BROA: An agent-based model to recommend relevant Learning Objects from Repository Federations adapted to learner profile

TitleBROA: An agent-based model to recommend relevant Learning Objects from Repository Federations adapted to learner profile
Publication TypeJournal Article
Year of Publication2013
AuthorsRodríguez, P. A., V. Tabares, N. D. Duque, D. A. Ovalle, and R. M. Vicari
JournalInternational Journal of Interactive Multimedia and Artificial Intelligence
ISSN1989-1660
IssueSpecial Issue on Artificial Intelligence and Social Application
Volume2
Number1
Date Published03/2013
Pagination6-11
Abstract

Learning Objects (LOs) are distinguished from traditional educational resources for their easy and quickly availability through Web-based repositories, from which they are accessed through their metadata. In addition, having a user profile allows an educational recommender system to help the learner to find the most relevant LOs based on their needs and preferences. The aim of this paper is to propose an agent-based model so-called BROA to recommend relevant LOs recovered from Repository Federations as well as LOs adapted to learner profile. The model proposed uses both role and service models of GAIA methodology, and the analysis models of the MAS-CommonKADS methodology. A prototype was built based on this model and validated to obtain some assessing results that are finally presented.

KeywordsEducation, Learning Objects, Multi-Agent Systems, Recommendation Systems
DOI10.9781/ijimai.2013.211
URLhttp://www.ijimai.org/journal/sites/default/files/files/2013/03/ijimai20132_11_pdf_51705.pdf
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IJIMAI20132_11.pdf693.89 KB