A Learning Objects Recommendation Model based on the Preference and Ontological Approaches

With vigorous development of Internet, especially the web page interaction technology, distant e-learning has become more and more realistic and popular. Digital courses may consist of many learning units or learning objects and, currently, many learning objects are created according to SCORM standard. It can be seen that, in the near future, a vast amount of SCORM-compliant learning objects will be published and distributed cross the Internet. Facing the huge volume of learning objects, learners will be lost in selecting suitable and favorite learning objects. In this paper an adaptive personalized ranking mechanism is proposed to help recommend SCORM-compliant learning objects from repositories in the Internet. The mechanism uses both preference-based and neighbor-interest-based approaches in ranking the degree of relevance of learning objects to a user’s intension. By this model, a tutoring system is able to provide easily and efficiently for active learners suitable learning objects.