The document discusses findings from applying learning analytics to MOOCs. It summarizes that learning analytics of MOOCs has shown that the dropout rate is not as high as assumed, with many learners participating intensively for the first 4 weeks. While effort levels varied considerably between learners, higher effort did not necessarily predict success in courses. Learning analytics also provided insights into how learning happens for groups of students in MOOC classrooms.

1. When Learning Analytics
Meets MOOCs
Martin Ebner, Mohammad Khalil
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This work is licensed under a
Creative Commons Attribution
4.0 International License.

2. http://www.facebook.com/
martin.ebner
http://www.martinebner.at
https://twitter.com/#!/
mebner
http://
elearningblog.
tugraz.at
https://
www.researchgate.net/
profile/Martin_Ebner2

3. is a xMOOC platform for courses
with an explicit open license (Creative
Commons).

4. http://imoox.at

5. http://imoox.at
https://youtu.be/DljC8FPpE1s

6. Austrian National Price of Adult
Education 2015

8. How to Implement Learning Analytics

9. Khalil, M, Ebner, M. (2016) What CAN Massive Open Online Course (MOOC)
Stakeholders Learn from Learning Analytics? accepted, in print

10. Khalil, M, Ebner, M. (2016) What CAN Massive Open Online Course (MOOC)
Stakeholders Learn from Learning Analytics? accepted, in print

11. Khalil, M, Ebner, M. (2016) What CAN Massive Open Online Course (MOOC)
Stakeholders Learn from Learning Analytics? accepted, in print

12. 5 crucial statements
What can we learn from Learning Analytics?

13. Thesis 1
High Dropout Rate on MOOCs is a
legend

14. GOL
• 1012 Registrants
• 479 Active Students (47.3%)
• 217 complete course (21,5%)
• 177 got certification (17,5%)
LIN
• 618 Registrants
• 461 Active Students (74.5%)
• 131 compete course (21,2%)
• 99 got certification (16%)
Lackner, E., Ebner, M., Khalil, M. (2015) MOOCs as granular systems: design
patterns to foster participant activity, eLearning Papers, 42 (2015), pp. 28-37

15. Khalil, M., Ebner, M. (2015) A STEM MOOC for School Children - What Does
Learning Analytics Tell us? In: Proceedings of 2015 International Conference on
Interactive Collaborative Learning (ICL), Florence, Italy, pp. 1-7

16. Thesis 2
MOOCs means participatory education
(at least in the first four weeks)

17. ~ 23,000 reads in GOL Forums
~ The highest on 21-Oct
~ Average of 170 read per day
~ Median of 47 read per day
~ 1623 Max reads on 21-Oct
Week 1 Starts
Week 2 Starts
Week 3 Starts
Course ended

19. Khalil, M, Ebner, M. (2016) What CAN Massive Open Online Course (MOOC)
Stakeholders Learn from Learning Analytics? accepted, in print

20. Thesis 3
Higher effort is good but not a good
predictor for success

21. Khalil, M, Ebner, M. (2016) What CAN Massive Open Online Course (MOOC)
Stakeholders Learn from Learning Analytics? accepted, in print

22. Khalil, M, Ebner, M. (2016) What CAN Massive Open Online Course (MOOC)
Stakeholders Learn from Learning Analytics? accepted, in print

23. Thesis 4
Learning Analytics tells us how learning
in classrooms happens

24. Students
(n=459)
Khalil, M, Kastl, C., Ebner, M. (2016) Portraying MOOCs Learners: a Clustering
Experience Using Learning Analytics, accepted, in print
1 (n=95)
2 (n=154)
3 (n=206)
4 (n=4)

25. Students
(n=459)
Khalil, M, Kastl, C., Ebner, M. (2016) Portraying MOOCs Learners: a Clustering
Experience Using Learning Analytics, accepted, in print

26. Learning happens all time
Thesis 5

27. Lackner, E., Ebner, M., Khalil, M. (2016) How to foster forum discussions within
MOOCs. A case study, in review

28. Lackner, E., Ebner, M., Khalil, M. (2016) How to foster forum discussions within
MOOCs. A case study, in review

29. http://www.flickr.com/photos/o5com/5489061293
MOOC & Learning Analytics
challenges?

30. Kopp, M., Ebner, M., Dorfer-Novak, A. (2014) Introducing MOOCs to Middle
European Universities – is it worth it to accept the challenge?, International
Journal for Innovation and Quality in Learning, Vol. 2/3, pp. 46-52

31. Khalil, M. & Ebner, M. (2015). Learning Analytics: Principles and Constraints. In
Proceedings of World Conference on Educational Multimedia, Hypermedia and
Telecommunications 2015. pp. 1326-1336. Chesapeake, VA: AACE
LA constraints

32. Khalil, M. & Ebner, M. (2015). Learning Analytics: Principles and Constraints. In
Proceedings of World Conference on Educational Multimedia, Hypermedia and
Telecommunications 2015. pp. 1326-1336. Chesapeake, VA: AACE
LA constraints
Revealing Personal Information
Morality to view students’ data
Collecting and Analyzing data
Transparency
Students’ data deletion policy

33. Khalil, M. & Ebner, M. (2015). Learning Analytics: Principles and Constraints. In
Proceedings of World Conference on Educational Multimedia, Hypermedia and
Telecommunications 2015. pp. 1326-1336. Chesapeake, VA: AACE
LA constraints
Who owns students data,
Students or institutions?
Data Protection and Copyright
Laws limit the use of LA apps
Inaccurate analysis results?
Achieving Confidentiality, Integrity
and Availability

34. Potentials for
society
http://www.flickr.com/photos/ell-r-brown/3912248265
lifelong
learning
Knowledge
society
Access to
education
cost
reducement Quality
improvement

35. Summary
„Learning Analytics for MOOCs tells us that
the DropOut rate is not so high as
proposed, …“

36. „… learners participating for about 4
weeks intensively the whole day … “
Summary

37. „… with considerable different effort and
commitment.“
Summary

38. http://emoocs2016.eu
Visit us in Graz!
(22- 24. February)

39. Graz University of Technology
EDUCATIONAL TECHNOLOGY
Graz University of Technology
Martin Ebner
http://elearning.tugraz.at
martin.ebner@tugraz.at
http://elearningblog.tugraz.at
mebner
Slides available at:
This work is licensed under a
Creative Commons Attribution
4.0 International License.