Learning objects recommendation system: issues and approaches for retrieving, indexing and recomend learning objects

• AZAMBUJA SILVEIRA, Ricardo ^[3] ; LUNARDI COMARELLA, Rafaela ^[3] ; LIMA ROCHA CAMPOS, Ronaldo ^[1] ; VIAN, Jonas ^[3] ; DE LA PRIETA, Fernando ^[2]
  1. [1] Instituto Federal Catarinense

     Instituto Federal Catarinense

     Brasil

     
  2. [2] Universidad de Salamanca

     Universidad de Salamanca

     Salamanca, España

     
  3. [3] University of Santa Catarina

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• Localización: ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, ISSN-e 2255-2863, Vol. 4, Nº. 4, 2015, págs. 69-82
• Idioma: inglés
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• Resumen

  • This paper discusses some important issues regarding the the management of Learning objects covering searching over repositories and different approaches of recommendation systems and presents a multiagent system based application model for indexing, retrieving and recommending learning objects stored in different and heterogeneous repositories. The objects within these repositories are described by filled fields using different metadata (data about data) 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 relevance of the results we propose an information retrieval model based on a multiagent system approach and an ontological model to describe the covered knowledge domain.