Model of Recommendation System for for Indexing and Retrieving the Learning Object based on Multiagent System

This paper proposes a multiagent system application model for indexing, retrieving and recommendation learning objects stored in different and heterogeneous repositories. The objects within these repositories are described by filled fields using different metadata 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 signification of the results we propose an information retrieval model based on the multiagent system approach and an ontological model to describe the knowledge domain covered. Keywords: AI in education, multi-agent systems, learning objects, recommendation systems.

[1]  Organización Internacional de Normalización.  ISO 15836 : Information and documentation -- The Dublin Core metadata element set , 2009 .

[2]  Susan Smith Nash,et al.  Learning Objects, Learning Object Repositories, and Learning Theory: Preliminary Best Practices for Online Courses , 2005 .

[3]  Steve Hitchcock,et al.  Using OAI-ORE to Transform Digital Repositories into Interoperable Storage and Services Applications , 2009 .

[4]  Jonas Vian,et al.  Multiagent System for Indexing and Retrieving Learning Objects , 2011, PAAMS.

[5]  Margarita Todorova,et al.  Learning objects , 2003, CompSysTech '03.

[6]  Michael Wooldridge,et al.  Introduction to multiagent systems , 2001 .

[7]  Brandon Muramatsu,et al.  Draft Standard for Learning Object Metadata , 2002 .

[8]  Paul Resnick,et al.  Recommender systems , 1997, CACM.

[9]  J. Euzenat,et al.  Ontology Matching , 2007, Springer Berlin Heidelberg.

[10]  Stephen Downes.  Learning Objects: Resources For Distance Education Worldwide , 2001 .

[11]  Gord McCalla,et al.  The Ecological Approach to the Design of E-Learning Environments: Purpose-based Capture and Use of Information About Learners , 2004 .

[12]  John Riedl,et al.  GroupLens: an open architecture for collaborative filtering of netnews , 1994, CSCW '94.

[13]  Michael J. Pazzani,et al.  A Framework for Collaborative, Content-Based and Demographic Filtering , 1999, Artificial Intelligence Review.

[14]  Fernando de la Prieta,et al.  Automatic Learning Object Extraction and Classification in Heterogeneous Environments , 2011, PAAMS.

[15]  David Wiley,et al.  Connecting learning objects to instructional design theory: A definition, a metaphor, and a taxonomy , 2000 .

[16]  John Riedl,et al.  Recommender systems in e-commerce , 1999, EC '99.

[17]  Rory McGreal.  Online Education Using Learning Objects , 2004 .

[18]  Raymond J. Mooney and Paul N. Bennett and Loriene Roy,et al.  Book Recommending Using Text Categorization with Extracted Information , 1998 .

[19]  Tao Huang,et al.  Research of web information retrieval based on metadata and OAI , 2008, 2008 IEEE International Conference on Granular Computing.