In the last years, the growing of the Internet have opened the door to new ways of learning and education methodologies. Furthermore, the appearance of different tools and applications has increased the need for interoperable as well as reusable learning contents, teaching resources and educational tools (Wiley, 2000). Driven by this new environment, several metadata specifications describing learning resources, such as IEEE LOM (LTCS, 2002) or Dublin Core (DCMI, 2004), and learning design processes (Rawlings et al., 2002) have appeared. In this context, the term learning design is used to describe the method that enables learners to achieve learning objectives after a set of activities are carried out using the resources of an environment. From the proposed specifications, the IMS (IMS, 2003) has emerged as the de facto standard that facilitates the representation of any learning design that can be based on a wide range of pedagogical techniques. The metadata specifications are useful solutions to describe educational resources in order to favour the interoperability and reuse between learning software platforms. However, the majority of the metadata standards are just focused on determining the vocabulary to represent the different aspects of the learning process, while the meaning of the metadata elements is usually described in natural language. Although this description is easy to understand for the learning participants, it is not appropriate for software programs designed to process the metadata. To solve this issue, ontologies (Gómez-Pérez, Fernández-López, and Corcho, 2004) could be used to describe formally and explicitly the structure and meaning of the metadata elements; that is, an ontology would semantically describe the metadata concepts. Furthermore, both metadata and ontologies emphasize that its description must be shared (or standardized) for a given community. In this paper, we present a short review of the main ontologies developed in last years in the Education field, focusing on the use that authors have given to the ontologies. As we will show, ontologies solve issues related with the inconsistencies of using natural language descriptions and with the consensous for managing the semantics of a given specification.
Ontologies In Education
In the educational domain a number of ontologies have been developed for authors. Thus ontologies have been developed to describe the learning contents of technical documents and formalize the semantics of learning objects; model the elements required for the design, analysis, and evaluation of the interaction between learners in computer supported cooperative learning; and describe the learning design associated to a unit of learning in which the learning flow is explicitly declared.
Ontologies in Learning Contents and Metadata
The main purpose of these ontologies is to describe the contents or features of documents in order to favor its indexing and retrieval from applications. Thus Kabel, Wielinga, and Hoog (1999) develop three ontologies that annotate technical documents from a given domain: these documents are converted in a large collection of information elements described by a number of attributes to which values are assigned from the ontologies. These attributes are referred to the subject matter in the application domain, structural and representational properties (paragraphs, sections, etc.) and the potencial instructional roles of the information elements. Following this approach the ontologies represent the semantics of the documents, enabling its indexing and retrieving from databases.
Other interesting ontology in this field is proposed by Brase, Painter and Nejdl (2004). Using an ontology language as TRIPLE, this ontology describes the semantics of the LOM specification, adding formal axioms and rules to the metadata representation of the standard. With this formal description the semantics of the LOM specification is not changed, but it helps to define the constraints on LOM fields, making clear the meaning and use of these LOM fields, resulting in easier exchange of LOM metadata between different applications and contexts.
Key Terms in this Chapter
Metadata: Information about data, which can be used to comprehend, use, and manage data.
Learning Objects: Any reproducible and addressable digital or non-digital resource used to perform learning activities or support activities. Examples are: web pages, text books, text processors, instruments, etc.
Collaborative Learning Environment: Software system oriented to support collaborative learning experience in which two or more agents engage the goal of constructing knowledge based on group discussion and decision-making processes.
Interoperability: Capability to communicate, execute programs, or transfer data among various functional units in a manner that requires the user to have little or no knowledge of the unique characteristics of those units.
Ontology: Formal and explicit specification of a shared conceptualization, where conceptualization refers to an abstract model of a concept in the world; formal means that the ontology should be machine readable; explicit means that the type of concepts and the constraints on their use are explicitly defined; and shared reflects the notion that an ontology captures consensual knowledge accepted by a group.
Learning Design: Description of a method enabling learners to attain certain learning objectives by performing certain learning activities in a certain order in the context of a certain learning environment. A learning design is based on the pedagogical principles of the designer and on specific domain and contexts variables (e.g., designs for math be ematics teaching can differ from designs for language teaching).
Ontology Language: Formal language based on a logic paradimg that can represent concepts and the constraints between them. Reasoning capabilities of the language depend on the paradigm in which the language is based on.