NDLI: A Utility-Based Semantic Recommender for Technology-Enhanced Learning

Content Provider	IEEE Xplore Digital Library
Author	Zielinski, A.
Copyright Year	2015
Description	Author affiliation: Fraunhofer IOSB, Karlsruhe, Germany (Zielinski, A.)
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 learner's 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.
Starting Page	394
Ending Page	396
File Size	137490
Page Count	3
File Format	PDF
e-ISBN	9781467373340
DOI	10.1109/ICALT.2015.120
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-07-06
Publisher Place	Taiwan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Ontologies Cognition Semantics Recommender systems Semantic Web Standards Mathematical model Utility-based Recommender Semantic Web Technologies Personalization Recommender Systems
Content Type	Text
Resource Type	Article

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