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Learner profiling beyond the MOOC platform

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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

Published in: Data & Analytics
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Learner profiling beyond the MOOC platform

  1. 1. Learner profiling beyond the MOOC platform Guanliang Chen, Dan Davis, Jun Lin, Claudia Hauff, Geert-Jan Houben
  2. 2. Whythis research? Learner Engagement, retention, … During the MOOC
  3. 3. Whythis research? Learner Before the MOOC NOTHING Engagement, retention, … During the MOOC
  4. 4. Whythis research? Learner Before the MOOC NOTHING Engagement, retention, … During the MOOC NOTHING After the MOOC
  5. 5. Howto solve the problem? We propose: a deeper understanding about learners can be gained by exploring their traces on the Social Web.
  6. 6. Whatresearch questions?
  7. 7. Whatresearch questions? 1 On what Social Web platforms can a significant fraction of MOOC learners be identified? 

  8. 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. 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. 10. Learner identification across Social Web platforms edX learners Email Login name Full name+ +
  11. 11. Learner identification across Social Web platforms edX learners Email Login name Full name+ + 1. Explicit Matching Profile images & links Identification via emails
  12. 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. 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. 14. Social Web platforms involved in our work
  15. 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. 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. 17. Learners on Twitter - To predict learners’ demographics (e.g., age & gender)
  18. 18. Learners on Linkedin - Using job titles & skills to characterise learners
  19. 19. Learners on Linkedin - Using job titles & skills to characterise learners Data Analysis MOOC - Software Engineer - Business Analyst - …
  20. 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. 21. Learners on Linkedin - Using job titles & skills to characterise learners - Visualised by applying t-SNE techniques.
  22. 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. 23. Learners on GitHub - To what extent do learners transfer their acquired knowledge into practice? - Functional Programming learners
  24. 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. 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. 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. 27. Thank you! c.hauff@tudelft.nl http://bit.ly/lambda-lab

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