Using Topic Maps to Represent Learning Objects in a Learning Content Management System

This paper explores the use of Topic Maps to represent Learning Objects (LOs) in ROSA (Repository of Objects with Semantic Access for eLearning). ROSA is an e-learning system, which enables the creation, storage, reuse and management of LOs. A LO, characterized by a set of metadata descriptors, is a collection of reusable material used to support learning, i.e., an entity that can be digital or not, and can be used for learning, education, or training. ROSA system is the main focus of our study, where the benefits of the Topic Maps (TM) formalism are investigated to express a LO map with a higher level of semantic contextualization. Topic Maps is considered as a paradigm to improve information access and content organization, thus motivating its use as a candidate to represent information. In order to validate such aspects, a comparative analysis is done between ROSA and TM models, which made it possible to enrich the former with some new TM constructors and thus extending the original ROSA model. TM also helped in the creation and visualization of LO maps, which is done through the use of a graphic interface.