An Ontological Approach for Semantic-Aware Learning Object Retrieval

• Published in: Sixth IEEE International Conference on Advanced Learning Technologies (ICALT'06) pp. 208 - 210
• Main Authors: Ming-Che Lee, Kun Hua Tsai, Wang, T.I.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 2006
• Subjects: Electronic learning; Encoding; Engineering management; Indexing; Inference algorithms; Information retrieval; Intelligent networks; Laboratories; Ontologies; Space technology
• ISBN: 9780769526324; 0769526322
• ISSN: 2161-3761
• DOI: 10.1109/ICALT.2006.1652407

SCORM LOM (sharable courseware object reference model learning object metadata) , enables the indexing, location, management, and searching of learning objects in a learning object repository by extended sharing and searching features. However, LOM has a deficiency in semantic-awareness capability. In most information retrieval systems, users' cognitive ability on what they need is a basic system assumption. However, in e-learning systems, users may have no idea on what they want and what the learning objects' metadata is. This paper presents an ontological approach for semantic-aware learning object retrieval. This approach is generic enough to be embedded to other LOM-based search mechanisms to achieve semantic-aware learning object retrieval