Learning objects recommendation systemissues and approaches for retrieving, indexing and recomend learning objects

  1. AZAMBUJA SILVEIRA, Ricardo 1
  2. LUNARDI COMARELLA, Rafaela 1
  3. LIMA ROCHA CAMPOS, Ronaldo 2
  4. VIAN, Jonas 1
  5. DE LA PRIETA, Fernando 3
  1. 1 University of Santa Catarina
  2. 2 Instituto Federal Catarinense
    info

    Instituto Federal Catarinense

    Blumenau, Brasil

    ROR https://ror.org/02f8h1m78

  3. 3 Universidad de Salamanca
    info

    Universidad de Salamanca

    Salamanca, España

    ROR https://ror.org/02f40zc51

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Revista:
ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal

ISSN: 2255-2863

Año de publicación: 2015

Volumen: 4

Número: 4

Páginas: 69-82

Tipo: Artículo

DOI: 10.14201/ADCAIJ2015446982 DIALNET GOOGLE SCHOLAR lock_openAcceso abierto editor

Otras publicaciones en: ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal

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.

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