Agent-Based E-Learning Course Recommendation: Matching Learner Characteristics with Content Attributes

• Published in: International journal of computers & applications Vol. 25; no. 1; pp. 50 - 64
• Main Authors: Manouselis, N., Sampson, D.
• Format: Journal Article Conference Proceeding
• Language: English
• Published: Anaheim, CA Taylor & Francis 01.01.2003; Calgary, AB Acta Press; Zürich
• Subjects: Applied sciences; Artificial intelligence; Computer science; control theory; systems; Distributed computer systems; Electronics; Exact sciences and technology; Hardware; Intelligent agents for educational applications; intelligent technologies and education; Learning and adaptive systems; student modelling; Telecommunications; Telecommunications and information theory; Teleprocessing networks. Isdn; student modelling; Distance learning; Methodology; Multicriteria analysis; Decision making; Metadata; Distributed system; Modeling; Intelligent agent; Standards; Recommendation; Remote teaching; Intelligent agents for educational applications; Case study; Educational courses; intelligent technologies and education; Selection rule; Artificial intelligence
• ISSN: 1206-212X; 1925-7074
• DOI: 10.1080/1206212X.2003.11441685

In this article an agent framework for discovery and recommendation of e-learning courses in agent-based learning environments is presented. In the context of this framework, a way to model and store the learner model and the content attributes metadata information using international specifications and standards is studied, and two methodologies for constructing a matching mechanism to select courses most suited to the learner are introduced. These mechanisms are applied to the case of a learning community within the context of the NEMO project, and the results are discussed.