A service-based framework for personalized Learning Objects retrieval and recommendation

With vigorous development of Internet, especially the web page interaction technology, distant e-learning has become more and more realistic and popular. To solve the problems of sharing and reusing teaching materials in different e-learning systems, presently several standard formats, including SCORM, IMS, LOM, and AICC, etc., have been proposed by several different international organizations. SCORM LOM, i.e. the Learning Object Metadata, enables the indexing and searching of learning objects in a learning object repository by extended sharing and searching features. However, LOM is deficient in semantic-awareness operations in spite of its multifarious fields in describing a Learning Object. It is difficult for a learner, even for advanced learners, to completely specify so many terms when they are searching. This paper proposes a service-based framework for personalized learning objects retrieval and recommendation. The work of personalization harnesses the power of probabilistic semantic inference for query keywords, LOM-based user preference logging, and other users' feedback for recommendation weighting to retrieve the most suitable learning object for users. An ontology-based query expansion algorithm and an integrated learning objects recommendation algorithm are also proposed,