Rebecca Ferguson discusses the future of learning analytics, emphasizing the importance of data to improve teaching and learning while also addressing ethical concerns surrounding data usage. She proposes that learners should control their own data and highlights the necessity of developing open systems and credible research to demonstrate the benefits of learning analytics. The document outlines a multi-faceted strategy that includes pedagogy development, infrastructure enhancement, and stakeholder engagement to effectively implement learning analytics in education.

1. Rebecca Ferguson, The Open University, UK
LASI Bilbao, June 2016
The future of
learning analytics

2. In order to make
sensible decisions,
we need to look
ahead and plan
for the future

3. 3
http://careers2030.cst.org/jobs/
Preparing for the future

4. The Open University (OU)
• The Open University: largest in UK
• Distance university
• Making use of big data for 45 years
• Informal learning: iTunes, YouTube…
• MOOCs on FutureLearn,
OpenLearn and elsewhere
• Learning analytics research and events
• Learning Analytics Community
Exchange
• LAEP – for European educational policy
4
www.laceproject.eu
laepanalytics.wordpress.com

5. Learning analytics help us
to identify and make sense
of patterns in the data
to improve our teaching,
our learning and
our learning environments

6. Visions
of the
future
bit.ly/28X5tq7

7. Visions
of the
future
bit.ly/28X5tq7
Which of these futures aligns best with your work?

8. Learners are
monitored by
their learning
environments

9. Provocation 1:
Learners are
monitored by their
learning
environments
Just as in bioethics,
there are
fundamental
human factors at
play here.
Too much Big
Brother vision to
be appealing.
I think it is a promising
line of work for
learning analytics. I
think there will be
many advances in the
use of sensors to
identify aspects that
can be applied to this
vision.

10. Learners’ personal
data is tracked

11. Provocation 2:
Learners’ personal
data is tracked
Wearable sensors are
already present, but in
the next future they
must be improved,
especially for health
purposes, such as
diabetes monitoring or
cardiovascular diseases
prevention.
Quantified self strongly
builds on reflection and
self-organisation. This
is good and feasible.
A reliable evidence base
for the effectiveness of
these measures and some
sort of safety control to
prevent irresponsible
recommendations.

12. Analytics are
rarely used

13. Provocation 3:
Analytics are
rarely used
There should be strong
discussion about the
Ethical concerns that
apply to each approach
to Learning Analytics
the research community
must concretely show the
benefits of the use of data
to improve educational
outcomes.
Focus less on analytics to
automate, but rather to be
student-centered, to inform the
learner and improve their
personal practice. Once analytics
provides real, tangible value to
the learner, you begin to both
alleviate concerns about privacy
AND develop faith that if you can
give control of data back to the
learners, they will opt-in to
learning analytics.

14. Learners
control
their own
data

15. Provocation 4:
Learners control
their own data
Absolutely
essential.
Organisations
must not rely on
the data, but try
to build trust
with their
learners so that
learners see the
benefit of
sharing.
Can we assume
that "data
owners" know
what to do with
their data? -
Some people
can't even
manage the
money in their
pockets.
Users should be entitled to
know how their data are
interrogated and used and
for what reasons. This
should be made explicit
and easy to understand
Greatly limits our ability to
effectively use learning
analytics to improve
learning for ALL students.

16. Open systems are
widely adopted

17. Provocation 5:
Open systems are
widely adopted
Define standards for the information
exchange - Define standards to
exploit the information stored -
Define guides about what
information is useful - Define and
publish different models to
represent the information
align with corporate
and stop thinking that
academic is leading!
A multi-pronged approach
involving IT services, national
organizations like JISC and
ministry of education and
tertiary institutions would all
need to be involved.

18. Learning analytics are essential tools

19. Provocation 6:
Learning analytics are essential tools
Very little credible research has
demonstrated any real large-scale
benefits to learners or institutions.
Learning is not only about
success is about learning from
failure. So, yes, I think that it
would be desirable that the
prediction rates are very high,
however, I don't think it is the
final goal.
What is needed is support
for reflection, discussion
and debate on the purpose
of it all, especially to curb
the excesses of those that
see learning as something
teachers do to students. We
need to nurture rich,
reflective communities

20. Analytics help learners
make the right choices

21. Provocation 7:
Analytics help learners
make the right choices
From bitter
experience, I'm aware
of the very slow pace
of institutional change
companies will sell
politicians on the
budget savings and
lead us here.
With learning there is an
element that learning is a
process – reducing it to
simply outcomes that can be
measured is dangerous
limited as ignores learning
through interactions with
both peers and the wider
social environment.

22. Analytics have largely
replaced teachers

23. Provocation 8:
Analytics have largely
replaced teachers
autonomy begets engagement,
motivation, persistence, relevance.
The collective is as
important as the
individual: it is not
just about how *I*
learn but how *we*
learn.
Self-directed learning can let students
improve a lot according to their needs.
But they also need the instructors to
guide them when they are confusing
and frustrated during the learning
process.

24. Visions
of the
future
bit.ly/28X5tq7
Which future are you working towards?

25. Priority areas for education and training
25
Open and innovative education and training, fully embracing
the digital era.
Strong support for teachers, trainers, school leaders and
other educational staff
Relevant and high-quality knowledge, skills and
competences developed throughout lifelong learning
Focus on learning outcomes for
employability, innovation, active citizenship and well-being
and inclusive education, equality, equity, non-discrimination
and the promotion of civic competences.

26. LAEP: learning analytics for European
educational policy
26
What is the current state of the art?
What are the prospects for the implementation
of learning analytics?
What is the potential for European policy to be
used to guide and support the take-up and
adaptation of learning analytics to enhance
education in Europe?

27. Action for analytics
27
Strategy
Research and development
Infrastructure
Context
Standards
Skills
Outreach

28. 28
Strategy
Example of a framework for learning analytics: Siemens, G., Gašević, D., Haythornthwaite, C., Dawson, S.,
Buckingham Shum, S., Ferguson, R., Duval, E., Verbert, K. & Baker, R.S.J.d. (2011). Open Learning
Analytics: An Integrated and Modularized Platform (Concept Paper). Download from solaresearch.org
• Align work on learning analytics with
strategic objectives and priority areas for
education and training
• Develop a roadmap for learning analytics
• Assign responsibility for development of
learning analytics
• Identify and build on work in
related areas and other countries
• Build on learning analytics
work to develop new priorities

29. 29
Research and development
• Develop pedagogy that makes
good use of analytics
• Develop analytics that address
strategic objectives and priorities
• Develop technology that enables
deployment of analytics
• Develop frameworks that enable
development of analytics

30. 30
http://evidence.laceproject.eu
Infrastructure
●Increase data-handling
capability
●Create organisational
structures to support use
of learning analytics
●Use Evidence Hub to
identify areas for
development
●Develop methods of
sharing experience
and good practice

31. 31
Context
●Align learning analytics
work with different
sectors of education
●Develop practices that
are appropriate to
different contexts
●Identify successful
financial models

32. 32
Standards
●Adapt and employ interoperability
standards
●Develop and employ ethical
standards, including data
protection
●Align analytics with
assessment practices
●Develop a robust quality
assurance process
●Develop evaluation
frameworks
http://www.laceproject.eu/deliverables/d7-1-interoperability-studies/
http://www.open.ac.uk/students/charter/essential-documents/ethical-use-student-data-learning-analytics-policy

33. 33
Skills
●Identify the skills required in
different areas
●Train and support educators to
use analytics to support
achievement
●Train and support researchers and
developers to work in this field
●Develop and support educational
leaders to implement these
changes
●Educate learners to use analytics
to support their own achievement

34. 34
Outreach
●Engage stakeholders
throughout the learning
analytics process
●Support collaboration
with commercial
organisations
●Promote awareness of
learning analytics

35. 35
solaresearch.org • LAK conferences • LASI events • LACE
Stay connected

36. 36
Slides online at www.slideshare.net/R3beccaF
Rebecca Ferguson @R3beccaF
http://r3beccaf.wordpress.com/