The document discusses learning analytics, highlighting its role in the measurement and optimization of learning environments and learner outcomes. It outlines priority areas such as open education, supporting educators, and enhancing employability through data analysis while raising ethical concerns regarding data privacy and learner autonomy. The text suggests a need for a collaborative approach across various educational sectors to develop effective learning analytics frameworks and infrastructure.

1. 1
Learning analytics:
the way ahead
Rebecca Ferguson
The Open University
Nordic LASI, 2017

2. 2
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.

3. Priority areas for education and training
3
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.

4. • Our researchers and students shall contribute
insight and communication of knowledge in
public discussions.
• We will challenge the knowledge front and
conventional notions through critical analysis
and knowledge made available to everyone.
• Through research and education, we shall
contribute towards challenging power
structures and promote a diversified and
sustainable society.
• We shall have the reputation of being a
national institution of culture and a crucible for
new ideas, innovation and new ways of
learning.
• We shall be a meeting place for staff, students
and society at large in an attractive arena for
lifelong learning.
• We shall have a strong and vibrant university
democracy characterized by generosity,
openness, diversity and dialogue.

5. Priority areas for education and training
5
• Bringing together different sectors: higher education, schools & workplace learning
• Building networks that have outlived the project’s funding period
• Helping to develop learning analytics capability
• Creating and sharing resources
• Developing visions of the future and agreeing how to work towards them
http://www.laceproject.eu/

6. 6
http://careers2030.cst.org/jobs/
Preparing for the future

7. 7
(Just over) a decade of change
2012: ‘Year of the MOOC’
2007: Launch of the iphone
2006: First tweets

8. Provocations
for visions
of the
future
Full report
bit.ly/28X5tq7

9. Provocation 1:
Learners are
monitored by
their learning
environments

10. 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

11. Provocation 2:
Learners’ personal
data are tracked

12. Learners’ personal
data are 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

13. Provocation 3:
Analytics are
rarely used

14. Analytics are
rarely used
There should be strong
discussion about the
ethical concerns that
apply to each approach
to learning analytics
Focus less on analytics to
automate, but rather to be
student-centred, 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.
The research community
must concretely show the
benefits of the use of data
to improve educational
outcomes

15. Provocation 4:
Learners
control
their own
data

16. 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

17. Provocation 5:
Open systems are
widely adopted

18. 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
organisations like JISC and
ministry of education and
tertiary institutions would all
need to be involved

19. Provocation 6:
Learning analytics are essential tools

20. 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

21. Provocation 7:
Analytics help learners
make the right choices

22. 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

23. Provocation 8:
Analytics have largely
replaced teachers

24. 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

25. 25
Learning analytics for European educational policy
LAEP
• 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?

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

27. 27
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

28. 28
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

29. 29
Context
●Align learning analytics
work with different
sectors of education
●Develop practices that
are appropriate to
different contexts
●Identify successful
financial models

30. 30
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

31. 31
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

32. 32
Outreach
●Engage stakeholders
throughout the learning
analytics process
●Support collaboration
between academics and
commercial organisations
●Promote awareness of
learning analytics

33. 33
Research and development
Develop the evidence base
• 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

34. 34https://xkcd.com/1739/

35. 35
A problem that we can address
Very little hard evidence is currently
available that is based on anything other
than short-term studies.
Some positive work is cited in the LACE
Evidence Hub but, at this stage, there is
no overwhelming evidence that
learning analytics have fostered more
effective and efficient learning
processes and organisations.
However, there is convincing evidence in
the Inventory and Case Studies that
companies and organisations believe
they can do this in the future, and are
prepared to invest time and resources in
order to achieve this.

36. 36
Clow, LAK12, http://oro.open.ac.uk/34330/

37. http://evidence.laceproject.eu/

38. http://evidence.laceproject.eu/evidence/evidence-flow-map/

45. 45
Slides online at www.slideshare.net/R3beccaF
Rebecca Ferguson @R3beccaF
http://r3beccaf.wordpress.com/