The document summarizes a report by the Joint Research Centre (JRC) on learning analytics. The JRC conducted a study between 2015-2016 that included an inventory of 60 learning analytics tools, practices and policies, as well as 5 case studies. The study found that most learning analytics work is not strongly aligned with European priorities for education. It identified several research challenges, including developing a common vision for learning analytics in Europe, building tools that help teachers and learners, and conducting research that validates tools. The report suggests policy goals should drive learning analytics research.

1. The European Commission’s
science and knowledge service
Joint Research Centre
Learning Analytics –
Research challenges arising from
a current review of LA use
Aulanko, Finland April 5 2017
Dr. Riina Vuorikari
DG JRC – Directorate Innovation and Growth
Unit B4 Human Capital and Employment

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Focus on the priorities of the
European Commission:
working for more than
20 policy DGs
Policy neutral:
has no policy agenda
of its own
Independent:
no private, commercial or
national interests
The Joint Research Centre (JRC)
Directorate
Growth &
Innovation
Seville

3. DigComp
(DG EMPL)
EntreComp
(DG EMPL)
DigCompConsumers
(DG JUST)
Openedu Policies(HE)
(DG EAC)
MOOCKnowledge
(DG EAC)
Blockchain
(DG JRC)
OPTEV
(DG JRC)
MOOCs4 inclusion
(DG EAC)
Learning Analytics
(DG JRC)
Anticipatory studies Policy & society
OrganisationsIndividuals
DigCompEdu
(DG EAC)
DigPolEdu
(DG EAC)
CPDmodels
(DG EAC)
ICTinPISA
(DG EAC)
CompuThink
(DG JRC)
DigCompOrg4Schools
(DG EAC)
OpenEdu (HE)
(DG EAC)
DigCompOrg
(DG EAC)
Current JRC research on Digital Age Learning
and 21st Century Skills

4. Riina Vuorikari
• A research fellow at the JRC
in Seville since June 2013
• 2000-2013 in European Schoolnet
as Senior Research Analyst and
Project Manager
• Background:
• OKL in Savonlinna (MEd)
• Studying abroad (exchange
and postgraduate studies)
e.g. hypermedia (DEA)
web, use of ICT in education
• Doctoral 2009 from the Dutch School
Information and Knowledge System
• https://www.slideshare.net/vuorikari

5. The European Commission’s
science and knowledge service
Joint Research Centre
. Part 1: Introduction to the
Report: aims, inventory of LA
(10min)
. Part 2: Some results of the
study
(10 min)
. Part 3: research challenges
(10 min)
. Part 4: Short discussion
(10 min)
Outline:

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Learning analytics involve
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.
(source)
Learning analytics have their roots in many fields of
educational and technical research, including assessment,
personal learning and social learning, but also in business
intelligence and data mining.
The field draws on theory and methodologies from
disciplines as statistics, artificial intelligence and
computer science (Dawson et al., 2014).

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The Study behind the JRC Report
• Goal: Provide research evidence on the use of
learning analytics and discuss their implications
for education policy
• Study conducted between September 2015-June
2016
• Design of the study: the JRC in Seville,
Unit of "Human Capital and Employment”
• Research: The Open University, UK under the
contract and supervision of the JRC

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To access the Inventory
• Google “leap inventory learning analytics”
• http://cloudworks.ac.uk/cloudscape/view/2959
• Google “learning analytics JRC science hub”
To access the Report

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What does the Study contain?
• An inventory of recent implementations of learning
analytics:
• Tools, practices and policies (60 examples)
• 5 case studies
• Review of research literature on implementation
• To critically reflect on the impact, potential and limits
of using learning analytics in education
• To consider the implication for education policy:
“The Action List for Learning Analytics”

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The Inventory: Tools
• 26 examples with international focus
• Descriptions available online
A template used for describe tools:
• Inventory type: Design and planning tool, Learner support tool;
Analyics for assessment; General analytics tool;
Recommendations; "smart system"; learning enviroment tool;
• Role of analytics – the different uses of analytics: summary
and description; visualisation; statistical inference; modelling;
alerting; prediction; adaptation
• Data sources – where the data originate
Uses own data; Other (vle; mis; social media; statistical services)
• Keywords, Context info, Maturity and Evidence, Further
info…
• …

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Example 1: Inventory of Tools
1.
2.
3.
4.
5.
Specific models
of domain knowledge
(in math) and
on the learner
responses (cognitive
models )
Stand alone
application that
generates its own
data.

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http://cloudworks.ac.uk/cloud/view/9633
1.
2.
3.
4.
5.
6.
Example 2: Inventory of Tools
Data sources include
VLE; social media, “card
swipes” (e.g. using
student card to go to
library), libraries,
housing

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1.
2.
3.
4.
Example 3: Inventory of Tools

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• Tools in Inventory target
• compulsory education (13); HE (8);
• workplace (2); any (6)
• “stand-alone” tools; custom-made solutions; add
on to an existing VLE
• Different data sources:
• Student digital traces from the platform or
outside of it, e.g. interaction data, social media, libraries
• Data from offline sources, e.g. evaluations by the learner,
demographic data, nation-wide test data/evaluations
The Inventory: Tools (1)

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The Inventory: Tools (2)
• Different target beneficiaries of analytics:
• Learners, teachers, tutors, advisors, counsellors, school
heads/managers, policy-makers,..
• Different contexts: e.g. face to face learning in class,
distance learning, blended learning out of school
• Actions on data: scaffold, support, recommend, predict,..
• Action based on based: past behaviour, similarity in
grades, domain knowledge, right answers, statistics, …

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The Inventory: Practices (18)
Cases where learning analytics are being deployed or
developed at scale
• Examples of institutions’ practices at scale (8);
pilots (6); candidate for mainstreaming (2)
• Networks and organisations concerned with
development (LACE, SoLAR, SNOLA, Jisc, Kennisnet);
• Reports on practice and related issues
A template used for describe tools:
• Learning – educational sector to which the practice applies
• Geographical – where the practice is applied
• Pedagogic – theory of teaching and learning that underpins the practice
• Tools used – any relevant tools
• Design and implementation – how the practice developed and is applied

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The Inventory: Policy-related documents
• 14 examples with international focus
• Descriptions available online
A template used for describe tools:
• Document source – where the policy originated
• Geographical – region where the policy applies
• Relationships – areas covered by the policy

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5 Case studies:
1. Developing school sector awareness, knowledge and skills
around learning analytics in the Netherlands (Kennisnet)
2. The process of developing an institutional ethics policy
(The Open University, UK)
3. Learning analytics in the context of a data-intensive
strategy (University of Technology, Sydney)
4. Open-source software and architecture as an option
(The Apereo Foundation Learning Analytics Initiative)
5. Commercial providers of learning analytics critically moving
the whole field forward (Blue Canary)

20. The European Commission’s
science and knowledge service
Joint Research Centre
. Part 1: Introduction to the
Report: aims, inventory of LA
(10min)
. Part 2: Some results of the
study
(10 min)
. Part 3: research challenges
(10 min)
. Part 4: Short discussion
(10 min)
Outline:

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What do we learn from the study (1)?

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2.
1..
Example 4: Inventory of Tools

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What do we learn from the study (1)?
• Evidence of Impact: The research evidence
documented in this study shows that there is little
formal validation of tools
• e.g. whether the tools fulfil their intended purpose
such as having a positive impact on learning;
encouraging more efficient learning; or more
effective learning,..

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LACE Evidence Hub; 37 examples

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LACE Evidence Hub: 32 examples

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What do we learn from the study (2)?

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Examples of Open University, UK
• Tools: Open Essayist and OU Analyse
• Practices: Ethical use of student data policy
• Case study on “The process of developing an institutional ethics
policy ” (part of the Report)
Example 5: Inventory

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What do we learn from the study (2)?
• Impact: The research evidence documented in
this study shows that currently, most impact of
learning analytics in education and training seems
takes place around issues, little impact on
changing practices yet:
• E.g. Sensitive issues of personal data and privacy are
at the centre of discussion

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Europe’s General Data Protection Regulation
(GDPR)
• Europe has taken the position that individual privacy is important and
that changes to current practices in general analytics are needed
• Institutions will need to understand their responsibilities and
obligations with regard to data privacy and data protection and will
have to put procedures in place to ensure that they are compliant with
the legislation.
http://ec.europa.eu/justice/data-protection/reform/

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What do we learn from the study (3)?

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Examples of Australia
Interesting policy documents include a report by the Australian
Government Office for Learning and Teaching on
“improving the quality and productivity of the higher education sector”
• Practices:
Student retention and learning analytics: A snapshot of Australian
practices and a framework for advancement
• Tools: Loop, open source analytics tool to connect with Moodle or
Blackboard (funded by the same gov. body)
• Case study: “Learning analytics in the context of a data-intensive
strategy” - University of Technology, Sydney
Example 6: Inventory

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What do we learn from the study (3)?
• Impact: the implementation of learning analytics
seems to be a long-term process requiring a vision
and a strategy, policy and structure, but also
knowledge and skills in technology and pedagogy
• E.g.
• Case study in UTS (Au): vision of becoming a data-
intensive university in 2011 – strategy and a new centre
in 2014, tools are being developed and piloted now
• Kennisnet working w/schools and vendors since 2014
(products that have useful features!), now focus also on
standardisation of student data, etc.

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What do we learn from the study (4)?

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Narrowing the attainment gap: Georgia State
University At the university, predictive analytics
have been used to tackle the achievement gap for
low income and first-generation students. GSU
graduation rate rose from 32% in 2003 to 54% in
2014.
In the process, the university claims to have
removed the achievement gap between
students from minority backgrounds or lower
socioeconomic status and their peers.
Example 7: Inventory of Practices
Inclusive
education,
equality!

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Strategic objectives for European cooperation
in education and training (ET2020)
1. Relevant and high-quality skills and competences for
employability, innovation, active citizenship
2. Inclusive education, equality, non-discrimination,
civic competences
Indicator: reducing school drop-out rates to less than 10%
3. Open and innovative education and training, including by fully
embracing the digital era
4. Strong support for educators
5. Transparency and recognition of skills and qualifications
6. Sustainable investment, performance and efficiency of education
and training systems

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Is the “school drop-out rate”
the low hanging
fruit of Learning Analytics?
What about the other priorities and
visions for the purpose of LA?

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What do we learn from the study (4)?
• The majority of current learning analytics
work is not strongly aligned with the
European Union’s priority areas for
education and training
• E.g. Strategic objectives for European
cooperation in education and training
(ET2020)

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1. Policy leadership and governance practices
2. Institutional leadership and governance practices
3. Collaboration and networking
4. Teaching and learning practices
5. Quality assessment and assurance practices
6. Capacity building
7. Infrastructure
The Action List for Learning Analytics

40. The European Commission’s
science and knowledge service
Joint Research Centre
. Part 1: Introduction to the
Report: aims, inventory of LA
(10min)
. Part 2: Some results of the
study
(10 min)
. Part 3: research challenges
(10 min)
. Part 4: Short discussion
(10 min)
Outline:

41. 41
Research challenges arising from the
Report
• Challenge 1: Create common vision for LA in
European education and training
• Use the ET2020 Priority areas to make policy hooks
• Challenge 2: Build LA tools that help teachers and
learners
• Now too much focus on the supply side!
• Help generate demand: Talk to teachers and
learners to understand what they want
• Challenge 3: Conduct research that ends up in the
LACE Evidence Hub
• Validation of tools and their promises should lead
the research

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Ready to debate the challenges?
…would you sign up? Are they feasible?
Are they desirable? …
...what do you think, should policy goals
drive the research?

43. The European Commission’s
science and knowledge service
Joint Research Centre
Check the research of our team
at the JRC Science Hub:
https://ec.europa.eu/jrc/
New skills agenda:
https://ec.europa.eu/education/
news/20160610-education-skills-
factsheet_en
Thank you!