Learning Content Recommendation Service Based-On Simple Sequencing Specification

Abstract

A new era of e-learning is on the horizon, hundreds of Learning Contents are created and more and more people begin to acquire acknowledge thru e-learning. The traditional teaching method is already showing its limitations that students from different backgrounds are still given the same contents at the same time, and they may only interest in part of a whole learning content. In this paper, we propose a novel way to organize learning contents into small "atomic" units called Learning Objects so that they could be used and reused effectively. The Learning Objects together with their ontology are systemized into knowledge base. An intelligent recommendation mechanism based on sequencing rules is then introduced with detail, where the rules are formed from the knowledge base and competency gap analysis. Finally we establish a test knowledge base system, using and extending the ontology editor Protégé-2000 and its Protégé Axiom Language. © Springer-Verlag 2004.