Adaptive Learning through Conceptual Lattice-based SCORM Meta-Objects

When dealing with computer-mediated adaptive learning, conceptual relationships among learning objects inside a repository are essential to provide dynamic creation of learning resources. Such creation could be performed through a proper selection of learning objects according to a wide range of factors, which can vary from students´ previous knowledge through individual learning styles. This problem becomes harder when learning objects to be retrieved are not physically located inside a sole repository, leading to federated searches through a pool of repositories that follow some well-established standards like SCORM. SCORM is a set of standards that allows learning objects to be modeled in order to be interoperable and reusable through repositories and learning management systems. However, in spite of its generality, SCORM does not propose any way to provide conceptual relationships among learning objects. This paper presents an alternative architecture for using SCORM to provide an effective mapping of conceptual relationships among learning objects. It is shown a navigational architecture through SCORM objects based on Conceptual Lattice Theory. Conceptual lattices are used to formalize conceptual structures, being a natural way to specify learning objects. XLink-based semantically structured links and annotations are proposed over SCORM objects, which would allow a customized point-to-point, non-linear navigation through them