PhD Defense: Navigation Support for Lerners in Informal Learning Networks
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Hendrik Drachsler defended his PhD thesis on developing recommender systems to help learners find personalized paths through informal learning networks. His research included a theoretical background study, a psychology experiment, and a simulation of learning networks. The goal was to address the problem of learners feeling overwhelmed by the large amount of information in learning networks by creating a recommender system prototype that could suggest personalized learning activities and paths based on a learner's interests and previous activities. The conclusion was that recommender systems designed specifically for learning networks can help lifelong learners more efficiently pursue personalized learning goals.
PhD Defense: Navigation Support for Lerners in Informal Learning Networks
- 2. Networked Knowledge Society g y
- 3. Learning = Knowledge Society Knowledge Society g y
- 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