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Ideally, learning resources should be built over a shared pool of fine reusable granular learning objects. However, in order to avoid contextual lacks, dynamic creation of such resources would mostly rely on the conceptual relationships among learning objects inside a repository. These conceptual relationships, as well as the learning objects creation, are best established if students’ learning styles are considered. Common standards like SCORM do not have tools to provide conceptual relationships among fine granular learning objects. This paper presents an architecture to navigate through a SCORM objects net via conceptual lattices with dynamical graph navigational transformations. The theory of partially ordered sets and lattices has been successfully applied to the modelling of hierarchical systems and has produced many contributions in several computational areas as Artificial Intelligence, Category Theory, Semantics of Programming Languages and Concurrency Theory. These lattices allow navigation among objects and attributes in a bi-directional way. The architecture is based on the introduction of annotations and links via XLink technology that is highly applied to integrate XML documents. The annotations and links produce a low impact on the current SCORM structure and make possible the building of complex SCORM objects nets through simple constructions. Links among objects could be endowed with qualified semantic processing. Besides, they allow the abstraction of connections as aspects among the manifest files associated with the learning objects and styles. The approach used in this paper for learning styles respects some learners’ individual characteristics even if it could be considered a simplistic form to face learning styles. Furthermore, specific learning styles effectively guide a dynamic graph navigational transformation.
Keywords:
learning objects, conceptual lattices, SCORM, granularity
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