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How Recommender Systems in
Technology-Enhanced Learning
depend on ContextHendrik Drachsler Open University of the Netherlands
Nikos ManouselisGreek ResearchTechnology Network
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TEL context
Figure by : Cross, J. (2006). Informal learning: Rediscovering the natural pathways that inspire innovation and performance. San Francisco, CA: Pfeiffer.
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Formal learning
Figure by:Cross, J. (2006)
• are learning offers from educational institutions.
• is imbedded into a curriculum or syllabus framework.
• is highly structured.• leads to a specific accreditation.• involves domain experts to guarantee quality.
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Formal learning = structured layers
Generic layers within a simplified architecture of an educational AEH(Karampiperis & Sampson, 2005)
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Informal learning• content is provide from different sources.
• happens outside formal educational settings (e.g. related to work or leisure time.
• is less structured (in terms of learning goals, study time or learning support).
• does not lead to a certain accreditation.
Figure by:Cross, J. (2006)
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Informal learning = emergence
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Context variables Formal learning Curriculum (Closed-Corpus) Teacher directed Predefined learning resources, learning goals Maintenance
Informal learning Learning resources from different providers (Open-Corpus)
More self-directed learning goals
Responsible for own learning pace / path
Lack of maintenance
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Recommendation approaches
Learning settings, environmental conditions and the task greatly affect the design of systems in TEL.
Learning settings, environmental conditions and the task greatly affect the design of recommender systems in TEL.
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Formal recommendation approach
Content Layer
Conceptual Layer
Learning Goal LayerAdaptiveSequencing(top-down)
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Informal recommendation approach
Content Layer
Clustering Layer 1..n
Clustering Layer nHierarchicalclustering(bottom-up)
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Research question
How can we get the best out of both worlds?
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A solution for formal learning
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• 100 user (hopefully some more after today )
• 20000 Web 2.0 items (increasing every hour)
• 700 ratings in the data base
• 10000 tags in the data base
A solution for informal learning
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Version 1.0
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Version 1.0
DUINE Prediction Engine
Database of Items
User Interface
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How does it work?
Cold-Start = Tag-based recommendation
Collaborative Filtering with ratings
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Learner profile
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Conclusions
Fed by bottom-upapproach
Fed by top-downapproach
How tocombine?
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Future R&D
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Many thanks for your interest!
This slide is available here:http://www.slideshare.com/Drachsler
Email: [email protected]: celstec-hendrik.drachslerBlogging at: http://elgg.ou.nl/hdr/weblogTwittering at: http://twitter.com/HDrachsler
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