Checking semantic consistency of SCORM like learning objects

Semantic metadata are not yet fully integrated in most learning metadata profiles. Moreover, when it is supported it is only used to improve the quality of results returned by search engines in LOR (learning object repositories) and LMS (Learning Management Systems). Within the framework of APOGE¹ project, we want to demonstrate the usability of semantic metadata, when they are provided, during the authoring phase to assist authors to improve the consistency of new designed learning objects. The added value of our approach is maximized in the case of SCORM (Sharable Content Object Reference Model) like learning objects' authoring. Indeed, when we have learning objects designed by reuse of existing ones there is risks to have weaknesses mainly due to a bad understanding of the true nature of the reused objects. To overcome those risks we have defined an automated authoring assistance approach. In this paper we put the emphasis on two semantic based methods used in our approach: semantic consistency checking and learning object's semantic space analysis.