of this PhD research are both theoretical and practical. Privacy is defined in the context of big data; the theory of contextual integrity is extended to include the concept of ‘context trigger’, and design proposals explore the role of privacy policies in regulating data sharing. Risks and benefits of data sharing is explored to develop a learning analytics design space model. In addition, other constructs to facilitate discourse on data sharing in context are developed. Keywords: privacy, privacy engineering, contextual integrity, personal data, learning analytics, big data