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dataTEL - Datasets for Recommender Systems in Technology-Enhanced Learning

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Grand Challenges of the dataTEL Theme Team at the Alpine-Rendez-Vous 2011, dataTEL workshop at the ARV2011, La Clusaz, France

Grand Challenges of the dataTEL Theme Team at the Alpine-Rendez-Vous 2011, dataTEL workshop at the ARV2011, La Clusaz, France

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  • 1. Sets for Recommender Systems in TELA Data Set dataTEL - Datasets for Recommender Systems in Technology-Enhanced Learning 29.03.2011 dataTEL workshop at the ARV2011, La Clusaz, Francepicture by Tom Raftery http://www.flickr.com/photos/traftery/4773457853/sizes/lHendrik Drachsler #dataTEL11Centre for Learning Sciences and Technology@ Open University of the Netherlands1 MAVSEL
  • 2. Sets for Recommender Systems in TELA Data Set dataTEL - Datasets for Recommender Systems in Technology-Enhanced Learning 29.03.2011 dataTEL workshop at the ARV2011, La Clusaz, France Free the datapicture by Tom Raftery http://www.flickr.com/photos/traftery/4773457853/sizes/lHendrik Drachsler #dataTEL11Centre for Learning Sciences and Technology@ Open University of the Netherlands1 MAVSEL
  • 3. Who is dataTEL ? dataTEL is a Theme Team funded by the STELLAR network of excellence Riina Stephanie Katrien Nikos Martin HendrikVuorikari Lindstaedt Verbert Manouselis Wolpers Drachsler 2
  • 4. Who is dataTEL ? dataTEL is a Theme Team funded by the STELLAR network of excellence Riina Stephanie Katrien Nikos Martin HendrikVuorikari Lindstaedt Verbert Manouselis Wolpers Drachsler MAVSEL CEN PT Social Data Miguel JorisAngel Sicillia Klerkx2
  • 5. Recommender Systems in TEL 3
  • 6. The TEL recommender are a bit like this... 4
  • 7. The TEL recommender are a bit like this... We need to design for each domain anappropriate recommender system that fits the goals, tasks, and particular constraints 4
  • 8. But...“The performance resultsof different researchefforts in TELrecommender systemsare hardly comparable.”(Manouselis et al., 2010) Kaptain Kobold http://www.flickr.com/photos/ kaptainkobold/3203311346/ 5
  • 9. But...The TEL recommender“The performance resultsexperiments lackof different researchtransparency. They needefforts in TELto be repeatable to test:recommender systemsare hardly comparable.”• Validity• Verificationet al., 2010)(Manouselis• Compare results Kaptain Kobold http://www.flickr.com/photos/ kaptainkobold/3203311346/ 5
  • 10. Survey on TEL Recommender 6
  • 11. Survey on TEL RecommenderManouselis, N., Drachsler, H., Vuorikari, R., Hummel, H. G. K., & Koper, R. (2011). Recommender Systemsin Technology Enhanced Learning. In P. B. Kantor, F. Ricci, L. Rokach, & B. Shapira (Eds.), RecommenderSystems Handbook (pp. 387-415). Berlin: Springer. 6
  • 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 Systemsin Technology Enhanced Learning. In P. B. Kantor, F. Ricci, L. Rokach, & B. Shapira (Eds.), RecommenderSystems Handbook (pp. 387-415). Berlin: Springer. 6
  • 13. How others compare their recommenders 7
  • 14. dataTEL::CollectionDrachsler, 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 DataSets for Recommender Systems in Technology Enhanced Learning. Presentation at the 1st WorkshopRecommnder Systems in Technology Enhanced Learning (RecSysTEL) in conjunction with 5th EuropeanConference on Technology Enhanced Learning (EC-TEL 2010): Sustaining TEL: From Innovation to Learningand Practice. September, 28, 2010, Barcelona, Spain. 8
  • 15. dataTEL::EvaluationVerbert, 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. dataTEL::Pressing topics 10
  • 17. dataTEL::Pressing topics1. Evaluation of recommender systems in TEL2. Data supported learning examples3. Datasets from Learning Object Repositories and Web content4. Privacy and data protection for dataTEL 10
  • 18. dataTEL::Grand Challenges1. Contextualisation AND 2. Connecting Learner 11
  • 19. dataTEL::Grand Challenges1. 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. Evaluation of TEL recommender 12
  • 21. Evaluation of TEL recommender 12
  • 22. Join us for a Coffee ...http://www.teleurope.eu/pg/groups/9405/datatel/ 13
  • 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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