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.

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