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The article first summarizes reasons why current approaches supporting Open Learning and Distance Education need to be complemented by tools permitting lecturers, researchers and students to cooperatively organize the semantic content of Learning related materials (courses, discussions, etc.) into a fine-grained shared semantic network. This first part of the article also quickly describes the approach adopted to permit such a collaborative work. Then, examples of such semantic networks are presented. Finally, an evaluation of the approach by students is provided and analyzed. | Knowledge Management E-Learning An International Journal Vol. 1 No. 2 103 Managing Knowledge to Enhance Learning Philippe A. Martin Eurecom France and Griffith University Australia Griffith Uni. - School of ICT - PMB 50 GcMC - QLD 9726 Australia E-mail kme @ phmartin.info. Corresponding author Abstract The article first summarizes reasons why current approaches supporting Open Learning and Distance Education need to be complemented by tools permitting lecturers researchers and students to cooperatively organize the semantic content of Learning related materials courses discussions etc. into a fine-grained shared semantic network. This first part of the article also quickly describes the approach adopted to permit such a collaborative work. Then examples of such semantic networks are presented. Finally an evaluation of the approach by students is provided and analyzed. Keywords Capacity management capacity constraint inventory level service level production management bottleneck. Biographical notes After his Ph.D. at the INRIA France and its postdoc at the University of Adelaide Australia Philippe A. Martin worked as senior researcher lecturer at Griffith University and the DSTC Australian W3C office . Since 2008 Dr Martin is project leader at Eurecom France . He mainly worked in knowledge representation and management. 1. Introduction Most Semantic Learning projects Stutt Motta 2004 Devedzic 2004 and most Learning Object related standards or practices Downes 2001 IEEE LTSC 2001 Hodgins 2006 Tane et al. 2003 rely on a rather coarse grained indexation of informal data natural language sentences images etc. by simple meta-data. Conceptual categories or even mere keywords are manually or automatically associated to relatively big chunks of informal data typically a whole document and almost always more than one sentence . In fine-grained approaches the data learning materials or very-detailed personalized user models are represented and organized into a formal or .