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

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. 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. 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
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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. Recommender Systems
in TEL
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6. The TEL recommender
are a bit like this...
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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
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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/
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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/
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10. Survey on TEL Recommender
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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.
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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.
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13. How others compare their
recommenders
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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.
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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
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16. dataTEL::Pressing topics
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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
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18. dataTEL::Grand Challenges
1. Contextualisation AND 2. Connecting Learner
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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).
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20. Evaluation of TEL recommender
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21. Evaluation of TEL recommender
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22. Join us for a Coffee ...
http://www.teleurope.eu/pg/groups/9405/datatel/
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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
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