Data Sets as Facilitator for new Products and Services for Universities

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

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Data Sets as Facilitator for new Products and Services for Universities

  1. 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. 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. 3. We live in a decade of industrial change Change picture 3
  4. 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. 5. Graphic by Alex Guerten, 2008 5
  6. 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. 7. G LO C A L I S AT I O N Graphic by Alex Guerten, 2008 5
  8. 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. 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. 10. http://flickr.com/photos/zoharma/97214235/sizes/l/by Zohar Manor-Abel 6 The Glocalisation / Prosumers world
  11. 11. 7 Example Networks
  12. 12. 7 Example Networks
  13. 13. 7 Example Networks
  14. 14. 7 Example Networks
  15. 15. v 8 http://blog.core-ed.net/derek/2006/11/more_on_mles_and_ples.html
  16. 16. life symbiotic... 9
  17. 17. Data & Recommender Systems 10
  18. 18. Data emerge Johnson, S. (2001) 11
  19. 19. Data emerge Johnson, S. (2001) 11
  20. 20. Data emerge Johnson, S. (2001) 11
  21. 21. Data emerge Johnson, S. (2001) 11
  22. 22. Data emerge Johnson, S. (2001) 11
  23. 23. Data emerge Johnson, S. (2001) 11
  24. 24. Data emerge “We are leaving the age of information and entering the age of recommendation” Chris Anderson (2004) Johnson, S. (2001) 11
  25. 25. Recommender Systems 12
  26. 26. Recommender Systems 12
  27. 27. Recommender Systems People who bought the same product also bought product B or C … 12
  28. 28. The Long Tail 13Graphic Wilkins, D., (2009); Long tail concept Anderson, C. (2004)
  29. 29. The Long Tail 13Graphic Wilkins, D., (2009); Long tail concept Anderson, C. (2004) The Long Tail of Learning
  30. 30. TEL recommender are a bit like this... 14
  31. 31. TEL recommender are a bit like this... 14 We need to select for each application an appropriate recommender that fits its needs.
  32. 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. 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. 34. How others compare their their recommender systems 16
  35. 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. 36. Data Products & Science 17
  37. 37. Promises of Open Data 18
  38. 38. Promises of Open Data 18
  39. 39. Promises of Open Data 18 Unexploited potentials:
  40. 40. Promises of Open Data 18 Unexploited potentials: • The evaluation of learning theories and learning technology
  41. 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. 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. 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. 44. 20 Data Products
  45. 45. 20 Data Products
  46. 46. 21 Data Products
  47. 47. 21 Data Products
  48. 48. 21 Data Products Educational Data Products • Drop-out Analyzer • Group Formation Recommender • Question-Answering Tool •Awareness Tools
  49. 49. 21 Data Products Educational Data Products • Drop-out Analyzer • Group Formation Recommender • Question-Answering Tool •Awareness Tools
  50. 50. Future Activities 22
  51. 51. Beyond O P E N DATA 2348
  52. 52. Beyond 23 Beyond the 48
  53. 53. Beyond O P E N DATAO P E N DATA 23 Beyond the 48
  54. 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. 55. Beyond VISUALIZATION OF DATAVISUALIZATION OF DATA 23 Beyond the 48
  56. 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. 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. 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. 59. 24 Open Data
  60. 60. 24 Open Data
  61. 61. 24 Open Data
  62. 62. 24 Open Data
  63. 63. 24 Open Data
  64. 64. 24 Open Data
  65. 65. 24 Open Data
  66. 66. DataVisualization 25
  67. 67. DataVisualization 25
  68. 68. DataVisualization 25
  69. 69. Open Innovation 26
  70. 70. Open Innovation 26 R&D -> C&D
  71. 71. Open Innovation 26 R&D -> C&D
  72. 72. Open Innovation 26 R&D -> C&D
  73. 73. Mashups and Widgets 27
  74. 74. Mashups and Widgets 27
  75. 75. Recommender Systems 28
  76. 76. Recommender Systems 28
  77. 77. Recommender Systems 28
  78. 78. 29 Protection Rights
  79. 79. 29 Protection Rights
  80. 80. 29 Protection Rights OVERSHARING
  81. 81. 3 Take away messages 30
  82. 82. 31
  83. 83. 31 Plan(t)ing for the future
  84. 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. 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. 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. 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. 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.

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