Towards a Better Understanding of Learning Objects Content

Usually semantic metadata are introduced to support effective search of relevant learning objects from LOR (Learning Object Repositories). We propose to use them to assist authors of SCORM (Sharable Content Object Reference Model) like learning objects. In fact, when we design new learning objects by reuse of existing ones, there are 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 focus especially on how we use semantic metadata to produce indicators about the content of learning objects, more precisely about their complexity, heterogeneity and imbalance from a semantic perspective. To attend this specific objective we introduce the notion of "semantic space" and "semantic component". Then we use them to compute metrics which are aggregated together to produce meaningful indicators.