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Claudia Hauff
Joint work with Dan Davis, Guanliang Chen, Markus Krause,
Efthimia Aivaloglou and Geert-Jan Houben
Analysing...
✤ 60+ MOOCs
✤ 1.5 million enrollments
✤ From primary school to PhD level
✤ Lots of user data (click logs)
Our goals
Data
Knowledge
Application
to learning
Gain actionable insights into learner
behaviours at scale.
a. Data Scienc...
Learner profiling 

beyond the MOOC platform
Learning transfer: 

does it take place?
From learners to 

earners
Learning p...
Learner profiling beyond
the MOOC platform
ACM WebScience 2016
Guanliang Chen, Dan Davis, Jun Lin, Claudia Hauff, and Geert...
Whythis research?
Learner
Engagement, retention, …
During the MOOC
Whythis research?
Learner
Before the MOOC
NOTHING
Engagement, retention, …
During the MOOC
Whythis research?
Learner
Before the MOOC
NOTHING
Engagement, retention, …
During the MOOC
NOTHING
After the MOOC
Howto solve the problem?
We propose:
a deeper understanding about learners
can be gained by exploring their traces
on the ...
Whatresearch questions?
1
On what Social Web platforms can a significant fraction of
MOOC learners be identified? 

Are lear...
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 i...
Learner identification
across Social Web platforms
edX learners
Email Login name Full name+ +
1. Explicit Matching
Profile i...
Learner identification
across Social Web platforms
edX learners
Email Login name Full name+ +
1. Explicit Matching
Profile i...
Social Web platforms
involved in our work
Matching results
for 18 DelftX MOOCs
Lowest Highest Overall
Gravatar 4.37% 23.49% 7.81%
Twitter 4.99% 17.58% 7.78%
Linkedi...
Matching results
for 18 DelftX MOOCs
Lowest Highest Overall
Gravatar 4.37% 23.49% 7.81%
Twitter 4.99% 17.58% 7.78%
Linkedi...
Learners on
Linkedin
- Using job titles & skills to characterise learners
Learners on
Linkedin
- Using job titles & skills to characterise learners
Spreadsheet MOOC
- Software Engineer
- Business ...
Learners on
Linkedin
- Using job titles & skills to characterise learners
Spreadsheet MOOC
- Software Engineer
- Business ...
Learners on
StackExchange
- Functional
Programming
learners in
StackOverflow
- To what extent do
learners change their
ques...
Take-home
Messages
On average, 5% of learners from 18 DelftX MOOCs
can be identified on 5 globally popular Social Web platf...
Learning Transfer:
does it take place?
Best Paper Nominee at
ACM Learning At Scale 2016
An Investigation into the Uptake o...
Whatis learning transfer?
Learning transfer is the application 

of knowledge or skills gained in a
learning environment t...
Whydo we care?
Learning transfer is a more important
measure of learning in MOOCs than
retention, success or engagement.
FP101x
@flickr:christiaan_008
Course programming language: Haskell
Run as a typical video-lecture based MOOC
Assessment: 28...
FP101x
@flickr:christiaan_008
Course programming language: Haskell
Run as a typical video-lecture based MOOC
Assessment: 28...
Whatdid we do?
FP101x
logs
surveys
coding
activities
3 months 2.5 years + 0.5 years
+ +
email address
Whatdid we do?
FP101x
logs
surveys
coding
activities
3 months 2.5 years + 0.5 years
+ +
email address
Are changes made in ...
GitHub
hosting, collaboration and organisation
the most popular social coding platform
GitHub
hosting, collaboration and organisation
the most popular social coding platform
detailed
GitHub
hosting, collaboration and organisation
the most popular social coding platform
founded in 2007
detailed
GitHub
hosting, collaboration and organisation
the most popular social coding platform
founded in 2007
long-term
detailed
GitHub
10+ million registered users
hosting, collaboration and organisation
the most popular social coding platform
founde...
GitHub
10+ million registered users
hosting, collaboration and organisation
the most popular social coding platform
founde...
detailed logs
code changes
project meta-data
A sanity check
Are “GitHub learners” different?
GitHub
learners
Non-GitHub
learners
#Learners 12,415 25,070
Completion rate 7.71% 4.03%
A...
Are “GitHub learners” different?
GitHub
learners
Non-GitHub
learners
#Learners 12,415 25,070
Completion rate 7.71% 4.03%
A...
Are “Expert learners” different?
Expert GitHub
learners
Novice GitHub
learners
#Learners 1,721 10,694
Completion rate 15% ...
Are “Expert learners” different?
Expert GitHub
learners
Novice GitHub
learners
#Learners 1,721 10,694
Completion rate 15% ...
To what extent do engaged learners
exhibit learning transfer?
5-10% >30%10-30%<5%
To what extent do engaged learners
exhibit learning transfer?
5-10%
Which type of learner is more likely
to display learning transfer?
flickr@ConalGallagher
Intrinsically motivated Extrinsica...
Which type of learner is more likely
to display learning transfer?
flickr@ConalGallagher
Intrinsically motivated
Which type of learner is more likely
to display learning transfer?
Experienced Inexperienced
Which type of learner is more likely
to display learning transfer?
Experienced
Which type of learner is more likely
to display learning transfer?
High-spacing

learning routine
Low-spacing

learning ro...
Which type of learner is more likely
to display learning transfer?
High-spacing

learning routine
Learners who transfer
quickly move to Scala
FP101x
Conclusions
Most transfer learning findings from the classroom hold.
The observed transfer rate is low: 8.5%.
Learners quic...
From Learners to Earners:
Enabling MOOC Learners to Apply
their Skills and Earn Money in an
Online Market Place
IEEE Trans...
What
MOOCs aim to educate the world.
Most successful learners are already
highly educated.
Learners from developing countr...
Whatis the problem?
EX101x: Data Analysis to the MAX()
How
Pay learner at scale: recommend tasks
from online market places to learners
that are relevant to the course material.
...
How
Pay learner at scale: recommend tasks
from online market places to learners
that are relevant to the course material.
...
Howcan we tackle it?
What
1) To what extent do online market 

places contain relevant tasks?
2) Are learners able to solve 

real-world tasks ...
Setup
1) Weekly spreadsheet “bonus 

exercises” drawn from Upwork 

(manually checked) in EX101x
2) Accuracy check
3) Qual...
Howare learners doing?
Good accuracy & quality.
Built a working
recommender.
Gauging MOOC learners’
adherence to the
designed learning path
Educational Data Mining 2016
Dan Davis, Guanliang Chen, Cla...
WHAT IS A LEARNING
PATH ?
THE SEQUENCE OF EVENTS A STUDENT
TAKES TOWARDS A LEARNING OBJECTIVE
RQ
To what extent do learners
adhere to the designed
learning path?
VIDEO QUIZ PROGRESS FORUM
WATCH START VIEW START
SUBMIT
END
SUBMIT
END
Frame101x
VIDEO
QUIZ
END
FORUM
SUBMIT
FORUM
END
PROGRESS
QUIZ
START
FORUM
START
QUIZ
SUBMIT
3
3
4
6
36
VIDEO
QUIZ
END
FORU...
MOOC ENROLLED PASS CHAINS
PASS/NON
QUIZ
QUESTIONSRATE
TRIES VIDEOS
ACCESSED
FP101x
RI101x
Frame101x
EX101x
37k
9k
34k
33k
...
APPROACH
1. VIDEO INTERACTIONS
2. BEHAVIOR PATTERN CHAINS
3. EVENT TYPE TRANSITIONS
VIDEO INTERACTIONS
Non-Passing
Passing
Week 1 Week 2
Week 3
FP101x
Designed Lecture Order
Executed Paths
2. BEHAVIOR
PATTERN
CHAINSWHAT ARE THE MOST COMMON SEQUENCES 

IN THE EXECUTED LEARNING PATHS?
FORUMEND
EIGHT-STEP CHAIN
EXAMPLE
LECTUREWATCH
QUIZSTART
QUIZSUBMIT
QUIZSUBMIT
QUIZEND
PROGRESS
LECTUREWATCH
MOOC ENROLLED PASS CHAINS
PASS/NON
QUIZ
QUESTIONSRATE
TRIES VIDEOS
ACCESSED
FP101x
RI101x
Frame101x
EX101x
37k
9k
34k
33k
...
OPEN CARD
SORTING
OPEN CARD
SORTING
MOTIF
A RECURRING OR
REPEATED ELEMENT
MOTIF FREQ
TOTAL
FREQ
PASS
FREQ
FAIL
QUIZ COMPLETE 552,363
(29.4%)
328,995
(30.8%)
223,368
(27.7%)
1.
BINGE WATCHING 149,7...
3. EVENT
TYPE
TRANSITIONSHOW DO LEARNERS NAVIGATE BETWEEN 

CERTAIN EVENT TYPES?
EVENT TYPE TRANSITIONS
Frame101x
Non-
Passing
Frame101x
Passing
(EXECUTED)
Ongoing work
Data
Knowledge
Application
to learning
Gain actionable insights into learner
behaviours at scale.
a. Data Sci...
MOOCs are vital to bring higher
education to the world.
Lots of unexplored potential.
Plenty of data.
Many users.
http://b...
VII Jornadas eMadrid "Education in exponential times". "Analysing and Altering MOOC Learners' Behaviours at Scale". Claudi...
VII Jornadas eMadrid "Education in exponential times". "Analysing and Altering MOOC Learners' Behaviours at Scale". Claudi...
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VII Jornadas eMadrid "Education in exponential times". "Analysing and Altering MOOC Learners' Behaviours at Scale". Claudia Hauff. TU Delft, Países Bajos. 03/07/2017.

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VII Jornadas eMadrid "Education in exponential times". "Analysing and Altering MOOC Learners' Behaviours at Scale". Claudia Hauff. TU Delft, Países Bajos. 03/07/2017.

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VII Jornadas eMadrid "Education in exponential times". "Analysing and Altering MOOC Learners' Behaviours at Scale". Claudia Hauff. TU Delft, Países Bajos. 03/07/2017.

  1. 1. Claudia Hauff Joint work with Dan Davis, Guanliang Chen, Markus Krause, Efthimia Aivaloglou and Geert-Jan Houben Analysing and altering MOOC learners’ behaviours at scale
  2. 2. ✤ 60+ MOOCs ✤ 1.5 million enrollments ✤ From primary school to PhD level ✤ Lots of user data (click logs)
  3. 3. Our goals Data Knowledge Application to learning Gain actionable insights into learner behaviours at scale. a. Data Science b. Big data processing Increase our knowledge about learners by looking beyond the learning platform a. Web data analytics Design technology interventions that enable adaptive learning at scale. a. Information retrieval b. Human-centred design c. Learning technologies
  4. 4. Learner profiling 
 beyond the MOOC platform Learning transfer: 
 does it take place? From learners to 
 earners Learning paths: desired vs. executed flickr@herrolsen
  5. 5. Learner profiling beyond the MOOC platform ACM WebScience 2016 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.
  6. 6. Whythis research? Learner Engagement, retention, … During the MOOC
  7. 7. Whythis research? Learner Before the MOOC NOTHING Engagement, retention, … During the MOOC
  8. 8. Whythis research? Learner Before the MOOC NOTHING Engagement, retention, … During the MOOC NOTHING After the MOOC
  9. 9. Howto solve the problem? We propose: a deeper understanding about learners can be gained by exploring their traces on the Social Web.
  10. 10. 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
  11. 11. Learner identification across Social Web platforms edX learners Email Login name Full name+ +
  12. 12. Learner identification across Social Web platforms edX learners Email Login name Full name+ + 1. Explicit Matching Profile images & links Identification via emails
  13. 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
  14. 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 names
  15. 15. Social Web platforms involved in our work
  16. 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. 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. 18. Learners on Linkedin - Using job titles & skills to characterise learners
  19. 19. Learners on Linkedin - Using job titles & skills to characterise learners Spreadsheet MOOC - Software Engineer - Business Analyst - …
  20. 20. Learners on Linkedin - Using job titles & skills to characterise learners Spreadsheet MOOC - Software Engineer - Business Analyst - … Design Approach MOOC - Co founder - UX designer - …
  21. 21. Learners on StackExchange - Functional Programming learners in StackOverflow - To what extent do learners change their question/answering behaviour during and after a MOOC?
  22. 22. 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
  23. 23. Learning Transfer: does it take place? Best Paper Nominee at ACM Learning At Scale 2016 An Investigation into the Uptake of Functional Programming in Practice 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.
  24. 24. Whatis learning transfer? Learning transfer is the application 
 of knowledge or skills gained in a learning environment to another context.
  25. 25. Whydo we care? Learning transfer is a more important measure of learning in MOOCs than retention, success or engagement.
  26. 26. FP101x @flickr:christiaan_008 Course programming language: Haskell Run as a typical video-lecture based MOOC Assessment: 288 Multiple Choice questions Introduction to Functional Programming
  27. 27. FP101x @flickr:christiaan_008 Course programming language: Haskell Run as a typical video-lecture based MOOC Assessment: 288 Multiple Choice questions Introduction to Functional Programming 37,485 learners registered. 41% engaged with the course. 5% completed the course. 33% were active on GitHub (1.1M events).
  28. 28. Whatdid we do? FP101x logs surveys coding activities 3 months 2.5 years + 0.5 years + + email address
  29. 29. Whatdid we do? FP101x logs surveys coding activities 3 months 2.5 years + 0.5 years + + email address Are changes made in a functional language?
  30. 30. GitHub hosting, collaboration and organisation the most popular social coding platform
  31. 31. GitHub hosting, collaboration and organisation the most popular social coding platform detailed
  32. 32. GitHub hosting, collaboration and organisation the most popular social coding platform founded in 2007 detailed
  33. 33. GitHub hosting, collaboration and organisation the most popular social coding platform founded in 2007 long-term detailed
  34. 34. GitHub 10+ million registered users hosting, collaboration and organisation the most popular social coding platform founded in 2007 long-term detailed
  35. 35. GitHub 10+ million registered users hosting, collaboration and organisation the most popular social coding platform founded in 2007 long-term large-scale detailed
  36. 36. detailed logs code changes project meta-data
  37. 37. A sanity check
  38. 38. 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%
  39. 39. 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% GitHub learners are more engaged than non-GitHub learners and exhibit higher levels of knowledge.
  40. 40. Are “Expert learners” different? Expert GitHub learners Novice GitHub learners #Learners 1,721 10,694 Completion rate 15% 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%
  41. 41. Are “Expert learners” different? Expert GitHub learners Novice GitHub learners #Learners 1,721 10,694 Completion rate 15% 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% Expert learners are more engaged than Novice learners and exhibit higher levels of knowledge.
  42. 42. To what extent do engaged learners exhibit learning transfer? 5-10% >30%10-30%<5%
  43. 43. To what extent do engaged learners exhibit learning transfer? 5-10%
  44. 44. Which type of learner is more likely to display learning transfer? flickr@ConalGallagher Intrinsically motivated Extrinsically motivated
  45. 45. Which type of learner is more likely to display learning transfer? flickr@ConalGallagher Intrinsically motivated
  46. 46. Which type of learner is more likely to display learning transfer? Experienced Inexperienced
  47. 47. Which type of learner is more likely to display learning transfer? Experienced
  48. 48. Which type of learner is more likely to display learning transfer? High-spacing
 learning routine Low-spacing
 learning routine
  49. 49. Which type of learner is more likely to display learning transfer? High-spacing
 learning routine
  50. 50. Learners who transfer quickly move to Scala FP101x
  51. 51. Conclusions Most transfer learning findings from the classroom hold. The observed transfer rate is low: 8.5%. Learners quickly moved on after the course to industrially-relevant functional languages. @flickr:torsten-reuschling
  52. 52. 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.
  53. 53. What MOOCs aim to educate the world. Most successful learners are already highly educated. Learners from developing countries 
 are underrepresented. is the problem?
  54. 54. Whatis the problem? EX101x: Data Analysis to the MAX()
  55. 55. How Pay learner at scale: recommend tasks from online market places to learners that are relevant to the course material. can we tackle it?
  56. 56. How Pay learner at scale: recommend tasks from online market places to learners that are relevant to the course material. can we tackle it?
  57. 57. Howcan we tackle it?
  58. 58. What 1) To what extent do online market 
 places contain relevant tasks? 2) Are learners able to solve 
 real-world tasks with high quality? do we need to look at?
  59. 59. Setup 1) Weekly spreadsheet “bonus 
 exercises” drawn from Upwork 
 (manually checked) in EX101x 2) Accuracy check 3) Quality check (code smells)
  60. 60. Howare learners doing? Good accuracy & quality.
  61. 61. Built a working recommender.
  62. 62. Gauging MOOC learners’ adherence to the designed learning path Educational Data Mining 2016 Dan Davis, Guanliang Chen, Claudia Hauff and Geert-Jan Houben. Gauging MOOC Learners’ Adherence to the Designed Learning Path, 9th International Conference on Educational Data Mining, pp. 54-61, 2016.
  63. 63. WHAT IS A LEARNING PATH ? THE SEQUENCE OF EVENTS A STUDENT TAKES TOWARDS A LEARNING OBJECTIVE
  64. 64. RQ To what extent do learners adhere to the designed learning path?
  65. 65. VIDEO QUIZ PROGRESS FORUM WATCH START VIEW START SUBMIT END SUBMIT END
  66. 66. Frame101x VIDEO QUIZ END FORUM SUBMIT FORUM END PROGRESS QUIZ START FORUM START QUIZ SUBMIT 3 3 4 6 36 VIDEO QUIZ END FORUM SUBMIT FORUM END PROGRESS QUIZ START FORUM START QUIZ SUBMIT 1 1 1 121 VIDEO QUIZ END FORUM SUBMIT FORUM END PROGRESS QUIZ START FORUM START QUIZ SUBMIT 3 7 7 7 6 VIDEO QUIZ END FORUM SUBMIT FORUM END PROGRESS QUIZ START FORUM START QUIZ SUBMIT 5 1 1 1 1 1 1 101 FP101x RI101x EX101x
  67. 67. MOOC ENROLLED PASS CHAINS PASS/NON QUIZ QUESTIONSRATE TRIES VIDEOS ACCESSED FP101x RI101x Frame101x EX101x 37k 9k 34k 33k 5.3% 4.3% 2.4% 6.5% 1.06M/
 807K 66k/
 30k 95k/
 141k 1.02M/ 855k 288 75 26 136 1 1-3 2 2 67.5% 49.7% 51.0% 45.0%
  68. 68. APPROACH 1. VIDEO INTERACTIONS 2. BEHAVIOR PATTERN CHAINS 3. EVENT TYPE TRANSITIONS
  69. 69. VIDEO INTERACTIONS Non-Passing Passing Week 1 Week 2 Week 3 FP101x Designed Lecture Order Executed Paths
  70. 70. 2. BEHAVIOR PATTERN CHAINSWHAT ARE THE MOST COMMON SEQUENCES 
 IN THE EXECUTED LEARNING PATHS?
  71. 71. FORUMEND EIGHT-STEP CHAIN EXAMPLE LECTUREWATCH QUIZSTART QUIZSUBMIT QUIZSUBMIT QUIZEND PROGRESS LECTUREWATCH
  72. 72. MOOC ENROLLED PASS CHAINS PASS/NON QUIZ QUESTIONSRATE TRIES VIDEOS ACCESSED FP101x RI101x Frame101x EX101x 37k 9k 34k 33k 5.3% 4.3% 2.4% 6.5% 1.06M/
 807K 66k/
 30k 95k/
 141k 1.02M/ 855k 288 75 26 136 1 1-3 2 2 67.5% 49.7% 51.0% 45.0%
  73. 73. OPEN CARD SORTING OPEN CARD SORTING
  74. 74. MOTIF A RECURRING OR REPEATED ELEMENT
  75. 75. MOTIF FREQ TOTAL FREQ PASS FREQ FAIL QUIZ COMPLETE 552,363 (29.4%) 328,995 (30.8%) 223,368 (27.7%) 1. BINGE WATCHING 149,784 (8.0%) 59,498 (5.6%) 90,286 (11.2%) 2. LECTURE -> QUIZ COMPLETE 100,179 (5.3%) 50,415 (4.7%) 49,764 (6.2%) 3. QUIZ COMPLETE -> FORUM 99,828 (5.3%) 67,722 (6.3%) 32,106 (4.0%) 4. FP101x XQUIZ events only with at least 1 X = SUBMIT WATCH events only WATCH event(s) followed by XQUIZ 
 events w/ >1 X = SUBMIT XQUIZ events with >1 X = SUBMIT followed 
 by XFORUM events
  76. 76. 3. EVENT TYPE TRANSITIONSHOW DO LEARNERS NAVIGATE BETWEEN 
 CERTAIN EVENT TYPES?
  77. 77. EVENT TYPE TRANSITIONS Frame101x Non- Passing Frame101x Passing (EXECUTED)
  78. 78. Ongoing work Data Knowledge Application to learning Gain actionable insights into learner behaviours at scale. a. Data Science b. Big data processing Increase our knowledge about learners by looking beyond the learning platform a. Web data analytics Design technology interventions that enable adaptive learning at scale. a. Information retrieval b. Human-centred design c. Learning technologies
  79. 79. MOOCs are vital to bring higher education to the world. Lots of unexplored potential. Plenty of data. Many users. http://bit.ly/lambda-lab Overall …

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