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The amount of digital educational content that is continually created makes the repositories of educational materials increasingly large and the search for speciĄc and targeted content is increasing. Thus, a study was carried out based on learning objects generated in a repository. The IEEE LOM standard was used to generate groups of learning objects that could be used for a given student proĄle. It is a necessity that these objects be organized so that their recommendation is more eicient. Thus, this paper presents a proposal that uses clustering techniques to group educational content into repositories based on student learning styles. Initially a comparative analysis was performed between grouping algorithms with dummy data in which good results were obtained. Based on these results, a case study using real data from students in an educational system was discussed and implemented.

Uma Abordagem para Análise Automática de Repositórios de Objetos de Aprendizagem Através de Técnicas de Aprendizagem de Máquina para Apoio a Recomendação Personalizada de Conteúdo em Sistemas Adaptativos e Inteligentes para Educação