Privacy in Learning Analytics – Implicationsfor System Architecture

Tore Hoel and Weiqin ChenOslo and Akershus University College of Applied Sciences,

Norway

Presentation at ICKM 2015, Osaka, Japan - 2015-11-05

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.

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

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

What is the stumbling block?

Lack of trust

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

Initial understanding of LA process (SC36/WG8)

Updated understanding of LA process (SC36/WG8)

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.

Vulnerability & Student Agency

We are more than our data!

Understanding Informed Consent

New perspectives on the use of data

Emerging understanding of data protection

Xu (2012) Privacy 2.0 in Online Social Networks

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!

Requirements for New Design – applying Privacy-by-Design Principles

• Open Architecture • Transparency & Trust• Ownership & Consent

Existing architectures

Jisc (United Kingdom)Open Learning AnalyticsArchitecture

Apereo Dimond Model

Proposed architecture

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

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