1. LEARNING ANALYTICS AND
ONLINE LEARNING:
NEW OPPORTUNITIES?
IADAT-e2013 International Conference on Education,
Bilbao, July 2013
Svet Ivantchev, eFaber
Ana Fernandez, UPV/EHU

2. University of Boloña, XIV
foto: http://openhive.net/

3. University of Boloña, XIV
foto: http://openhive.net/

4. University of Boloña, XIV
foto: http://openhive.net/

5. University of Boloña, XIV
foto: http://openhive.net/

6. LEARNING ANALYTICS
“The measurement, collection, analysis and reporting
of data about learners and their contexts,
for purposes of understanding and optimizing
learning and the environments in which it occurs.”
As defined at LAK12, http://www.solaresearch.org/mission/about/

7. Bloom, B. "The 2 Sigma Problem:The Search for Methods of Group Instruction as Effective as One-to-One Tutoring",
Educational Researcher, 13-6, 4-16, (1984).

8. Bloom, B. "The 2 Sigma Problem:The Search for Methods of Group Instruction as Effective as One-to-One Tutoring",
Educational Researcher, 13-6, 4-16, (1984).

9. • Use data for formative assessments
and than:
• Tailor instruction to students’ needs
25 yrs of research, 50 000
studies, 200M students
Visible Learning for Teachers: Maximizing Impact on Learning, J. Hattie, 2011

10. Lectures were once useful, but
now, when all can read and
books are numerous, lectures
are unnecessary.
If your attention fails and you
miss part of a lecture, you are
lost; you cannot go back as you
do upon a book.
James Boswell - Life of Johnson (1791)

11. MOOCS
"MOOC, every letter is negotiable", http://en.wikipedia.org/wiki/Massive_open_online_course

13. foto: http://en.wikipedia.org/wiki/Cash_register

21. PROBLEMS
• Maybe it is too much data, can we manage it?
• Once collected, what to do with all this numbers?

24. SFO, March 5, 2013

25. MACHINE LEARNING
Machine learning, a branch of artificial intelligence,
is about the construction and study of systems
that can learn from data.
Field of study that gives computers the ability to learn
without being explicitly programmed

26. BIG DATA O BIG BROTHER?
• TV advertisements and counterprogramming
• Internet advertising
• Credit card and consumer credit authorizations
• Fidelity cards (Travel Club, FNAC, much more ...)
• Buying recommendations, cross selling and upselling
• Churn rates
... but not in education!

31. BIG DATA O BIG BROTHER?
• TV advertisements and counterprogramming
• Internet advertising
• Credit card and consumer credit authorizations
• Fidelity cards (Travel Club, FNAC, much more ...)
• Buying recommendations, cross selling and upselling
• Churn rates
... but not in education!

32. http://en.wikipedia.org/wiki/Luddite

34. OK,WE HAVETHETOOLS, SO?
OUR PROJECT
• Short videos (12-18 min), commentable
• Tests with variable difficulty (self-adjustable)
• Full text search of all the content, audio included
• Log of everything
• ... nothing more (very important ;-)

36. modelo

37. modelo

38. modelo

39. modelo

40. FIRST BENEFITS
• Adapt the difficulty of the test questions based on
student’s behavior (“student model”).
• Feedback to the content/class authors.
• Flipped classroom.

41. Quizzes with personalized difficulty

42. Implicit feedback on video viewing

43. Implicit feedback on video viewing

44. FLIPPED CLASSROOM
• Learn the theory at home, exercises at school
• More time to resolve unclear points and practical examples.
• Peer instruction.

46. MORE IDEAS
• Cross-usage of the data of different students
• Personalized sequence of (optional) videos
• Personalized annotations of the material
• Group creation/assignment
• Early outlier detection

47. GRACIAS