Ontology-based learning content recommendation

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 through 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 organise 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 systemised 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 Protege-2000 and its Protege Axiom language.