Beyond the MOOC platform: Gaining Insights about Learners from the Social Web

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A presentation given in the ACM Web Science conference in May 2016, describing how to profile MOOC learners by mining the Social Web.

Science

Beyond the MOOC platform:
Gaining Insights about Learners
from the Social Web
Guanliang Chen, Dan Davis, Jun Lin,
Claudia Hauff, Geert-Jan Houben
Web Information Systems, TU Delft

Whythis research?
Learner
Engagement, retention, …
During MOOC

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

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

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

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

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

Learner Identiﬁcation
across Social Web platforms
edX learners
Email Login name Full name+ +

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

Social Web platforms
involved in our work

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

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

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

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

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

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

http://bit.ly/wis-learning-analytics
Thanks for your
participation!

With increase in usage of the Internet, there has been an exponential increase in the use of online social media on the Internet. Websites like Facebook, Google+, YouTube, Orkut, Twitter and Flickr have changed the way Internet is being used. There is a dire need to investigate, study and characterize privacy and security on online social media from various perspectives (computational, cultural, psychological). Real world scalable systems need to be built to detect and defend security and privacy issues on online social media. I will describe briefly some cool ongoing projects that we have: Twit-Digest, MultiOSN, Finding Nemo, OCEAN, Privacy in India, and Call Me MayBe. Many of our research work is made available for public use through tools or online services. Our work derives techniques from Data Mining, Text Mining, Statistics, Network Science, Public Policy, Complex networks, Human Computer Interaction, and Psychology. In particular, in this talk, I will focus on the following: (1) Twit-Digest is a tool to extract intelligence from Twitter which can be useful to security analysts. Twit-Digest is backed by award-winning research publications in international and national venues. (2) MultiOSN is a platform to analyze multiple OSM services to gain intelligence on a given topic / event of interest (2) OCEAN: Open source Collation of eGovernment data and Networks Here, we show how publicly available information on Government services can be used to profile citizens in India. This work obtained the Best Poster Award at Security and Privacy Symposium at IIT Kanpur, 2013 and it has gained a lot of traction in Indian media. (3) In Finding Nemo, given an identity in one online social media, we are interested in finding the digital foot print of the user in other social media services, this is also called digital identity stitching problem. This work is also backed by award-winning research publication. I will be more than happy to clarify, discuss, any of our work indetail, as required, after the talk.

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Beyond the MOOC platform: Gaining Insights about Learners from the Social Web

1. Beyond the MOOC platform: Gaining Insights about Learners from the Social Web Guanliang Chen, Dan Davis, Jun Lin, Claudia Hauff, Geert-Jan Houben Web Information Systems, TU Delft

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

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

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

5. Howto solve the problem? We propose: a deeper understanding about learners can be gained by exploring their traces in 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 sets of 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 sets of 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 highly 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. 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 namesMATCH !

15. Social Web platforms involved in our work

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%

17. 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.  

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

19. Learners on Linkedin - Using skills to characterise learners - Visualised by applying t-SNE techniques.

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

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

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

23. 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

24. 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

25. http://bit.ly/wis-learning-analytics Thanks for your participation!

Beyond the MOOC platform: Gaining Insights about Learners from the Social Web

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