1. The document discusses using social web data to better understand and engage MOOC learners. It identifies learners on platforms like LinkedIn, Twitter, and GitHub to analyze traits and learning transfer. 2. On average, 5% of learners from 18 MOOCs could be identified across social platforms. Learners with certain traits preferred different MOOC types. Engaged learners exhibited more learning transfer. 3. The document proposes enabling learners to apply skills and earn money by connecting MOOCs to freelance platforms. Learners successfully solved real-world tasks, and earnings improved engagement. Overall, social web data provides insights that can enhance the learning experience.

1. Understanding and Engaging
MOOC Learners:
A Data-driven Approach
Guanliang Chen
Web Information Systems, TU Delft
https://angusglchen.github.io/

2. Background

3. Background
Learning data

4. Background
KnowledgeLearning data

5. Background
Knowledge
Application
to learning
Learning data

6. Background
Knowledge
Application
to learning
Learning data

7. Background
Knowledge
Application
to learning
Learning data
Social Web
data

8. Background
Knowledge
Application
to learning
Learning data
Social Web
data
+

9. Background
Knowledge
Application
to learning
Learning data
Social Web
data
+
How can the Social Web
data be utilised to better
understand and engage
our MOOC learners?

10. Learner Profiling Beyond the
MOOC Platform
ACM WebScience 2016
&
ACM Learning at Scale 2016
(Best Paper Nominee)
Guanliang Chen, Dan Davis, Jun Lin, Claudia Hauff, and Geert-Jan Houben. Beyond the MOOC
platform: Gaining Insights about Learners from the Social Web, ACM WebScience, pp. 15-24, 2016.
Guanliang Chen, Dan Davis, Claudia Hauff and Geert-Jan Houben, Learning Transfer: does it take
place in MOOCs?, ACM Learning At Scale, pp. 409-418, 2016.

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

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

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

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

15. Whatresearch questions?

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

17. 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

18. 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

19. Learner Identification
across Social Web platforms

20. Learner Identification
across Social Web platforms
Email
Login name
Full name

21. Learner Identification
across Social Web platforms
1) Explicit discovery
via emails
Email
Login name
Full name

22. Learner Identification
across Social Web platforms
1) Explicit discovery
via emails
Email
Login name
Full name
Profile
pictures
Profile links

23. Learner Identification
across Social Web platforms
1) Explicit discovery
via emails
2) Search
Email
Login name
Full name
Profile
pictures
Profile links

24. Learner Identification
across Social Web platforms
1) Explicit discovery
via emails
2) Search
Email
Login name
Full name
Profile
pictures
Profile links
Compare:
1. Profile link
2. Profile pictures
3. Login & Full
names

25. Learner Identification
across Social Web platforms
1) Explicit discovery
via emails
2) Search
Email
Login name
Full name
Profile
pictures
Profile links
Compare:
1. Profile link
2. Profile pictures
3. Login & Full
names
MATCH !

26. Social Web platforms
involved in our work

27. Matching Results
for 18 DelftX MOOCs

28. 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%

29. 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.

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

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

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

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

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

35. Learners on
Twitter
- To gather social relation information
Framing MOOC Functional Programming

36. Learners on
Twitter
WITH friends WITHOUT friends
# Learners 637 1292
Completion rate 28,57% 23,99%
Avg. time watching
videos
116.61 min 119.5 min
Avg. # questions
attempted
117.7 102.9
Avg. #posts per
learner
0.96 0.76

37. Learners on
Twitter
WITH friends WITHOUT friends
# Learners 637 1292
Completion rate 28,57% 23,99%
Avg. time watching
videos
116.61 min 119.5 min
Avg. # questions
attempted
117.7 102.9
Avg. #posts per
learner
0.96 0.76
MORE
ENGAGING

38. Learners on
GitHub
- To what extent do learners transfer their acquired
knowledge into practice?
- Learning transfer is the application of knowledge
or skills gained in a learning environment to
another context.
- A more important measure of learning in MOOCs
than retention, engagement, or completion rate.

39. Learners on
GitHub
3 months 2.5 years + 0.5 years
+ +FP101x
logs
Surveys
Coding
data

40. Learners on
GitHub
3 months 2.5 years + 0.5 years
+ +FP101x
logs
Surveys
Coding
data
Are changes made in a
functional language?

41. Learners on
GitHub- Are “Github learners” different?
GitHub learners Non-GitHub learners
# Learners 12,415 25,070
Completion rate 7.71% 4.03%
Avg. time watching
videos
49.1 min 27.7 min
Avg. # questions
attempted
31.3 17.5
Avg. accuracy of
learners’ answers
23.4% 12.9%

42. Learners on
GitHub- Are “Github learners” different?
GitHub learners Non-GitHub learners
# Learners 12,415 25,070
Completion rate 7.71% 4.03%
Avg. time watching
videos
49.1 min 27.7 min
Avg. # questions
attempted
31.3 17.5
Avg. accuracy of
learners’ answers
23.4% 12.9%
MORE
ENGAGING

43. Learners on
GitHub- Are “Expert learners” different?
Expert GitHub
learners
Novice GitHub
learners
# Learners 1,721 10,694
Completion rate 15.0% 6.5%
Avg. time watching
videos
78.6 min 44.4 min
Avg. # questions
attempted
57.9 27.0
Avg. accuracy of
learners’ answers
38.0% 21.1%

44. Learners on
GitHub- Are “Expert learners” different?
Expert GitHub
learners
Novice GitHub
learners
# Learners 1,721 10,694
Completion rate 15.0% 6.5%
Avg. time watching
videos
78.6 min 44.4 min
Avg. # questions
attempted
57.9 27.0
Avg. accuracy of
learners’ answers
38.0% 21.1%
MORE
ENGAGING

45. To what extent do engaged learners
exhibit learning transfer?
>30%10-30%<5% 5-10%

46. Which type of learner is more likely
to display learning transfer?
Intrinsically motivated Extrinsically motivated

47. Which type of learner is more likely
to display learning transfer?
Intrinsically motivated

48. Experienced Inexperienced
Which type of learner is more likely
to display learning transfer?

49. Experienced
Which type of learner is more likely
to display learning transfer?

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

51. 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

52. 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

53. From Learners to Earners:
Enabling MOOC Learners to Apply
their Skills and Earn Money in an
Online Market Place
IEEE Transactions on Learning
Technologies
Guanliang Chen, Dan Davis, Markus Krause, Efthimia Aivaloglou, Claudia Hauff and Geert-Jan
Houben. Can Learners be Earners? Investigating a Design to Enable MOOC Learners to Apply their
Skills and Earn Money in an Online Market Place, IEEE Transactions on Learning Technologies.

54. Whatis the problem?
EX101x: Data Analysis to the MAX()

55. Whatis the problem?
EX101x: Data Analysis to the MAX()
Most successful learners are already highly
educated. Learners from developing countries
are underrepresented.

56. Whatis the problem?
EX101x: Data Analysis to the MAX()
Most successful learners are already highly
educated. Learners from developing countries
are underrepresented.
But, MOOCs aim to educate the world!

57. Howto solve the problem?
We propose:
learners can be paid to take MOOCs.

58. Howto solve the problem?

59. Howto solve the problem?
Learners MOOCs
take

60. Howto solve the problem?
Learners MOOCs
take
Freelance
Platforms
connect

61. Howto solve the problem?
Learners MOOCs
take
Freelance
Platforms
connect
solve

62. Howto solve the problem?
Learners MOOCs
take
Freelance
Platforms
connect
solve
pay

63. Setup
1
2
3
Weekly spreadsheet “bonus exercises”
drawn from UpWork (manually checked) in
EX101x.
Accuracy check.
Quality check (code smells).

64. Howare learners doing?

65. Howare learners doing?
Learners can solve real-world tasks in good quality.

66. Howare learners doing?
Learners can solve real-world tasks in good quality.
Real-world tasks improve learners’ engagement.

67. Recommender
System
We have built a
working
recommender
and deployed in
a MOOC for
experiment.

68. Overall
Knowledge
Application
to learning
Learning data
Social Web
data
+

69. Overall
Knowledge
Application
to learning
Learning data
Social Web
data
+

70. Overall
Knowledge
Application
to learning
Learning data
Social Web
data
+
To better understand our
learners & To better
engage our learners.

71. Overall
Knowledge
Application
to learning
Learning data
Social Web
data
+
To better understand our
learners & To better
engage our learners.
Many learners/courses,
plenty of data, lots of
potential unexplored.

72. Thank you for your
participation!
http://bit.ly/lambda-lab