Presentation at ED-Media 2015 conference, Montreal, Canada

1. Learning Analytics
Principles & Constraints
Khalil, Mohammad and Ebner, Martin
EdMedia 2015, Montréal

2. Big Data Analytics

3. Psychology
ComputerScience
http://wallpapers.brothersoft.com/track-and-field-93927.html

4. What Can Learning Analytics do?
• Students improve their performance
• Predict the student’s grades
• Enhance retention percentages
• Point difficulties in courses models

5. Phases ?
Constraints ?

11. A Life Cycle?

13. Phases ?
Constraints ?

14. What
if
Learning
Analy/cs
went
wrong?
h5ps://www.flickr.com/photos/thierrylord/4541208666
Mistaken
Predic/ons!
Ethical
breaches!!

15. Learning Analytics
http://www.beyondblackwhite.com/wp-content/uploads/2011/09/thumbs-down.jpg

16. Learning Analytics - Constraints

17. Learning Analytics - Constraints
Revealing Personal Information
Morality to view students’ data
Collecting and Analyzing data
Transparency
Students’ data deletion policy

18. Learning Analytics - Constraints
Achieving Confidentiality, Integrity
and Availability
Who owns students data,
Students or institutions?
Data Protection and Copyright
Laws limit the use of LA apps
Inaccurate analysis results?

19. A Framework?

20. Learning Analytics: Principles & Constraints

21. THANK YOU
Mohammad
Khalil
Ø Mohammad.Khalil@tugraz.at
“Learning
Analy.cs:
Principles
and
Constraints”,
By
Mohammad
Khalil
&
Mar.n
Ebner,
Graz
University
of
Technology,
EDMEDIA-­‐15’
,
Canada,
June.
2015
Mar/n
Ebner
Ø Mar/n.Ebner@tugraz.at