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PhD Defense: Navigation Support for Lerners in Informal Learning Networks
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PhD Defense: Navigation Support for Lerners in Informal Learning Networks

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This presentation offers an extended abstract of a PhD project that focuses on supporting learners in finding most suitable learning activities in informal learning environments. For this purpose we …

This presentation offers an extended abstract of a PhD project that focuses on supporting learners in finding most suitable learning activities in informal learning environments. For this purpose we aim to develop a personal recommender system, which will recommend most suitable learning activities to learners regarding their personal needs and preferences. As a theoretical framework for informal learning environments we use the concept of Learning Networks. Learning Networks can be filled with lots of learning activities stemming from different providers. Such networks are dynamic, because each member could add or delete content at any time. A personal recommender system is needed to support learners in selecting learning activities from a Learning Network that will enable them to achieve their learning goals in a specific domain. It is expected that such support will minimize the amount of time learners need for finding suitable learning activities. A better alignment of the characteristics of learners and learning activities is expected to increase both effectiveness and efficiency of learning progress of the learners.

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  • 1. Hendrik Drachsler, Open University of the Netherlands PhD Defense, 16 October 2009
  • 2. Networked Knowledge Society g y
  • 3. Learning = Knowledge Society Knowledge Society g y
  • 4. Informal Learning Activities
  • 5. Learning Networks • explicitly address informal  learning  • allow learners to publish,  share, rate, tag and adjust  their own Learning Activities  their own Learning Activities in a Learning Network • contain open corpora that  emerge from the bottom  upwards
  • 6. Emerging paths g gp Personalized paths th Main Road © peterme.com, flickr 2009
  • 7. Selection Problem Learners can get  overwhelmed by  y the amount of  information in  Learning Networks.
  • 8. Recommender Systems y People who bought the same product also bought product B or C …
  • 9. Recommender Systems for  y Learning Paths
  • 10. The PhD Project j Prototype:  Practical Recommender System  for Learning Networks Study 3: Learning Networks y g Simulation Study 2: Psychology Experiment Study 2: Psychology Experiment Theoretical Study 1: Theoretical Background y g 2006 2007 2008 2009 hendrik.drachsler@ou.nl Recommender Systems 2008, Lausanne Page 10
  • 11. The PhD Project j Prototype:  Practical Recommender System  for Learning Networks 2006 Study 3: Learning Networks y g Simulation Study 2: Psychology Experiment Study 2: Psychology Experiment Theoretical Study 1: Theoretical Background y g 2006 2007 2008 2009 hendrik.drachsler@ou.nl Recommender Systems 2008, Lausanne Page 11
  • 12. The PhD Project j Prototype:  Practical Recommender System  for Learning Networks 2007 2008 2006 2009 Study 3: Learning Networks y g Simulation Study 2: Psychology Experiment Study 2: Psychology Experiment Theoretical Study 1: Theoretical Background y g 2006 2007 2008 2009 hendrik.drachsler@ou.nl Recommender Systems 2008, Lausanne Page 12
  • 13. The PhD Project j Prototype:  Practical Recommender System  for Learning Networks 2008 2006 2009 Study 3: Learning Networks y g Simulation Study 2: Psychology Experiment Study 2: Psychology Experiment Theoretical Study 1: Theoretical Background y g 2006 2007 2008 2009 hendrik.drachsler@ou.nl Recommender Systems 2008, Lausanne Page 13
  • 14. The PhD Project j Prototype:  Practical Recommender System  for Learning Networks 2006 2009 Study 3: Learning Networks y g Simulation Study 2: Psychology Experiment Study 2: Psychology Experiment Theoretical Study 1: Theoretical Background y g 2006 2007 2008 2009 hendrik.drachsler@ou.nl Recommender Systems 2008, Lausanne Page 14
  • 15. Conclusions Recommender Systems for learning have to be designed differently to recommender systems for e- commerce. Recommender Systems can support lifelong learners to follow more personalized learning paths. Further, they positively influence the time they need to reach their learning goals.
  • 16. Many thanks for your attention!  y y Sponsored by

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