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dataTEL - Datasets for Recommender Systems in Technology-Enhanced Learning

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dataTEL - Datasets for Recommender Systems in Technology-Enhanced Learning

  1. 1. dataTEL - Datasets for Recommender Systems in Technology-Enhanced Learning 29.03.2011 dataTEL workshop at the ARV2011, La Clusaz, France picture by Tom Raftery http://www.flickr.com/photos/traftery/4773457853/sizes/l Hendrik Drachsler #dataTEL11 Centre for Learning Sciences and Technology @ Open University of the Netherlands 1 MAVSEL
  2. 2. dataTEL - Datasets for Recommender Systems in Technology-Enhanced Learning 29.03.2011 dataTEL workshop at the ARV2011, La Clusaz, France Free the data picture by Tom Raftery http://www.flickr.com/photos/traftery/4773457853/sizes/l Hendrik Drachsler #dataTEL11 Centre for Learning Sciences and Technology @ Open University of the Netherlands 1 MAVSEL
  3. 3. Who is dataTEL ? dataTEL is a Theme Team funded by the STELLAR network of excellence Riina Stephanie Katrien Nikos Martin Hendrik Vuorikari Lindstaedt Verbert Manouselis Wolpers Drachsler 2
  4. 4. Who is dataTEL ? dataTEL is a Theme Team funded by the STELLAR network of excellence Riina Stephanie Katrien Nikos Martin Hendrik Vuorikari Lindstaedt Verbert Manouselis Wolpers Drachsler MAVSEL CEN PT Social Data Miguel Joris Angel Sicillia Klerkx2
  5. 5. Recommender Systems in TEL 3
  6. 6. The TEL recommender are a bit like this... 4
  7. 7. The TEL recommender are a bit like this... We need to design for each domain an appropriate recommender system that fits the goals, tasks, and particular constraints 4
  8. 8. But... “The performance results of different research efforts in TEL recommender systems are hardly comparable.” (Manouselis et al., 2010) Kaptain Kobold http://www.flickr.com/photos/ kaptainkobold/3203311346/ 5
  9. 9. But... The TEL recommender “The performance results experiments lack of different research transparency. They need efforts in TEL to be repeatable to test: recommender systems are hardly comparable.” • Validity • Verificationet al., 2010) (Manouselis • Compare results Kaptain Kobold http://www.flickr.com/photos/ kaptainkobold/3203311346/ 5
  10. 10. Survey on TEL Recommender 6
  11. 11. Survey on TEL Recommender Manouselis, N., Drachsler, H., Vuorikari, R., Hummel, H. G. K., & Koper, R. (2011). Recommender Systems in Technology Enhanced Learning. In P. B. Kantor, F. Ricci, L. Rokach, & B. Shapira (Eds.), Recommender Systems Handbook (pp. 387-415). Berlin: Springer. 6
  12. 12. Survey on TEL Recommender The continuation of small-scale experiments with a limited amount of learners that rate the relevance of suggested resources only adds little contributions to a evidence driven knowledge base on recommender systems in TEL. Manouselis, N., Drachsler, H., Vuorikari, R., Hummel, H. G. K., & Koper, R. (2011). Recommender Systems in Technology Enhanced Learning. In P. B. Kantor, F. Ricci, L. Rokach, & B. Shapira (Eds.), Recommender Systems Handbook (pp. 387-415). Berlin: Springer. 6
  13. 13. How others compare their recommenders 7
  14. 14. dataTEL::Collection Drachsler, H., Bogers, T., Vuorikari, R., Verbert, K., Duval, E., Manouselis, N., Beham, G., Lindstaedt, S., Stern, H., Friedrich, M., & Wolpers, M. (2010). Issues and Considerations regarding Sharable Data Sets for Recommender Systems in Technology Enhanced Learning. Presentation at the 1st Workshop Recommnder Systems in Technology Enhanced Learning (RecSysTEL) in conjunction with 5th European Conference on Technology Enhanced Learning (EC-TEL 2010): Sustaining TEL: From Innovation to Learning and Practice. September, 28, 2010, Barcelona, Spain. 8
  15. 15. dataTEL::Evaluation Verbert, K., Duval, E., Drachsler, H., Manouselis, N., Wolpers, M., Vuorikari, R., Beham, G. (2011). Dataset- driven Research for Improving Recommender Systems for Learning. Learning Analytics & Knowledge: February 27-March 1, 2011, Banff, Alberta, Canada 9
  16. 16. dataTEL::Pressing topics 10
  17. 17. dataTEL::Pressing topics 1. Evaluation of recommender systems in TEL 2. Data supported learning examples 3. Datasets from Learning Object Repositories and Web content 4. Privacy and data protection for dataTEL 10
  18. 18. dataTEL::Grand Challenges 1. Contextualisation AND 2. Connecting Learner 11
  19. 19. dataTEL::Grand Challenges 1. Contextualisation AND 2. Connecting Learner Recommender technologies are promising to match users on defined characteristics and create a kind ‘neighborhood’ of like-minded users (Context). In that way, recommender systems extract contextual information and offer valuable data to suggest suitable peer learners (Connecting Learners). 11
  20. 20. Evaluation of TEL recommender 12
  21. 21. Evaluation of TEL recommender 12
  22. 22. Join us for a Coffee ... http://www.teleurope.eu/pg/groups/9405/datatel/ 13
  23. 23. Many thanks for your interests 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 14

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