Learning Objects Recommendation System: Issues and Approaches for Retrieving, Indexing and Recomend Learning Objects
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 stor...
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Ediciones Universidad de Salamanca
2016-07-01
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Online Access: | https://revistas.usal.es/index.php/2255-2863/article/view/14830 |
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doaj-a3ace4bdd1574ca38e8fe5b57f86ee102020-11-25T03:06:37ZengEdiciones Universidad de SalamancaAdvances in Distributed Computing and Artificial Intelligence Journal2255-28632016-07-0144698210.14201/ADCAIJ201544698213395Learning Objects Recommendation System: Issues and Approaches for Retrieving, Indexing and Recomend Learning ObjectsRicardo AZAMBUJA SILVEIRA0Rafaela LUNARDI COMARELLA1Ronaldo LIMA ROCHA CAMPOS2Jonas VIAN3Fernando DE LA PRIETA4University of SalamancaUniversity of Santa CatarinaInstituto Federal CatarinenseUniversity of Santa CatarinaUniversity of SalamancaThis 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.https://revistas.usal.es/index.php/2255-2863/article/view/14830learning objectsrepositoriessearchrecommendationsystemsmultiagentsystemsontology |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Ricardo AZAMBUJA SILVEIRA Rafaela LUNARDI COMARELLA Ronaldo LIMA ROCHA CAMPOS Jonas VIAN Fernando DE LA PRIETA |
spellingShingle |
Ricardo AZAMBUJA SILVEIRA Rafaela LUNARDI COMARELLA Ronaldo LIMA ROCHA CAMPOS Jonas VIAN Fernando DE LA PRIETA Learning Objects Recommendation System: Issues and Approaches for Retrieving, Indexing and Recomend Learning Objects Advances in Distributed Computing and Artificial Intelligence Journal learning objects repositories search recommendationsystems multiagentsystems ontology |
author_facet |
Ricardo AZAMBUJA SILVEIRA Rafaela LUNARDI COMARELLA Ronaldo LIMA ROCHA CAMPOS Jonas VIAN Fernando DE LA PRIETA |
author_sort |
Ricardo AZAMBUJA SILVEIRA |
title |
Learning Objects Recommendation System: Issues and Approaches for Retrieving, Indexing and Recomend Learning Objects |
title_short |
Learning Objects Recommendation System: Issues and Approaches for Retrieving, Indexing and Recomend Learning Objects |
title_full |
Learning Objects Recommendation System: Issues and Approaches for Retrieving, Indexing and Recomend Learning Objects |
title_fullStr |
Learning Objects Recommendation System: Issues and Approaches for Retrieving, Indexing and Recomend Learning Objects |
title_full_unstemmed |
Learning Objects Recommendation System: Issues and Approaches for Retrieving, Indexing and Recomend Learning Objects |
title_sort |
learning objects recommendation system: issues and approaches for retrieving, indexing and recomend learning objects |
publisher |
Ediciones Universidad de Salamanca |
series |
Advances in Distributed Computing and Artificial Intelligence Journal |
issn |
2255-2863 |
publishDate |
2016-07-01 |
description |
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. |
topic |
learning objects repositories search recommendationsystems multiagentsystems ontology |
url |
https://revistas.usal.es/index.php/2255-2863/article/view/14830 |
work_keys_str_mv |
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1724673368059281408 |