01545nas a2200253   4500000000100000000000100001008004100002260001200043653002700055653002100082653002400103653001400127100002100141700002200162700001800184700002000202700001600222245012500238856009700363300000900460490000600469520080200475022001401277       2013                            d  c03/201310aRecommendation Systems10aLearning Objects10aMulti-Agent Systems10aEducation1 aPaula Rodríguez1 aValentina Tabares1 aNéstor Duque1 aDemetrio Ovalle1 aRosa Vicari00aBROA: An agent-based model to recommend relevant Learning Objects from Repository Federations adapted to learner profile  uhttp://www.ijimai.org/journal/sites/default/files/files/2013/03/ijimai20132_11_pdf_51705.pdf  a6-110 v23 aLearning 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.  a1989-1660