Privacy for learning analytics in the age of big data – exploring conditions for design of privacy solutions
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,
...
Publisher
Jyväskylän yliopistoISBN
978-951-39-8190-7ISSN Search the Publication Forum
2489-9003Contains publications
- Artikkeli I: Hoel, T., Chen, W., & Mason, J. (2016). Data sharing for learning analytics – exploring risks and benefits through questioning. Journal of the Society of e-Learning. Vol.1. No.1.
- Artikkeli II: Hoel, T. & Chen, W. (2018). Privacy and data protection in learning analytics should be motivated by an Educational maxim - towards a proposal. Research and Practice in Technology Enhanced Learning. DOI: 10.1186/s41039-018-0086-8
- Artikkeli III: Hoel, T. & Chen, W. (2019). Privacy engineering for learning analytics in a global market – defining a point of reference. International Journal of Information and Learning Technology. DOI: 10.1108/IJILT-02-2019-0025
- Artikkeli IV: Hoel, T., & Chen, W. (2016). Privacy-driven design of learning analytics applications: exploring the design space of solutions for data sharing and interoperability. Journal of Learning Analytics, 3(1), 139–158. DOI: 10.18608/jla.2016.31.9
- Artikkeli V: Hoel, T. & Chen, W. (2015). Privacy in learning analytics – implications for system architecture. Watanabe, T. and Seta, K. (Eds.) (2015). Proceedings of the 11th International Conference on Knowledge Management.
- Artikkeli VI: Hoel, T. & Chen, W. (2016). Data sharing for learning analytics – designing conceptual artefacts and processes to foster interoperability. In Chen, W. et al. (Eds.) (2016). Proceedings of the 24th International Conference on Computers in Education. India: Asia-Pacific Society for Computers in Education.
- Artikkeli VII: Hoel, T. & Chen, W. (2018). Interaction between standardisation and research – a case study. In International Journal of Standardization Research (IJSR). Vol 16. Issue 1. DOI: 10.4018/IJSR.2018010102
- Artikkeli VIII: Hoel, T., Chen, W., & Gregersen, A.B. (2018). Are norwegian academic librarians ready to share usage data for learning analytics? Nordic Journal of Information Literacy in Higher Education,Vol 10, No 1. DOI: 10.15845/noril.v10i1.269
- Artikkeli IX: Hoel, T. & Mason, J. (2018). Standards for smart education - towards a development framework. Smart Learning Environments. Springer Open. DOI: 10.1186/s40561-018-0052-3
- Artikkeli X: Hoel, T. & Chen, W., & Pawlowski, J.M. Making context the central concept in privacy engineering. Submitted for review.
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