The JRC report on Learning Analytics, 2017, gave a list of actions to policymakers. The panel input presents two national examples that follow the line of actions

1. 1
The European Commission’s
science and knowledge service
Joint Research Centre
SHEILA workshop
Policy-perspective:
Supporting capacity
development
•
Riina Vuorikari, PhD
•
DG JRC – Directorate Innovation and Growth
SHEILA, 5 June 2018

2. 2
European Commission in-
house science and
knowledge service
Policy neutral: No
policy agenda of its own
Independent of private,
commercial or national
interests
The Joint Research Centre (JRC)
Dir. Growth &
Innovation
Seville

5. The Action List for Learning Analytics
•
Seven areas of activity
•
Outlines a set of actions for educators,
researchers, developers and policymakers so that
learning analytics are used to drive work in
Europe’s priority areas for education and training
•
Suggest that actions should be taken at:
• European level
• National level
• Local level

6. I. 1.1 Develop common visions of learning analytics that address
strategic objectives and priorities
II. 1.2 Develop a roadmap for learning analytics within Europe
III. 1.3 Align learning analytics work with different sectors of education
IV. 1.4 Develop frameworks that enable development of analytics
V. 1.5 Assign responsibility for development of learning analytics within
Europe
VI. 1.6 Continuously work on reaching common understanding and
developing new priorities
1. Policy leadership and governance
practices

7. By the Finnish Ministry of Education and Cultura

9. Some examples of action list

10. • 1.1 Develop common visions of
learning analytics that address
strategic objectives and priorities
• What we measure shows what we value. A danger with
analytics is that they prompt educational institutions to
generate data that can be processed easily, focusing on
assessment that shows how much information has been
retained.
• Shift work towards analytics that support commonly agreed
visions and goals – from performance metrics to include other
values such as the delight and motivation

11. •
1.2 Develop a roadmap for learning
analytics within Europe
• A European roadmap for learning analytics development could
be used to drive the construction and development of a set of
interoperable learning analytics tools that are tailored for the
needs of Europe and that have been shown to work in practice
• The development of sustainable tools and practices that outlive
individual projects and can be deployed outside the settings in
which they are developed
Action point: At European level, work with learning analytics experts,
educators, vendors and policymakers to develop a roadmap for learning
analytics within Europe that is aligned with Europe’s priority areas, fills
gaps in the European toolkit and supports the development of sustainable
tools and practices.

12. •
1.3 Align learning analytics work
with different sectors of education
• European priorities cover all areas of education and training,
with a focus on making lifelong learning and mobility a reality
and increasing opportunities for open education and training.
However, as the LACE Evidence Hub (see the Inventory no: 51)
indicates, much of the work in this area takes place within
formal education, usually in the context of learning at
secondary level and above.
Action point: At European and national levels, explore the possibility of
funding and supporting learning analytics work that extends into the
workplace, work that focuses on implementation in informal learning
settings, and qualitative work that considers the factors influencing
success or failure when learning analytics are applied in different contexts.

13. •
Create organisational structures to support use of learning
analytics
• To avoid failure and maximize success, implementation of
learning analytics at scale requires explicit and careful
consideration of the entire TEL complex: the different groups of
people involved, the educational beliefs and practices of those groups,
the technologies they use, and the specific environments within which
they operate.
• Educators need to be involved in designing the tools and able to
evaluate any implementation of analytics tools in order to use
them effectively. So do support staff, library staff, IT staff…
• E.g. Case study on University of Technology Sydney, SHEILA (on-going
project-Erasmus+)
Action point: At European level, identify ways in which the funding system
can be adapted to support learning analytics implementation that works
2. Institutional leadership and
governance practices

14. •
Develop and employ ethical standards, including data
protection
• Europe’s General Data Protection Regulation (GDPR) entered
into force in May 2016 and will affect the learning analytics
field in many ways. Europe has taken the position that
individual privacy is important and that changes to current
practices in general analytics are needed see also: Cormack, A., A
data protection framework for learning analytics. Journal of Learning
Analytics, 2016. 3(1).
• E.g. Case study on OU ethics policy and Ethical use of student data
policy,
EP4LA by LACE project, Jisc code of practice ,
Learning analytics: a guide for students’ unions – NUS;
Action point: At national level, develop and share model policies on data
privacy and data protection, and support institutions to understand their
2. Institutional leadership and
governance practices

15. •
Identify and build on work in related areas and other
countries
•
The Inventory of learning analytics tools, policies and practices
•
provides a good starting point, while work from Australia provides
a good example of a strategy that is being based on sound
research [3, 4].
•
Action point:
•
At European level, commission reports from countries that are active
in the field of learning analytics and update these on a regular basis and
support the development of an accessible repository for learning
analytics evidence (e.g. LACE Evidence Hub).
•
At national level, support and develop active national networks of
learning analytics stakeholders.
•
At European, national and local levels, promote work on learning analytics
3. Collaboration and Networking

16. •
Develop pedagogy that makes good use of analytics
•
Successful analytics do not begin with a set of data; they begin
with an understanding of how people learn
•
e.g. see: Improving students’ learning habits - CLARA, Helping students to
reflect: Open Essayist, Supporting collaborative or group learning: SNAPP
•
Action point: identify areas of relevant expertise, and analytics work that
can be developed and aligned with European priority areas.
•
Action point: At local level, identify how current work and expertise can
be aligned with European priority areas and with other work in these
areas.
4. Teaching and Learning Practices

17. •
Develop a robust quality assurance process
•
More empirical evidence is needed about the effects of learning
analytics. This will form part of a process of quality assurance, which will
be essential to the development of user trust in analytics.
•
The ‘black-box’ nature of many learning analytics. Data are entered
and results are generated, but it is not clear to the end user how those
results have been generated.
•
Quality assurance will involve checking the quality of data used, the
validity and reliability of tools, and whether they are employed effectively
in specific contexts.
•
Action point: Some of this work must be carried out at an institutional
level, but there is also a role for national or international quality
assurance.
5. Quality assessment and assurance

18. •
Identify the skills required in different areas
•
The skills under consideration are not only those which a ‘data
scientist’ or ‘data wrangler’ might possess, even if these people
were required to be able to link pedagogy and analytics.
It is not currently clear exactly which skills are needed, and how many
people already possess them.
•
Action point: At European and national levels, fund research on the data
literacies in need of development, the effectiveness of methods for their
development, the role of teaching support staff as analytics interpreters,
and the development and validation of more intuitive and easily
interpretable analytics outputs.
6. Capacity building

19. •
Develop technology that enable development of analytics
•
Work in Australia suggests a need to develop national data inventories,
identifying gaps in data collection that need to be addressed through
additional data collection activity and instruments.
•
Data from other sources are needed to complement activity data from
VLEs. E.g. see Conexus Vokai tool that draws on a range of anonymised
data from Statistics Norway; Denmark User Portal Initiative; Pilot by
SURFnet to provide a model for formative assessment (assessment for
learning, rather than of learning) and data about student dispositions.
•
Action point: At European and national levels, compile data inventories in
order to identify and address gaps in data collection.
•
Action point: At national level, where appropriate, develop centralised
databases to facilitate the collection and amalgamation of datasets that
can be used to support learning analytics work
7. Infrastructure

20. Comments and questions for now?
…about the Action List for Learning
Analytics, the areas, actions, their
validity to your work at the Member
State level …

21. Link
https://ec.europa.eu/jrc/en/publication/eur-scientific-and-technical-research-report
•
Ferguson, R., Brasher, A., Clow, D., Cooper, A.,
Hillaire, G., Mittelmeier, J., Rienties, B., Ullmann,
T., Vuorikari, R., Research Evidence on the
Use of Learning Analytics and Their
Implications for Education Policy.
(forthcoming), JRC.