Academic journal article Educational Technology & Society

Intelligent Discovery for Learning Objects Using Semantic Web Technologies

Academic journal article Educational Technology & Society

Intelligent Discovery for Learning Objects Using Semantic Web Technologies

Article excerpt

Introduction

The Sharable Content Object Reference Model (SCORM) (ADL 2006) provides specifications for implementing e-learning 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.

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. …

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