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A utility-based semantic recommender for technology-enhanced learning

 
: Zielinski, Andrea

:
Postprint urn:nbn:de:0011-n-3560801 (145 KByte PDF)
MD5 Fingerprint: d0f836c65a1a077d839b88176295a717
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Erstellt am: 18.8.2015


Institute of Electrical and Electronics Engineers -IEEE-; IEEE Computer Society:
15th IEEE International Conference on Advanced Learning Technologies, ICALT 2015 : July 6-9, 2015, Hualien, Taiwan
Piscataway, NJ: IEEE, 2015
ISBN: 978-1-4673-7334-0
S.394-396
International Conference on Advanced Learning Technologies (ICALT) <15, 2015, Hualien>
European Commission EC
FP7-ICT; 318496; INTUITEL
Englisch
Konferenzbeitrag, Elektronische Publikation
Fraunhofer IOSB ()
Web technologies; personalization; recommender systems; utility-based recommender

Abstract
In this paper, we present the design of a Knowledge-based recommender system for Technology Enhanced Learning based on Semantic Web Technologies. It uses a knowledge model for representing the current state of the learner, pedagogical strategies, and learning objects. To create a learner model, the learners’ activity and progress is tracked and higher-level learner features (i.e., Didactical Factors) are extracted. For a given learner state and set of pedagogical rules, the Recommendation Engine infers learning objects that lie on the learners personalized learning path. Furthermore, utility functions are used to compute a relevancy score for the best-fit learning objects. We describe the semantic-based recommendation approach on a conceptual level, discuss the strengths and weaknesses on the recommender framework and discuss future research.

: http://publica.fraunhofer.de/dokumente/N-356080.html