Keynote studiedag: Learning analytics - analyseren om leren te optimaliseren HUB - VOV lerend netwerk

1. Learning Analytics
Door data gestuurd leren
Joris Klerkx
Research Expert, PhD.
http://hci.cs.kuleuven.be
@jkofmsk
Erik Duval
Professor
http://hci.cs.kuleuven.be
@erikd
Studienamiddag - Learning analytics - HU Brussel - 22 mei 2014
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2. Our team:
Human-Computer Interaction
!
technology enhanced learning
music
research
(personal) health
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3. 3 http://eng.kuleuven.be/datavislab/

4. http://www.slideshare.net/jkofmsk
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5. About learning…
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6. we are teaching students
to solve problems we don't know
using technologies we don't know
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7. we are should be teaching students
to solve problems we don't know
using technologies we don't know
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8. http://www.flickr.com/photos/jonbecker/4625331304/8

9. http://www.youtube.com/watch?v=p0wk4qG2mIg
http://www.learner.org/vod/vod_window.html?pid=9
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10. 10

11. Motivation - empowerment
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12. http://www.motivationhacker.com/motivation-is-situational-not-a-personality-trait/
Motivation - empowerment
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13. Meten = Weten
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14. Ex cathedra - exams
http://www.flickr.com/photos/wolflawlibrary/2417195782/
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15. Fake learning
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16. exams
Judge a man by his questions/actions
rather than by his answers…Voltaire
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17. Continuous monitoring
exams
before - during - after
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18. Learning analytics
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19. Collecting traces that learners leave
behind and using those traces to improve
learning
http://erikduval.wordpress.com/2012/01/30/learning-analytics-and-educational-data-mining/
Learning analytics
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20. What to measure?
Depends on the user
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21. Analytics for Students
access to learning resources
posts in discussion fora
logins to learning management systems
posts of assignments
replies to posts
votes in lecture response systems
time on page in electronic textbook
location of device used to access course
(and thus proximity to other users)
software lines produced
contributions to shared documents or wikis
etc.
Who?
!
!
What?
!
!
When?
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22. Analytics for professors
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23. 23

24. email, twitter, facebook, web reading,
physical movement, location, proximity,
food intake, sleeping, drinking, emotion
tracking, weather info, attention,
brainwaves, …
As learning moves online, traces also
include…
Relevant?
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25. http://bigtablechi13.appspot.com/dashboard.jsp
Learning Dashboards
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Raw Aggregated

26. Conceptual process model
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27. awareness
(self) reflection
sense making
impact
data
questions
answers
behavior change or
new meaning
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28. How can data on relevant actions be
captured?
Inspiration
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29. Inspiration
quantifiedself.com
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30. http://www.fitbit.com/
Sensors
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31. 31

32. 32

33. https://www.rescuetime.com/
Software
Sensors
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34. Web Analytics - trackers34

35. 35

36. https://jawbone.com/up/coffee
Self-reporting
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37. http://developer.runkeeper.com/healthgraph
Sharing, Comparing with your friends
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38. What else do people track?
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39. http://quantifiedself.com/QSLondon_Survey_overview.pdf 39

40. Making sense of the data
Raise awareness & Reflection
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41. http://www.slideshare.net/infoscape41

42. 42

43. algorithm
<>
human
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44. Trust?
http://www.demorgen.be/dm/nl/5403/Internet/article/detail/1890428/2014/05/18/Twitteractiviteit-verraadt-je-politieke-profiel.dhtml
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45. Number crunching
<>
Human perception
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46. Anscombe’”⁹s quartet
!
uX = 9.0
uY = 7.5
sigma X = 3.317
sigma Y = 2.03
Y = 3 + 0.5X
http://en.wikipedia.org/wiki/Anscombe's_quartet
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47. Educational Data mining
Data Visualisation
Visual Dashboards
<>
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48. Example dashboards
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49. 49

50. http://www.snappvis.org
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51. http://www.engadget.com/2013/01/09/kno-launches-kno-me-interactive-textbook-metrics-lets-you-stu/
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52.
Khaled
Bachour,
Frederic
Kaplan,
Pierre
Dillenbourg,
"An
Interac:ve
Table
for
Suppor:ng
Par:cipa:on
Balance
in
Face-­‐to-­‐Face
Collabora:ve
Learning,"
IEEE
Transac:ons
on
Learning
Technologies,
vol.
3,
no.
3,
pp.
203-­‐213,
July-­‐September,
2010
52

53. http://gigaom.com/2013/06/19/new-augmented-reality-glasses-let-teachers-know-when-their-students-are-falling-behind/
T. Zarraonandia, I. Aedo, P. Dıaz, and A. Montero. An augmented lecture feedback system to support learner and teacher communication. British Journal o
Technology, 44(4):616–628, 2013.
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54. 54

55. What we do
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56. 56

57. 57

58. 58

59. 59

60. 60

61. 61

62. 62

63. 63

64. http://tomdebuyser.ulyssis.be/topija/Code/VisualizationV2_1.html
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65. 65

66. Abundance of data?

67. http://ariadne.cs.kuleuven.be/LARAe/chi14/67

68. 68

69. 69

70. 70

71. 71
Tabletops

72. J. Santos, S. Charleer, G. Parra, J. Klerkx, E. Duval, and K.Verbert. Evaluating the use of open badges in an open learning
environment. In D. Hernandez-Leo,T. Ley, R. Klamma, and A. Harrer, editors, Scaling up Learning for Sustained Impact, volume
8095 of Lecture Notes in Computer Science, pages 314–327. Springer Berlin Heidelberg, 2013.72

73. 73

74. 74
In-class display

75. 75
Measuring sentiment

76. 76
Measuring emotion

77. Privacy? Control?
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78. Fear
Uncertainty
Doubt
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79. http://www.slideshare.net/abelardo_pardo/ethics-and-privacy-in-learning-analytics#
Attention Trust (2005)
Property
Mobility
(Economy)
Transparancy
Data access policies
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80. http://quantifiedself.com/QSLondon_Survey_overview.pdf 80
QS community

81. When
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82. http://www.nmc.org/pdf/2014-nmc-horizon-report-he-EN.pdf82

83. 83

84. Also: you?
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85. what can we measure & how is it relevant?
what is relevant & how can we measure it?
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86. Thank you for your attention!
joris.klerkx@cs.kuleuven.be
@jkofmsk
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