Presentation for VOR-ICT Bijeenkomst, Utrecht, The Netherlands, 30.11.2010

1. Dr. Hendrik Drachsler
Centre for Learning Sciences and Technology
@ Open University of the Netherlands
Data Sets as Facilitator for new Products
and Services for Universities
1
29.11.2010 VOR-ICT Bijeenkomst, Utrecht,The Netherlands

2. • Assistant Professor at the
Learning Networks Program
OUNL / CELSTEC
• Research topics:
Learning Networks,
Technology Enhanced
Learning, Recommender
Systems, Personalisation,
Mash-Ups and widget
technology, Health2.0
2
Whoami

3. We live in a decade of
industrial change
Change picture
3

4. “The biggest challenge businesses face
today is unlearning what was successful in
the industrial age and learning how to
prosper in the network era.”
The challenge
Jay Cross (2006)
4

5. Graphic by Alex Guerten, 2008
5

6. G
L
O
B
A
L
I
S
A
T
I
O
N
LO C A L I S AT I O N
Graphic by Alex Guerten, 2008
5

7. G LO C A L I S AT I O N
Graphic by Alex Guerten, 2008
5

8. C O N S U M E R S
P
R
O
D
U
C
E
R
S
G LO C A L I S AT I O N
Graphic by Alex Guerten, 2008
5

9. G LO C A L I S AT I O N
P R O S U M E R S
Graphic by Alex Guerten, 2008
5

10. http://flickr.com/photos/zoharma/97214235/sizes/l/by Zohar Manor-Abel
6
The Glocalisation / Prosumers world

11. 7
Example Networks

12. 7
Example Networks

13. 7
Example Networks

14. 7
Example Networks

15. v
8
http://blog.core-ed.net/derek/2006/11/more_on_mles_and_ples.html

16. life symbiotic...
9

17. Data & Recommender
Systems
10

18. Data emerge
Johnson, S. (2001)
11

19. Data emerge
Johnson, S. (2001)
11

20. Data emerge
Johnson, S. (2001)
11

21. Data emerge
Johnson, S. (2001)
11

22. Data emerge
Johnson, S. (2001)
11

23. Data emerge
Johnson, S. (2001)
11

24. Data emerge
“We are leaving the age of information and
entering the age of recommendation”
Chris Anderson (2004)
Johnson, S. (2001)
11

25. Recommender Systems
12

26. Recommender Systems
12

27. Recommender Systems
People who bought the same
product also bought product
B or C …
12

28. The Long Tail
13Graphic Wilkins, D., (2009); Long tail concept Anderson, C. (2004)

29. The Long Tail
13Graphic Wilkins, D., (2009); Long tail concept Anderson, C. (2004)
The Long Tail of Learning

30. TEL recommender are
a bit like this...
14

31. TEL recommender are
a bit like this...
14
We need to select for each application an
appropriate recommender that fits its needs.

32. But...
15
Kaptain Kobold
http://www.flickr.com/photos/
kaptainkobold/3203311346/
“The performance results
of different research
efforts in recommender
systems are hardly
comparable.”
(Manouselis et al., 2010)

33. But...
15
Kaptain Kobold
http://www.flickr.com/photos/
kaptainkobold/3203311346/
“The performance results
of different research
efforts in recommender
systems are hardly
comparable.”
(Manouselis et al., 2010)
The TEL recommender
experiments lack
transparency.They need
to be repeatable to test:
•Validity
•Verification
• Compare results

34. How others compare their
their recommender systems
16

35. How others compare their
their recommender systems
16
Although the TEL domain stores plenty of
data everyday in e-learning environments
(LMS, PLEs) it typically lacks shareable
and publicly available data sets.

36. Data Products &
Science
17

37. Promises of Open Data
18

38. Promises of Open Data
18

39. Promises of Open Data
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Unexploited potentials:

40. Promises of Open Data
18
Unexploited potentials:
• The evaluation of learning
theories and learning technology

41. Promises of Open Data
18
Unexploited potentials:
• The evaluation of learning
theories and learning technology
• More transparent, mutually
comparable, trusted and
repeatable experiments that lead
to evidence-driven knowledge

42. Promises of Open Data
18
Unexploited potentials:
• The evaluation of learning
theories and learning technology
• More transparent, mutually
comparable, trusted and
repeatable experiments that lead
to evidence-driven knowledge
• Development of new
educational data products that
combine different data sources in
data mashups

43. New Science Paradigms
19
•Thousand years ago science was
empirical (Describing natural phenomena)
• Last few hundred years science:
theoretical branch (Using models,
generalizations)
• Last few decades: computational
branch (Simulating complex phenomena)
• Nowadays: data science
(Unify theory, experiment, and simulation,
data captured by instruments and processed
by software)

44. 20
Data Products

45. 20
Data Products

46. 21
Data Products

47. 21
Data Products

48. 21
Data Products
Educational Data Products
• Drop-out Analyzer
• Group Formation Recommender
• Question-Answering Tool
•Awareness Tools

49. 21
Data Products
Educational Data Products
• Drop-out Analyzer
• Group Formation Recommender
• Question-Answering Tool
•Awareness Tools

50. Future Activities
22

51. Beyond
O P E N DATA
2348

52. Beyond
23
Beyond the
48

53. Beyond
O P E N DATAO P E N DATA
23
Beyond the
48

54. Beyond
O P E N I N N OVAT I O NO P E N I N N OVAT I O N
23
Beyond the
48

55. Beyond
VISUALIZATION OF DATAVISUALIZATION OF DATA
23
Beyond the
48

56. Beyond
M A S H U P T E C H N O L O G YM A S H U P T E C H N O L O G Y
23
Beyond the
48

57. Beyond
R E C O M M E N D E R S Y S T E M SR E C O M M E N D E R S Y S T E M S
23
Beyond the
48

58. Beyond
P ROT E C T I O N R I G H T SP ROT E C T I O N R I G H T S
23
Beyond the
48

59. 24
Open Data

60. 24
Open Data

61. 24
Open Data

62. 24
Open Data

63. 24
Open Data

64. 24
Open Data

65. 24
Open Data

66. DataVisualization
25

67. DataVisualization
25

68. DataVisualization
25

69. Open Innovation
26

70. Open Innovation
26
R&D -> C&D

71. Open Innovation
26
R&D -> C&D

72. Open Innovation
26
R&D -> C&D

73. Mashups and Widgets
27

74. Mashups and Widgets
27

75. Recommender Systems
28

76. Recommender Systems
28

77. Recommender Systems
28

78. 29
Protection Rights

79. 29
Protection Rights

80. 29
Protection Rights
OVERSHARING

81. 3 Take away messages
30

82. 31

83. 31
Plan(t)ing for the future

84. 31
Plan(t)ing for the future
1. Use digital ecosystem services for student
projects (Google API, Yahoo pipes, Twitter,
Reuters Open Calais ...)

85. 31
Plan(t)ing for the future
1. Use digital ecosystem services for student
projects (Google API, Yahoo pipes, Twitter,
Reuters Open Calais ...)
2. Apply and create open data for research
(Become part of the ecosystem, open innovation,
science2.0 -> pre-processing, privacy protection)

86. 31
Plan(t)ing for the future
1. Use digital ecosystem services for student
projects (Google API, Yahoo pipes, Twitter,
Reuters Open Calais ...)
2. Apply and create open data for research
(Become part of the ecosystem, open innovation,
science2.0 -> pre-processing, privacy protection)
3. Empower your users to adjust and remix your
contributions to the web (Open API’s, protocols,
standards -> interoperability)

87. This silde is available at:
http://www.slideshare.com/Drachsler
Email: hendrik.drachsler@ou.nl
Skype: celstec-hendrik.drachsler
Blogging at: http://www.drachsler.de
Twittering at: http://twitter.com/HDrachsler
32
Questions and ideas now
or later...

88. References
33
Anderson, C. 2004.“The long tail.” Wired Magazine 12 (10).Available: http://www.wired.com/wired/archive/12.10/tail.html
Cross, J., (2006) Informal learning: Rediscovering the natural pathways that inspire innovation and performance. Pfeifer
Drachsler, H., Hummel, H., & Koper, R. (2008a). Personal recommender systems for learners in lifelong learning: requirements,
techniques and model. International Journal of LearningTechnology 3(4), 404 - 423.
Drachsler, H., Hummel, H., & Koper, R. (2008b). Using Simulations to Evaluate the Effects of Recommender Systems for Learners in
Informal Learning Networks. Paper presented at the EC-TEL conference, 2nd Workshop on Social Information Retrieval in
Technology Enhanced Learning (SIRTEL08). September, 16-19, 2008, Maastricht,The Netherlands: CEUR Workshop
Proceedings
Drachsler, H., Hummel, H., & Koper, R. (2009). Identifying the Goal, User model and Conditions of Recommender Systems for
Formal and Informal Learning. Journal of Digital Information.
Drachsler, H., Hummel, H., van den Berg, B., Eshuis, J., Berlanga,A., Nadolski, R.,Waterink,W., Boers, N., & Koper, R. (accepted).
Effects of the ISIS Recommender System for navigation support in self-organised Learning Networks. Journal of Educational
Technology and Society.
Drachsler, H., Dries, E.,Arts,T., Rutledge, L.,Van Rosmalen, P., Hummel, H. G. K., & Koper, R. (submitted). ReMashed –
Recommendations for Mash-Up Personal Learning Environments. 4th European Conference on Technology Enhanced
Learning, EC-TEL 2009. Learning in the Synergy of Multiple Disciplines, September, 29, 2009, Nice, Italy
Iyer, B., & Davenport,T. H. (2008). Reverse engineering Google's innovation machine. Harvard Business Review.
Kalz, M.,Van Bruggen, J., Giesbers, B., & Koper, R. (2007). Prior Learning Assessment with Latent Semantic Analysis. In F.Wild,
M. Kalz, J.Van Bruggen & R. Koper (Eds.). Proceedings of the First European Workshop on Latent Semantic Analysis in
Technology Enhanced Learning (pp. 24-25). Heerlen,The Netherlands: Open University of the Netherlands.
Gahn, C., Specht, M., & Koper, R. (2007). Smart Indicators on Learning Interactions. In E. Duval, R. Klamma, & M.Wolpers
(Eds), Creating New Learning Experiences on a Global Scale: LNCS 4753. Second European Conference on Technology
Enhanced Learning, EC-TEL 2007 (pp. 56-70). Berlin, Heidelberg: Springer.