Title :
A Utility-Based Semantic Recommender for Technology-Enhanced Learning
Author :
Andrea Zielinski
Author_Institution :
Fraunhofer IOSB, Karlsruhe, Germany
fDate :
7/1/2015 12:00:00 AM
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.
Keywords :
"Ontologies","Cognition","Semantics","Recommender systems","Semantic Web","Standards","Mathematical model"
Conference_Titel :
Advanced Learning Technologies (ICALT), 2015 IEEE 15th International Conference on
DOI :
10.1109/ICALT.2015.120