Invited talk at SURF seminar on Learning Analytics http://www.surf-academy.nl/programma/event/?id=395

1. Turning Learning into Numbers
A Learning Analytics Framework
Graphic by Alex Guerten, 2008
Hendrik Drachsler & Wolfgang Greller
Centre for Learning Sciences and Technologies (CELSTEC)
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2. Goals of the Presentation
LA Framework
Examples
Data
initiatives
Participation
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3. A view on Learning Analytics
The Learning
Analytics
Framework
Greller, W., & Drachsler, H., (submitted). Turning Learning into Numbers.
Toward a Generic Framework for Learning Analytics. Journal of Educational
Technology & Society.
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4. 4

5. 4

6. 4

7. 4

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10. 4

11. Zooming into the Framework
Practical
examples for
application
and use
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12. Stakeholders
data data
subjects clients
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13. Objectives
Reflection
(Glahn, 2009) 7

14. Objectives
Reflection
(Glahn, 2009) 7

15. Objectives
Reflection Prediction
(Glahn, 2009) 7

16. Educational Data
Verbert, K., Manouselis, N., Drachsler, H., and Duval,E. (submitted).Dataset-driven
Research to Support Learning and Knowledge Analytics. Journal of Educational Technology
& Society. 8

17. Educational Data
Drachsler, H., et al. (2010). Issues and Considerations regarding Sharable Data Sets for
Recommender Systems in Technology Enhanced Learning. 1st Workshop Recommnder
Verbert, in Manouselis, N., Drachsler, H., and Duval,E. (submitted).Dataset-driven
SystemsK., Technology Enhanced Learning (RecSysTEL@EC-TEL 2010) September, 28,
Research to Support Learning and Knowledge Analytics. Journal of Educational Technology
2010, Barcelona, Spain.
& Society. 8

18. Educational Data
Drachsler, H., et al. (2010). Issues and Considerations regarding Sharable Data Sets for
Recommender Systems in Technology Enhanced Learning. 1st Workshop Recommnder
Verbert, in Manouselis, N., Drachsler, H., and Duval,E. (submitted).Dataset-driven
SystemsK., Technology Enhanced Learning (RecSysTEL@EC-TEL 2010) September, 28,
Research to Support Learning and Knowledge Analytics. Journal of Educational Technology
2010, Barcelona, Spain.
& Society. 8

19. Educational Data
1.Privacy
2.Prepare datasets
3.Share datasets
4.Body of knowledge
Drachsler, H., et al. (2010). Issues and Considerations regarding Sharable Data Sets for
Recommender Systems in Technology Enhanced Learning. 1st Workshop Recommnder
Verbert, in Manouselis, N., Drachsler, H., and Duval,E. (submitted).Dataset-driven
SystemsK., Technology Enhanced Learning (RecSysTEL@EC-TEL 2010) September, 28,
Research to Support Learning and Knowledge Analytics. Journal of Educational Technology
2010, Barcelona, Spain.
& Society. 8

20. Technologies
Learning Analytics
Hanani et al., (2001). Information Filtering: Overview of Issues,
Research and Systems", User Modeling and User-Adapted
Interaction, 11, 2001 9

21. Technologies
Learning Analytics
Hanani et al., (2001). Information Filtering: Overview of Issues,
Research and Systems", User Modeling and User-Adapted
Interaction, 11, 2001 9

22. Technologies
Prediction
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23. Technologies
Reflection
Peter Kraker, Claudia Wagner, Fleur Jeanquartier, Stefanie N. Lindstaedt (2011):
On the Way to a Science Intelligence: Visualizing TEL Tweets for Trend Detection
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Sixth European Conference on Technology Enhanced Learning (EC-TEL 2011)

24. Technologies
Reflection
Peter Kraker, Claudia Wagner, Fleur Jeanquartier, Stefanie N. Lindstaedt (2011):
On the Way to a Science Intelligence: Visualizing TEL Tweets for Trend Detection
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Sixth European Conference on Technology Enhanced Learning (EC-TEL 2011)

25. Constraints
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26. Constraints
1.Legal protection
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27. Constraints
1.Legal protection
2.Privacy
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28. Constraints
1.Legal protection
2.Privacy
3.Ethics
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29. Constraints
1.Legal protection
2.Privacy
3.Ethics
4.Ownership
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30. Privacy
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31. Privacy
1.Privacy as confidentiality
The right to be let alone (Warren and Brandeis, 1890)
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32. Privacy
1.Privacy as confidentiality
The right to be let alone (Warren and Brandeis, 1890)
2.Privacy as control
The right of the individual to decide what information
about herself should be communicated to others and
under which circumstances.
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33. Privacy
1.Privacy as confidentiality
The right to be let alone (Warren and Brandeis, 1890)
2.Privacy as control
The right of the individual to decide what information
about herself should be communicated to others and
under which circumstances.
3.Privacy as practice
The right to intervene in the flows of existing data and
the re-negotiation of boundaries with respect to
collected data.
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34. Privacy solutions
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35. Privacy solutions
1.Privacy as confidentiality
Information services that minimizing, secure or
anonymize the collected information
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36. Privacy solutions
1.Privacy as confidentiality
Information services that minimizing, secure or
anonymize the collected information
2.Privacy as control
Identity Management Systems (IDMS),
with access control rules
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37. Privacy solutions
1.Privacy as confidentiality
Information services that minimizing, secure or
anonymize the collected information
2.Privacy as control
Identity Management Systems (IDMS),
with access control rules
3.Privacy as practice
Timestamp on data, data degradation technologies
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38. Competences
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39. Reading score of 15 year olds of countries
Competences
compared to the percentage of migrants
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40. Reading score of 15 year olds of countries
Competences
compared to the percentage of migrants
1.E-literacy
2.Interpretation skills
3.Self-directedness
4.Ethical understanding
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41. Data initiatives
Relevant
data initiatives
for Learning
Analytics
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42. Data Quality / Standard
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43. Data Quality / Standard
CAM -
Contextualized
Attention Metadata
https://
sites.google.com/site/
camschema/
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44. Data Citing
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45. Sharing policies
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46. Sharing policies
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47. Sharing policies
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48. Policy guidelines
A brief guide on data licenses developed by SURF and the Centre for
Intellectual Property Law (CIER), 2009 available at
www.surffoundation.nl
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49. Data platforms
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50. Data platforms
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51. Data platforms
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52. Data platforms
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53. Data platforms
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54. Participation
How you can
become
involved...
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55. Learning Analytics Challenges
• Reduce the drop-out rate by 10%
through applying Learning Analytics
prediction and reflection techniques.
• Customize data mining techniques to
learning and educational reality.
• Evaluation criteria for Learning
Analytics applications
• Generic infrastructure for sharing, analyzing and reusing
educational data.
• Applying existing privacy and legal protection solutions for
Learning Analytics.
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56. Open issues
1. Evaluation of LA approaches
2. Comparable experiments
3. Publicly available datasets
4. Body of knowledge
5. New Learning Analytic applications
6. Privacy and data protection
7. Best practice and ethical guidelines
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57. Learning Analytics Questionnaire
http://bit.ly/
Learning_Analytics
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58. Special Interest Group - dataTEL
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59. Special Interest Group - dataTEL
1.Networking
2.Privacy, legal, ethics
3.LA datasets
4.LA products
http://bit.ly/datatel
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60. Many Thanks::Questions?
This presentation is
available at:
slideshare.com/
Drachsler
Blackmore’s custom-built LSD Drive
http://www.flickr.com/photos/rootoftwo/
Email: hendrik.drachsler@ou.nl
Email: wolfgang.greller@ou.nl
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