Learning Analytics - Door data gestuurd leren

1,080 views

Published on

Keynote studiedag: Learning analytics - analyseren om leren te optimaliseren

HUB - VOV lerend netwerk

Published in: Education, Technology
0 Comments
4 Likes
Statistics
Notes
  • Be the first to comment

No Downloads
Views
Total views
1,080
On SlideShare
0
From Embeds
0
Number of Embeds
74
Actions
Shares
0
Downloads
16
Comments
0
Likes
4
Embeds 0
No embeds

No notes for slide

Learning Analytics - Door data gestuurd leren

  1. 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 1
  2. 2. Our team:
 Human-Computer Interaction ! technology enhanced learning music research (personal) health 2
  3. 3. 3 http://eng.kuleuven.be/datavislab/
  4. 4. http://www.slideshare.net/jkofmsk 4
  5. 5. About learning… 5
  6. 6. we are teaching students to solve problems we don't know using technologies we don't know 6
  7. 7. we are should be teaching students to solve problems we don't know using technologies we don't know 7
  8. 8. http://www.flickr.com/photos/jonbecker/4625331304/8
  9. 9. http://www.youtube.com/watch?v=p0wk4qG2mIg http://www.learner.org/vod/vod_window.html?pid=9 9
  10. 10. 10
  11. 11. Motivation - empowerment 11
  12. 12. http://www.motivationhacker.com/motivation-is-situational-not-a-personality-trait/ Motivation - empowerment 12
  13. 13. Meten = Weten 13
  14. 14. Ex cathedra - exams http://www.flickr.com/photos/wolflawlibrary/2417195782/ 14
  15. 15. Fake learning 15
  16. 16. exams Judge a man by his questions/actions rather than by his answers…Voltaire 16
  17. 17. Continuous monitoring exams before - during - after 17
  18. 18. Learning analytics 18
  19. 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 19
  20. 20. What to measure? Depends on the user 20
  21. 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? 21
  22. 22. Analytics for professors 22
  23. 23. 23
  24. 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? 24
  25. 25. http://bigtablechi13.appspot.com/dashboard.jsp Learning Dashboards 25 Raw Aggregated
  26. 26. Conceptual process model 26
  27. 27. awareness (self) reflection sense making impact data questions answers behavior change or new meaning 27
  28. 28. How can data on relevant actions be captured? Inspiration 28
  29. 29. Inspiration quantifiedself.com 29
  30. 30. http://www.fitbit.com/ Sensors 30
  31. 31. 31
  32. 32. 32
  33. 33. https://www.rescuetime.com/ Software Sensors 33
  34. 34. Web Analytics - trackers34
  35. 35. 35
  36. 36. https://jawbone.com/up/coffee Self-reporting 36
  37. 37. http://developer.runkeeper.com/healthgraph Sharing, Comparing with your friends 37
  38. 38. What else do people track? 38
  39. 39. http://quantifiedself.com/QSLondon_Survey_overview.pdf 39
  40. 40. Making sense of the data Raise awareness & Reflection 40
  41. 41. http://www.slideshare.net/infoscape41
  42. 42. 42
  43. 43. algorithm <> human 43
  44. 44. Trust? http://www.demorgen.be/dm/nl/5403/Internet/article/detail/1890428/2014/05/18/Twitteractiviteit-verraadt-je-politieke-profiel.dhtml 44
  45. 45. Number crunching <> Human perception 45
  46. 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 46
  47. 47. Educational Data mining Data Visualisation Visual Dashboards <> 47
  48. 48. Example dashboards 48
  49. 49. 49
  50. 50. http://www.snappvis.org 50
  51. 51. http://www.engadget.com/2013/01/09/kno-launches-kno-me-interactive-textbook-metrics-lets-you-stu/ 51
  52. 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. 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. 53
  54. 54. 54
  55. 55. What we do 55
  56. 56. 56
  57. 57. 57
  58. 58. 58
  59. 59. 59
  60. 60. 60
  61. 61. 61
  62. 62. 62
  63. 63. 63
  64. 64. http://tomdebuyser.ulyssis.be/topija/Code/VisualizationV2_1.html 64
  65. 65. 65
  66. 66. Abundance of data?
  67. 67. http://ariadne.cs.kuleuven.be/LARAe/chi14/67
  68. 68. 68
  69. 69. 69
  70. 70. 70
  71. 71. 71 Tabletops
  72. 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. 73
  74. 74. 74 In-class display
  75. 75. 75 Measuring sentiment
  76. 76. 76 Measuring emotion
  77. 77. Privacy? Control? 77
  78. 78. Fear Uncertainty Doubt 78
  79. 79. http://www.slideshare.net/abelardo_pardo/ethics-and-privacy-in-learning-analytics# Attention Trust (2005) Property Mobility (Economy) Transparancy Data access policies 79
  80. 80. http://quantifiedself.com/QSLondon_Survey_overview.pdf 80 QS community
  81. 81. When 81
  82. 82. http://www.nmc.org/pdf/2014-nmc-horizon-report-he-EN.pdf82
  83. 83. 83
  84. 84. Also: you? 84
  85. 85. what can we measure & how is it relevant? what is relevant & how can we measure it? 85
  86. 86. Thank you for your attention! joris.klerkx@cs.kuleuven.be @jkofmsk 86

×