Conference presenation at Learning With MOOCs III on October 6, 2016. The results are also described in an ACM WebScience 2016 paper: http://dl.acm.org/citation.cfm?doid=2908131.2908145

1. Learner profiling beyond the
MOOC platform
Guanliang Chen, Dan Davis, Jun Lin,
Claudia Hauff, Geert-Jan Houben

2. Whythis research?
Learner
Engagement, retention, …
During the MOOC

3. Whythis research?
Learner
Before the MOOC
NOTHING
Engagement, retention, …
During the MOOC

4. Whythis research?
Learner
Before the MOOC
NOTHING
Engagement, retention, …
During the MOOC
NOTHING
After the MOOC

5. Howto solve the problem?
We propose:
a deeper understanding about learners
can be gained by exploring their traces
on the Social Web.

6. Whatresearch questions?

7. Whatresearch questions?
1
On what Social Web platforms can a significant fraction of
MOOC learners be identified?

8. Whatresearch questions?
1
On what Social Web platforms can a significant fraction of
MOOC learners be identified?
Are learners who demonstrate specific traits
on the Social Web drawn to certain types of MOOCs? 2

9. Whatresearch questions?
1
On what Social Web platforms can a significant fraction of
MOOC learners be identified?
Are learners who demonstrate specific traits
on the Social Web drawn to certain types of MOOCs? 2
To what extent do Social Web platforms enable us to
observe (specific) user attributes
that are relevant to the online learning experience?
3

10. Learner identification
across Social Web platforms
edX learners
Email Login name Full name+ +

11. Learner identification
across Social Web platforms
edX learners
Email Login name Full name+ +
1. Explicit Matching
Profile images & links
Identification via
emails

12. Learner identification
across Social Web platforms
edX learners
Email Login name Full name+ +
1. Explicit Matching
Profile images & links
Identification via
emails
2. Direct Matching
Identification via profile
links from Step 1

13. Learner identification
across Social Web platforms
edX learners
Email Login name Full name+ +
1. Explicit Matching
Profile images & links
Identification via
emails
2. Direct Matching
Identification via profile
links from Step 1
3. Fuzzy Matching
Search learners by their
login & full names
Compare:
1. profile link
2. profile image
3. login & full names

14. Social Web platforms
involved in our work

15. Matching results
for 18 DelftX MOOCs
Lowest Highest Overall
Gravatar 4.37% 23.49% 7.81%
Twitter 4.99% 17.58% 7.78%
Linkedin 3.90% 11.05% 5.89%
StackExchange 1.23% 21.91% 4.58%
GitHub 3.43% 41.93% 10.92%

16. Matching results
for 18 DelftX MOOCs
Lowest Highest Overall
Gravatar 4.37% 23.49% 7.81%
Twitter 4.99% 17.58% 7.78%
Linkedin 3.90% 11.05% 5.89%
StackExchange 1.23% 21.91% 4.58%
GitHub 3.43% 41.93% 10.92%
On average, 5% of learners can be identified on globally
popular Social Web platforms.

17. Learners on
Twitter
- To predict learners’ demographics (e.g., age & gender)

18. Learners on
Linkedin
- Using job titles & skills to characterise learners

19. Learners on
Linkedin
- Using job titles & skills to characterise learners
Data Analysis MOOC
- Software Engineer
- Business Analyst
- …

20. Learners on
Linkedin
- Using job titles & skills to characterise learners
Data Analysis MOOC
- Software Engineer
- Business Analyst
- …
Design Approach MOOC
- Co founder
- UX designer
- …

21. Learners on
Linkedin
- Using job titles & skills to characterise learners
- Visualised by applying t-SNE techniques.

22. Learners on
StackExchange
- Functional
Programming
learners in
StackOverflow
- To what extent do
learners change their
question/answering
behaviour during
and after a MOOC?

23. Learners on
GitHub
- To what extent do learners transfer their acquired
knowledge into practice?
- Functional Programming learners

24. Take-home
Messages
On average, 5% of learners from 18 DelftX MOOCs
can be identified on 5 globally popular Social Web platforms. 1

25. Take-home
Messages
On average, 5% of learners from 18 DelftX MOOCs
can be identified on 5 globally popular Social Web platforms. 1
Learners with specific traits prefer different types of MOOCs.2

26. Take-home
Messages
On average, 5% of learners from 18 DelftX MOOCs
can be identified on 5 globally popular Social Web platforms. 1
Learners with specific traits prefer different types of MOOCs.2
Learners’ post-course behaviour can be investigated
by using their external Social Web traces.3

27. Thank you!
c.hauff@tudelft.nl
http://bit.ly/lambda-lab