An Evidential Reasoning Approach for Learning Object Recommendation with Uncertainty

Selecting the most suitable learning object in SCORM-compliant learning object recommendation system is a complex decision process. We exploit the techniques of collaborative concept map design, ontology explaining, an evidence reasoning that may be use to deal with uncertain decision making, an evaluation analysis model and the evidence combination rule of the Dempster-Shafer theory for supporting the system. Two combination algorithms have been developed in this approach for combining multiple uncertain subjective judgments. Based on this approach and the traditional multiple attribute decision making method, a recommendation procedure is proposed to rank the most suitable learning objects over learner preferences to a specific learner. A learning object raking example is discussed to demonstrate the method implementation based on multi-agent framework.