January 2012 Volume 15 Number 1 - Educational Technology ...

Hsu, I.-C. (2012). Intelligent Discovery for Learning Objects Using Semantic Web Technologies. Educational Technology &
Society, 15 (1), 298–312.
Intelligent Discovery for Learning Objects Using Semantic Web Technologies
I-Ching Hsu
Department of Computer Science and Information Engineering, National Formosa University, Taiwan //
hsuic@nfu.edu.tw
ABSTRACT
The concept of learning objects has been applied in the e-learning field to promote the accessibility, reusability,
and interoperability of learning content. Learning Object Metadata (LOM) was developed to achieve these goals
by describing learning objects in order to provide meaningful metadata. Unfortunately, the conventional LOM
lacks the computer interpretability needed to support knowledge representation when searching for finding
relevant learning objects. This study addresses this issue by defining a Multi-layered Semantic LOM Framework
(MSLF) for integrating Semantic Web technologies into LOM. The proposed MSLF is used to develop
LOFinder, an intelligent LOM shell that provides an alternative approach to enhancing the knowledge
representations. To test its feasibility, this study implemented a java-based prototype of LOFinder that enables
intelligent discovery of learning objects.
Keywords
LOM, Semantic Web, Ontology, RuleML, SCORM
Introduction
The Sharable Content Object Reference Model (SCORM) (ADL 2006) provides specifications for implementing elearning
systems and enabling learning object reusability and portability across diverse Learning Management
Systems (LMS). The development and extension of SCORM metadata is based on IEEE Learning Object Metadata
(LOM) (LOM 2005). The LOM in SCORM is used to provide consistent descriptions of SCORM-compliant learning
objects, such as Content Aggregations, Activities, Sharable Content Objects, and Assets so that they can be
identified, categorized, retrieved within and across systems in order to facilitate sharing and reuse.
The main problem with LOM is that it is an XML-based development, which emphasizes syntax and format rather
than semantics and knowledge. Hence, even though LOM has the advantage of data transformations and digital
libraries, it lacks the semantic metadata to provide reasoning and inference functions. These functions are necessary
for the computer-interpretable descriptions, which are critical in the area of dynamic course decomposition, learning
object mining, learning objects reusability and autoexec course generation (Kiu and Lee 2006; Balatsoukas, Morris et
al. 2008). This is why most Web-based courses are still manually developed.
To improve the above problem, a mapping from LOM to statements in an RDF model has been defined (Nilsson,
Palmer et al. 2003). Such a mapping allows LOM elements to be harvested as a resource of RDF statements.
Additionally, RDF and related specifications are designed to make statements about the resource on the Web (that is,
anything that has a URI), without the need to modify the resource itself. This enables document authors to annotate
and encode the semantic relationships among resources on the Web. However, RDF alone does not provide common
schema that helps to describe the resource classes and represent the types of relationships between resources. A
specification with more facilities than those found in RDF to express semantics flexibly is needed. The Semantic
Web (Shadbolt, Berners-Lee et al. 2006) can help solve these problems.
To enhance the knowledge representation of the XML-based markup language, the traditional Semantic Web
approach is to upgrade the original XML-based to ontology-based markup language. The upgrade mentioned above
from XML-based LOM to RDF-based LOM is an example. This approach is limited in that the original XML-based
markup language has to be replaced with a new ontology-based markup language, causing the compatibility
problems with existing data applications. This study proposes a novel integration approach that combines the first
four layers of Semantic Web stack, namely URI layer, XML layer (LOM), ontology layer and rule layer. This
integration approach is defined in a formal structure, called Multi-layered Semantic LOM Framework (MSLF),
which is a specific sub-model of the Semantic Web stack for LOM applications. In MSLF, Semantic Web
technologies can be integrated with LOM to enhance computational reasoning, and the original LOM can be retained
to cooperate with ontologies and rules. That is, MSLF does not change the original schema of LOM. Hence, the
existing LOM and SCORM metadata documents can continue to be used.
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