2024-03-28T21:39:43Zhttps://gredos.usal.es/oai/requestoai:gredos.usal.es:10366/1305292022-02-07T16:12:08Zcom_10366_127389com_10366_122682com_10366_4666com_10366_3823col_10366_130521
2016-09-28T07:37:07Z
urn:hdl:10366/130529
Learning Objects Recommendation System: Issues and Approaches for Retrieving, Indexing and Recomend Learning Objects
Silveira, Ricardo Azambuja
Comarella, Rafaela Lunardi
Campos, Ronaldo Lima Rocha
Vian, Jonas
Prieta Pintado, Fernando de la
Computación
Informática
Computing
Information Technology
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.
2016-09-28T07:37:07Z
2016-09-28T07:37:07Z
2015-12-24
info:eu-repo/semantics/article
info:eu-repo/semantics/article
ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 4 (2015)
2255-2863
http://hdl.handle.net/10366/130529
eng
https://creativecommons.org/licenses/by-nc-nd/3.0/
info:eu-repo/semantics/openAccess
Attribution-NonCommercial-NoDerivs 3.0 Unported
Ediciones Universidad de Salamanca (España)