INTRODUCTION
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 (Gomez-Perez, Fernandez-Lopez, 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 poten-cial 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.
Ontologies in Collaborative Learning Environments
These ontologies are used to model the interaction between the learning actors (typically teachers and students) in collaborative environments. Thus Inaba et al. (2001) present an ontology a collaborative learning ontology that facilitates the design, analysis, and evaluation of a collaborative learning sesion. This ontology describes the concepts of several well-established learning theories, defining the semantics of what learning goal concept is and connecting this concept with the theories which are formulated in a taxonomy. In this work, authors have used the ontology to facilitate users the design and execution of the instructional process in a collaborative environment (Barros, Verdejo, Read, & Mizoguchi, 2002).
Ontologies in Learning Design
These ontologies focus on the semantic description of the learning design modelling which defines the learning flow of the activities to be carried out by teachers and students. The ontologies developed in this field are based on the IMS Learning Design (IMS LD) specification which has risen as a de facto standard for defining learning designs. This specification has: (1) a well-founded conceptual model that declares the vocabulary and the functional relations between the concepts of the learning design; (2) an information model that describes in an informal (natural language) way the semantics of every concept and relation introduced in the conceptual model; and (3) a behavioural model that specifies the constraints imposed to the software system when a given learning deisgn is executed in runtime. In other words, the behavioural model defines the semantics of the IMS LD specification during the execution phase. Figure 1 depicts the main concepts of the IMS LD specification.
Knight, Gasevic and Richards (2006) present a general framework whose prupose is to save the gap between learning designs and the learning objects used in them. For achieved this, the framework considers the development of three ontologies that describe the learning design, the learning objects and the context in which these objects are used. LOCO is the ontology, defined in the language OWL (Dean & Schreiber, 2004), that deals with the description of learning designs. It represents the semantics specified in IMS LD and, particularly, in its conceptual model, which means that LOCO integrates the concepts and relations defined in the conceptual and information models of the IMS
Figure 1. Main concepts of the IMS Learning Design specification
Table 1. Examples of axioms that constrain the semantics of the IMS LD concepts
| IMS LD Specification | Page 38 (item 0.2.2): “The time limit specifies that it is completed when a certain amount of time has passed, relative to the start of the run of the current unit of learning. The time is always counted relative to the time when the run of the unit-of-learning has been started. Authors have to take care that the time limits set on role-parts, acts and plays are logical.” | |
| Design | ||
| Axiom 1 | Explanation | The value of the attribute time limit of a Method must be greater than the value of the time limit of any Play. That is, the Play(s) cannot finish after the Method. |
| Formal Description | V m, p, cm, cp | m e Method a p e Play a cm e Complete-Method a cp e Complete-Play a play-ref(p, m) a complete-unit-of-learning-ref(cm, m) a complete-play-ref(cp, p) § time-limit(cm) > time-limit(cp) | |
| Design Axiom 2 | IMS LD Specification | Page 90: “The same role can be associated with different activities or environments in different role-parts, and the same activity or environment can be associated with different roles in different role-parts. However, the same role may only be referenced once in the same act.” |
| Explanation | For the same Act, the Roles involved in the execution of the Act are disjoint. | |
| Formal Description | V a, r, rp | a e Act a r e Role a rp e Role-Part a role-part-ref(rp, a) a role-ref(r, rp) § — 3 rp1 | rp1 e Role-Part a rp1 ^ rp a role-part-ref(rp1, a) a role-ref(r, rp1) | |
| Runtime Axiom 1 | IMS LD Specification | Page 25 (item 0.2.1): “The create-new attribute indicates whether multiple occurrences of this role may be created during runtime. When the attribute has the value “not-allowed” then there is always one and only one instance of the role.” |
| Explanation | If the value of the attribute create-new is “not-allowed”, it can have an only instance of the Role at which it is applied. | |
| Formal Description | V r | r e Role a create-new(r) = “not-allowed” § — 3 r1 | r1 e r |
LD standard, but the semantics expressed in natural language is not included in the ontology.
To deal with this issue, Amorim, Lama, Sanchez, Riera and Vila (2006) propose an ontology also based on the IMS LD that incorporates all its semantics, adding a number of axioms to the conceptual model: they are extracted from the information model where are expressed as natural language restrictions to the values of the concept attributes (table 1). Therefore this ontology does not modify the IMS LD spefication, but it incorporates all the semantics in order to enable software programs to manage directly from the representation in the ontology. With this formal specification this ontology, which is developed in F-Logic (Kiefer, Lausen, Wu, 1996) and OWL, has been used to validate the consistency of unit of learnings defined in authoring tools and as a language for knowledge interchanging between agents in collaborative environment (Riera et al., 2005).
CONCLUSION
Ontologies in Education are usually developed following a metadata standard whose intend is capture the semantics of a given theory or specification. Most of metadata standards have been modelled following the XML-Schema language (Thompson, Beech, Maloney, & Mendelsohn, 2004) which is not expressive enough to describe the semantics (or meaning) associated to the elements defined in the metadata. Thus, the main limitations of the XML-Schema language are (Gil & Ratnakar, 2002) that hierarchical relations between two or more concepts cannot be explicitly defined, and general and formal constraints (or axioms) between concepts, attributes, and relations cannot be specified.
To solve these limitations of the XML-Schema language the modelling of metadata standards needs to be enriched in order to describe explicitly and formally the semantics of its elements. Thus misinterpretations or errors are avoided when the instances of the concepts are created. This is the main purpose of the ontologies developed in the Education field: to favour the interoperability between software programs by representing all the semantics of the metadata, not only the concepts and relations expressed in XML-based formats.
KEY TERMS
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.
Learning Design: Description of a method enabling learners to attain certain learning obj ectives 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).
Learning Objects: Any reproducible and addressable digital or non-digital resource used to perform learning activities or support activities. Examples are: web pages, text topics, text processors, instruments, etc.
Metadata: Information about data, which can be used to comprehend, use, and manage data.
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.
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.
