Presentation at The Web Conference (WWW 2018) international project track.

1. The AFEL Project
Mathieu d’Aquin | @mdaquin | @afelproject
Insight Centre for Data Analytics
National University of Ireland, Galway
Angela Fessl, Dominik Kowald, Elisabeth Lex and Stefan Thalmann
Know Center, Graz, Austria
The AFEL Consortium

2. Analytics for Everyday Learning

3. Analytics for Everyday Learning
=
Learning Analytics for online, social,
self-directed learning

4. Learning Analytics
According to Wikipedia (and past LAK CFPs, and some papers from relevant people)
Learning analytics is 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.

5. Learning Analytics
According to Wikipedia (and past LAK CFPs, and some papers from relevant people)
Learning analytics is 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.

6. Learning Analytics
According to Wikipedia (and past LAK CFPs, and some papers from relevant people)
Learning analytics is 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.
not only students
not only the classroom,
university, library or VLE

7. Learner
Platform
Analytics
VLE | Website | Library
Assessment | Enrollment
School/University
Prediction Drop out
BI
Planning
Recommendation
However, typically, Learning Analytics is...
A university uses data on the
students and their activities
collected through the
institution's information
systems with the goal to
predict their success so they
can improve help them
improve….
… and improve their teaching,
offering, environments as well

8. But: Everyday Learning
Learner
Platform
Analytics
VLE | Website | Library
Assessment | Enrollment
School/University
Prediction Drop out
BI
Planning
Recommendation

9. But: Everyday Learning
Learner
Platform
Analytics
VLE | Website | Library
Assessment | Enrollment
School/University
Prediction Drop out
BI
Planning
Recommendation

10. Learner
Platform
Analytics
VLE | Website | Library
Assessment | Enrollment
School/University
Prediction Drop out
BI
Planning
Recommendation
Sentiment Analysis
Collective Intelligence Behaviour Analysis
Collaboration
Community Support
But: Everyday Learning

11. 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.

12. as a testbed
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.
Cognitive Theories
of Learning
Technology
research
Technical
developm
ent

13. Guided by scenarios
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.

14. Guided by scenarios
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.

15. learner
browser
Challenge #1: Collecting data

16. Difficulté #1: D'où viennent les données
learner
activités générant des traces et
(des fois, différents) identifiants
browser
AFEL Data Platform
Extension
app
Tracker
Crawler
Crawler
Traces and
metadata
AFEL identifier
and local
identifier
AFEL identifier
and local
identifier

17. learner
activités générant des traces et
(des fois, différents) identifiants
browser
AFEL Data Platform
Analytics platform
VisualisationAFEL identifier
Analysis
Integrated
personal data
Extension
app
Tracker
Crawler
Crawler
AFEL Core Data
Model (based on
schema.org)
Learning
indicators
Traces and
metadata
AFEL identifier
and local
identifier
AFEL identifier
and local
identifier
Challenge #1: Collecting data

18. Example : Browser Extension
Same model used
for Facebook
application, Twitter,
Didactalia analytics..

19. Challenge #2: Indicators of learning?
Maximising what? Minimising what?
teacher analyst
Ratio :
students’ success
cost in effort/resources
(?)
learner
In the context of informal,
self-directed learning, what
is success?
What are the relevant
notions of effort and cost?

20. The dynamic processes of learning and knowledge construction from
Kimmerle, Moskaliuk, Oeberst, and Cress, 2015.
Understanding learning where it can’t be measured

21. Understanding learning where it can’t be measured
The dynamic processes of learning and knowledge construction from
Kimmerle, Moskaliuk, Oeberst, and Cress, 2015.

22. 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]
Understanding learning where it can’t be measured

23. Indicators of learning, based on measuring friction!
So to favour activities that generate a constructing fiction..
What kinds of frictions?
- In topic: In what way the activity introduces topics/themes/concepts
that have not been seen before?
- In complexity: In what way the activity introduces a further level of
complexity which was not accessible before and requires further
efforts.
- In view: In what way the activity introduces new points of view on an
already encountered topic, covering a different aspect.

24. Example - Analysing topic coverage from traces in web
browsing
Text analysis Clustering
Progress
analysis
Browser history
Learning scopes
(topics)

25. Example - Analysing topic coverage from traces in web
browsing

26. Example - Analysing topic coverage from traces in web
browsing

27. Example - Analysing topic coverage from traces in web
browsing

28. web programming
british isles
Example - Analysing topic coverage from traces in web
browsing

29. Overview of the technology in AFEL
AFEL Data Platform
Storage Catalog APIs
Data
Extractor
Data
Extractor
Data
Extractor
Recommender
services
Indicator
services
Data enrich. & feature extraction
Visualisation
GNOSS Tools
Other platforms
Tools

30. Overview
AFEL Data
Platform
InputAPIs
OutputAPIs
Target
platform
AFEL
Mobile
app
AFEL
Visual
Analytics
AFEL
Rec.
Services
enriched activity
data and
indicators
enriched activity
data and
indicators
recommendations
activity data
resource
text and
metatada
resources
and
activities

31. Overview
AFEL Data
Platform
InputAPIs
OutputAPIs
AFEL
Mobile
app
AFEL
Visual
Analytics
AFEL
Rec.
Services
enriched activity
data and
indicators
enriched activity
data and
indicators
recommendations
activity data
resource
text and
metatada
resources
and
activities

32. Overview
AFEL Data
Platform
InputAPIs
OutputAPIs
AFEL
Mobile
app
AFEL
Visual
Analytics
AFEL
Rec.
Services
enriched activity
data and
indicators
enriched activity
data and
indicators
recommendations
activity data
resource
text and
metatada
resources
and
activities

33. Results: Advanced Visual Analytics Framework for
Everyday Learning Analytics

34. A customisable mobile application for the self-directed
learner

35. A customisable mobile application for the self-directed
learner

36. Conclusion
The Objective of the project…
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.
… is being accomplished.
The basic theories, data collection, data
refinement, data visualisation and integration
mechanisms are in place.
Many other activities, including work on Data
Ethics, echo chambers, author profiling not
detailed here.

37. Examples of other useful outcomes
Public data release Learning Analytics
Glossary

38. Conclusion
And of course, we are open to requests
to test our tools on other platforms and
for collaborations, and welcome early
adopters!

39. http://afel-project.eu - @afelproject