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

To demonstrate the feasibility of MSLF, an intelligent LOM shell for finding relevant learning objects, called
LOFinder, is developed based on this framework. The core components of LOFinder include the LOM Base,
Knowledge Base, Search Agent, and Inference Agent. It supports three different approaches for finding relevant
learning objects of a certain course, namely LOM metadata, ontology-based reasoning and rule-based inference.
Such dynamic finding is desirable for a number of reasons. Firstly, it is customized for each individual learning
object, based on what metadata and knowledge the learning object has shown so far. Secondly, because the content
or category of a learning object may keep changing, dynamic finding provides more up-to-date suggestions than a
static design. Thirdly, as the number of learning objects may be large, adding suggestion links may become
cumbersome for the course developer. Lastly, it can also be used at run-time to help in the decision of what content
model component to deliver to the learner.
This study mainly aims to enhance the knowledge representation of LOM for computer-interpretable effects. It
makes three main contributions. (1) This study defines a common framework MSLF for integrating Semantic Web
technologies into LOM to facilitate machine understanding. (2) This study implements an intelligent LOFinder to
demonstrate the feasibility of MSLF. LOFinder can be associated with various domain knowledge and metadata to
offer dynamic relevant learning objects for different applications. (3) LOFinder can be easily transplanted to
Learning Objects Repositories.
Semantic Web and Learning Objects
The section briefly discusses the current Semantic Web technologies and learning objects.
Semantic Web Stack
This study primarily focuses on the first four layers of Semantic Web Stack, including URI, XML, Ontology, and
Rules layers. The first layer (including Unicode and URI) and second layer (including XML, Namespace, and XML
Schema) represent current web technology. URI allows any web based resource to be identified. Unicode provides
the basic character set for web based resources. XML and XML Schema provide a surface syntax for structured
documents, but impose no semantic constraints on the meaning of these documents.
The third layer is ontology vocabulary that is rapidly becoming a reality through the development of ontology
markup languages such as RDF, RDF Schema, DAML+OIL, and OWL (Web Ontology Language) (Smith, Welty et
al. 2004). These ontology markup languages enable the creation of arbitrary domain ontologies that support the
unambiguous description of web content. OWL is essentially an XML encoding of an expressive Description Logic,
builds upon RDF and includes a substantial fragment of RDF-Schema. OWL has more facilities for expressing
meaning and semantics than RDF and RDFS, thus OWL goes beyond these languages in its ability to represent
machine-readable content on the Web. Unfortunately, these ontology markup languages are insufficient for
describing the conditions under which specific relations might hold, which requires the explicit representation of
implications, as is provided by logic programs, such as rules. A broad consensus has evolved in the Semantic Web
community that the vision of the Semantic Web includes, specifically, rules as well as ontologies. The forth layer of
W3C’s Semantic Web stack is rules to reflect this idea consensus view.
Learning Objects
The term learning object is one of the main research topics in the e-learning community in the recent years. The
Semantic Web is an extension of the current web in which information is given well defined meaning, better
enabling computers and people to work in cooperation. Many studies (Hsu 2009; Hsu, Chi et al. 2009; Hsu, Tzeng et
al. 2009; Lu, Horng et al. 2010) adopt Semantic Web to build intelligent applications in various domains. Learning
objects can be considered as resources that are accessible over the Internet. Therefore, Semantic Web can be used to
enhance the accessibility, reusability, and interoperability of learning objects. In recent years, several research
studies have focused on adopting ontology to enhance the interoperability of learning objects. But, they do not
address the issue of how Semantic Web technologies can provide LOM to facilitate machine understanding.
LOFinder is first to address the issue. The following gives a brief overview on existing ontology approaches.
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