Methodology for recommendation and aggregation of Learning Objects in SCORM

From a literature review about the composition of educational content for e-Learning in accordance with SCORM, we noticed that, although widely used, the SCORM metadata model for content aggregation is still complex and difficult to be used by educators, content developers and instructional designers. Particularly, the identification of contents related with each other, in large repositories, has been the focus of considerable efforts by researchers in the field of computing in pursuit of the automation of this process. However, previous approaches have extended or altered the metadata defined by SCORM standard. In this paper, we present experimental results on our proposed methodology which employs ontologies, automatic annotation of metadata, information retrieval and text mining to recommend and aggregate related content, using the relation metadata category as defined by SCORM, without extending these metadata, or changing SCORM, or even developing specific implementations on a Learning Management System. We developed a computer system prototype which applies the proposed methodology on a sample of learning objects generating results to evaluate its efficacy. The results were analyzed and evaluated with the support of educators, who work on the development of content for e-Learning, demonstrating that the proposed method is feasible and effective to produce the expected results.