1. Data for Learning and
Learning with Data
Mathieu d’Aquin - @mdaquin
Data Science Institute
National University of Ireland Galway
Insight Centre for Data Analytics
AFEL project (@afelproject)
2. Learning
(from a system’s point of view)
Learner
Platform
VLE | Website | Library
Assessment | Enrollment
School/University
3. Learning
(from a system’s point of view)
Learner
Platform
VLE | Website | Library
Assessment | Enrollment
School/University
Edu on
4. Education/Learning
(still from a system’s point of view)
Learner
Platform
VLE | Website | Library
Assessment | Enrollment
School/University
5. Learning
(still from a system’s point of view)
Learner
Platform
VLE | Website | Library
Assessment | Enrollment
School/University
6. Learning
(still from a system’s point of view)
Learner
Platform
VLE | Website | Library
Assessment | Enrollment
School/University
This needs to
evolve to become
more open and
connected
10. Applications - Simple
A very simple map of the buildings of the
Open University….
Built in 2 hours…
Using data from ordnance survey.
b1
b1-addr
ess
Postcode-
mk76aa
name
“Berrill building”
Milton
Keynes
inDistrict
Buckingha
mshire
inCounty
Mk76aa
location
location
lat long
52.024
924
-0.709
726
17. A simple model of education
Person
Learner Author
Topic
Resource
Book
OER
Course
Material
Multimedia
Material
Organisation
Institution
Course
affiliated with
associated
with
created
Teacher
takesregistered
with
expert in
teaches
usesstudies
about
19. A simple(r) model of online education/learning
Person
Learner Author
Topic
Resource
Book
OER
Course
Material
Multimedia
Material
Organisation
Institution
Course
affiliated with
associated
with
created
Teacher
takesregistered
with
expert in
teaches
usesstudies
about
23. A much simpler model of online (possibly
self-directed, possibly informal, possibly incidental)
learning
Person Resource
to learn
about
interested in
Topic
about
uses
contributes tointeracts/colla
borates with
on
relates to
relates to
24. What can be done with data under this model?
Objective: To create theory-backed methods and tools supporting self-directed
learners and the people helping them in making more effective use of online resources,
platforms and networks according to their own goals.
25. Scenario
Jane is 37 and works as an administrative assistant in a local medium-sized company. As a hobbies, she enjoyed sewing and cycling in the local
forests. She is also interested in business management, and is considering either developing in her current job to a more senior level or making a
career change.
Jane spends a lot of time online at home and at her job. She has friends on facebook with whom she shares and discusses local places to go biking,
and others with whom she discusses sewing techniques and possible projects, often through sharing youtube videos.
Jane also follows MOOCs and forums related to business management, on different topics. She often uses online resources such as Wikipedia and
online magazine on the topics. At school, she was not very interested in maths, which is needed if she want to progress in her job. She is therefore
registered on Didactalia, connecting to resources and communities on maths, especially statistics.
Jane has also decided to take her learning seriously: She has registered to use the AFEL dashboard through the Didactalia interface. She has also
installed the browser extension to include her browsing history, as well as the facebook app. She has not included in her dashboard her emails, as
they are mostly related to her current job, or twitter, since she rarely uses it.
Jane looks at the dashboard more or less once a day, as she is prompted by a notification from the AFEL smartphone application or from the
facebook app, to see how she has been doing the previous day in her online social learning. It might for example say “It looks like you progressed
well with sewing yesterday! See how you are doing on other topics…”
Jane, as she looks at the dashboard, realises that she has been focusing a lot on her hobbies and procrastinated on the topics she enjoys less,
especially statistics. Looking specifically at statistics, she realises that she almost only works on it in Friday evenings, because she feels guilty of not
having done much during the week. She also sees that she is not putting as much effort into her learning of statistics as other learners, and not
making as much progress. She therefore makes a conscious decision to put more focus on it. She adds the dashboard goals of the form “to work on
statistics during my lunch break every week day” or “to have achieved a 10% progress compared to now by the same time next week”. The
dashboard will remind her how she is doing against those goals as she go about her usual online social learning activities. She also gets
recommendation of things to do on Didactalia and Facebook based on the indicators shown on the dashboard and her stated goals.
26. Scenario
Jane is 37 and works as an administrative assistant in a local medium-sized company. As a hobbies, she enjoyed sewing and cycling in the local
forests. She is also interested in business management, and is considering either developing in her current job to a more senior level or making a
career change.
Jane spends a lot of time online at home and at her job. She has friends on facebook with whom she shares and discusses local places to go biking,
and others with whom she discusses sewing techniques and possible projects, often through sharing youtube videos.
Jane also follows MOOCs and forums related to business management, on different topics. She often uses online resources such as Wikipedia and
online magazine on the topics. At school, she was not very interested in maths, which is needed if she want to progress in her job. She is therefore
registered on Didactalia, connecting to resources and communities on maths, especially statistics.
Jane has also decided to take her learning seriously: She has registered to use the AFEL dashboard through the Didactalia interface. She has also
installed the browser extension to include her browsing history, as well as the facebook app. She has not included in her dashboard her emails, as
they are mostly related to her current job, or twitter, since she rarely uses it.
Jane looks at the dashboard more or less once a day, as she is prompted by a notification from the AFEL smartphone application or from the
facebook app, to see how she has been doing the previous day in her online social learning. It might for example say “It looks like you progressed
well with sewing yesterday! See how you are doing on other topics…”
Jane, as she looks at the dashboard, realises that she has been focusing a lot on her hobbies and procrastinated on the topics she enjoys less,
especially statistics. Looking specifically at statistics, she realises that she almost only works on it in Friday evenings, because she feels guilty of not
having done much during the week. She also sees that she is not putting as much effort into her learning of statistics as other learners, and not
making as much progress. She therefore makes a conscious decision to put more focus on it. She adds the dashboard goals of the form “to work on
statistics during my lunch break every week day” or “to have achieved a 10% progress compared to now by the same time next week”. The
dashboard will remind her how she is doing against those goals as she go about her usual online social learning activities. She also gets
recommendation of things to do on Didactalia and Facebook based on the indicators shown on the dashboard and her stated goals.
27. Challenges
How do we recognise learning in (the data of) open, generic unconstrained
environments?
How do we measure learning in (the data of) open, generic unconstrained
environments?
28. Cognitive model: Learning and knowledge
construction through co-evolution
The dynamic processes of learning and knowledge construction from
Kimmerle, Moskaliuk, Oeberst, and Cress, 2015.
29. Cognitive model: Learning and knowledge
construction through co-evolution
The dynamic processes of learning and knowledge construction from
Kimmerle, Moskaliuk, Oeberst, and Cress, 2015.
30. Cognitive model: Learning and knowledge
construction through co-evolution
The dynamic processes of learning and knowledge construction from
Kimmerle, Moskaliuk, Oeberst, and Cress, 2015.
“constructive friction is the driving force behind
learning” -- AFEL Deliverable 4.1, [CK08]
31. Identified types of constructive frictions, indicators of
learning (in a given learning scope)
- Coverage: Most obvious indicator. How much of the concepts
covered by the given learning scope (topic) have been covered by
captured learning activities.
- Complexity: How the learner difficult at the resources used by the
learner in exploring this learning scope.
- Diversity: How diverse the resources and activities used by the
learner have been in the given learning scope.
33. Realisation
Data collection: Activity streams from specific platforms (e.g. Didactalia) or using
browser plugin.
Data Enrichment:
- Fine grained semantic topic extraction for resources
- Computing complexity indexes for textual resources
- Using learnt models to estimate gender, age and political orientation of author
of resources
Data processing:
- Clustering to compute learning scopes
- Compute indicators
- Recommendation based on learning scope and indicators
34. Conclusion
Using semantic technologies, large scale data management and data analytics is
driven by new practices in learning, and can help push those practices further.
It can in particular support learners in managing their learning, through
self-tracking of learning activities or goals
Applicable to open or closed environments, fully independent, self-directed
learning, or more formal settings.
AFEL tools (there are also others) looking for early adopters for validation and
participation in their evolution