Learning Objects Recommendation System: Issues and Approaches for Retrieving, Indexing and Recomend Learning Objects

• Main Authors: Ricardo AZAMBUJA SILVEIRA, Rafaela LUNARDI COMARELLA, Ronaldo LIMA ROCHA CAMPOS, Jonas VIAN, Fernando DE LA PRIETA
• Format: Article
• Language: English
• Published: Ediciones Universidad de Salamanca 2016-07-01
• Series: Advances in Distributed Computing and Artificial Intelligence Journal
• Subjects: learning objects; repositories; search; recommendationsystems; multiagentsystems; ontology
• Online Access: https://revistas.usal.es/index.php/2255-2863/article/view/14830

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.