No abstract

This paper proposes a multiagent system application model for indexing and retrieving learning objects stored in different and heterogeneous repositories. The objects within these repositories are described by filled fields using different metadata standards. The searching mechanism covers several different learning object repositories and the same object can be described in these repositories by the use of different types of fields. Aiming to improve accuracy and coverage in terms of recovering a learning object we propose an information retrieval model based on the multiagent system approach and an ontological model to describe the knowledge domain covered.

A evolução das tecnologias de informação e comunicação e o crescimento substancial da educação a distância aumentaram a utilização de diferentes mídias e recursos utilizados na estruturação de um modelo de curso. A escolha adequada da estrutura dos cursos é fundamental para a motivação e compreensão do conteúdo pelos estudantes. O presente artigo apresenta os conceitos relacionados à escolha do modelo de um curso na modalidade a distância,  ilustrado por meio de um estudo de caso. Este estudo evidencia a necessidade de planejamento na dinâmica  hipertextual e hipermidiática, bem como a importância da interoperabilidade de conteúdos e de estratégias alternativas para a realização de atividades práticas nos cursos a distância.

This paper proposes a multiagent system application model for indexing, retrieving and recommendation learning objects stored in different and heterogeneous repositories. The objects within these repositories are described by filled fields using different metadata standards. The searching mechanism covers several different learning object repositories and the same object can be described in these repositories by the use of different types of fields. Aiming to improve accuracy and coverage in terms of recovering a learning object and improve the signification of the results we propose an information retrieval model based on the multiagent system approach and an ontological model to describe the knowledge domain covered.Keywords: AI in education, multi-agent systems, learning objects, recommendation systems.