SWA-KMDLS: An Enhanced e-Learning Management System Using Semantic Web and Knowledge Management Technology

In this era of knowledge economy in which knowledge have become the most precious
resource, surveys have shown that e-Learning has been on the increasing trend in various
organizations including, among others, education and corporate. The use of e-Learning is
not only aim to acquire knowledge but also to maintain competitiveness and advantages
for individuals or organizations. However, the early promise of e-Learning has yet to be
fully realized, as it has been no more than a handout being published online, coupled with
simple multiple-choice quizzes. The emerging of e-Learning 2.0 that is empowered by
Web 2.0 technology still hardly overcome common problem such as information
overload and poor content aggregation in a highly increasing number of learning objects
in an e-Learning Management System (LMS) environment.
The aim of this research study is to exploit the Semantic Web (SW) and Knowledge
Management (KM) technology; the two emerging and promising technology to enhance
the existing LMS. The proposed system is named as Semantic Web Aware-Knowledge
Management Driven e-Learning System (SWA-KMDLS). An Ontology approach that is
the backbone of SW and KM is introduced for managing knowledge especially from
learning object and developing automated question answering system (Aquas) with
expert locator in SWA-KMDLS. The METHONTOLOGY methodology is selected to
develop the Ontology in this research work.
The potential of SW and KM technology is identified in this research finding which will
benefit e-Learning developer to develop e-Learning system especially with social
constructivist pedagogical approach from the point of view of KM framework and SW
environment. The (semi-) automatic ontological knowledge base construction system
(SAOKBCS) has contributed to knowledge extraction from learning object semiautomatically
whilst the Aquas with expert locator has facilitated knowledge retrieval
that encourages knowledge sharing in e-Learning environment.
The experiment conducted has shown that the SAOKBCS can extract concept that is the
main component of Ontology from text learning object with precision of 86.67%, thus
saving the expert time and effort to build Ontology manually. Additionally the
experiment on Aquas has shown that more than 80% of users are satisfied with answers
provided by the system. The expert locator framework can also improve the performance
of Aquas in the future usage.
Keywords: semantic web aware – knowledge e-Learning Management System (SWAKMDLS),
semi-automatic ontological knowledge base construction system (SAOKBCS),
automated question answering system (Aquas), Ontology, expert locator.