Presentation by Tore Hoel and Weiqin Chen at 115h International Conference on Knowledge Management, November 5th, 2015, Osaka, Japan

1. Privacy in Learning Analytics – Implications
for System Architecture
Tore Hoel and Weiqin Chen
Oslo and Akershus University College of Applied Sciences,
Norway
Presentation at ICKM 2015, Osaka, Japan - 2015-11-05

2. What is Learning Analytics?
The measurement, collection, analysis and reporting of
data about learners and their contexts, for purposes of
understanding and optimizing learning and the
environments in which it occurs.
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4. Benefits for the Teacher
• Monitor the learning process
• Explore student data
• Identify problems
• Discover patterns
• Find early indicators for success
• Find early indicators for poor marks or drop-out
• Assess usefulness of learning materials
• Increase awareness, reflect and self reflect
• Increase understanding of learning environments
• Intervene, advise and assist
• Improve teaching, resources and the environment
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5. What are the keys to make it work?
• Access to data
• Good predictive models
• Engagement and trust among students and faculty
• Institutional strategies
• Interoperability standards
• Well designed tools
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What is the stumbling block?
Lack of trust

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7. Challenges of design of new interoperable
solutions
• Understanding the process
• Understanding where the data come from
• Piloting new solutions
• Working with standards organisations to ensure interoperability
• Industry consortia
• IMS Global Learning
• Apereo
• Advanced Distributed Learning (ADL)
• Formal standardisation
• ISO/IEC JTC 1/SC36 Working Group 8 on Learning Analytics
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8. Initial understanding of LA process
(SC36/WG8)
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9. Updated understanding of LA process
(SC36/WG8)
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10. Research Questions:
What it means for technical-semantic interoperability
within the field of LA when privacy requirements, or more
widely, legal and organizational challenges, are translated
into technical solutions.
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11. Vulnerability & Student Agency
We are more than our data!
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12. Understanding Informed Consent
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13. New perspectives on the use of data
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14. Emerging understanding of data protection
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15. Xu (2012) Privacy 2.0 in Online Social Networks
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16. Informed consent and the Privacy Paradox
• Users may genuinely want to protect their personal data, but…
• …they may opt for immediate gratification instead (Xu, 2012)
• Informed consent is a limited waiver of rights and obligation (it is
not a permanent situation!) (Borocas & Nissenbaum, 2015)
• Privacy is all about context!
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17. Requirements for New Design –
applying Privacy-by-Design Principles
• Open Architecture
• Transparency & Trust
• Ownership & Consent
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18. Existing architectures
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19. Jisc (United
Kingdom)
Open Learning
Analytics
Architecture
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20. Apereo Dimond Model
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21. Proposed architecture
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23. Conclusions
• Introduction of a dynamic Search Middle Layer
• Establishing a Trust System as part of a search service outside the
Data Warehouses
• Dynamic Usage Agreements gives access to do search
• Only the search gives access to making sense of the data (access
to the ontology)
• Post-Search process allowing adjustment of the Search Context
and strengthening Student and Teacher Agency, e.g., learning
about use of one’s data
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24. The European LACE project builds a Community of Interest on Learning
Analytics – check out laceproject.eu
APSCE (Asian-Pacific Society for Computers in Education) has a Special
Interest Group on Learning Analytics – join the community!
tore.hoel@hioa.no
@tore
This work was undertaken as part of the LACE Project, supported by the European Commission Seventh
Framework Programme, grant 619424.
These slides are provided under the Creative Commons Attribution Licence:
http://creativecommons.org/licenses/by/4.0/. Some images used may have different licence terms.
www.laceproject.eu
@laceproject
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