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Interoperable Learning Objects Management - INTRODUCTION, BACKGROUND, The Semantic Web, DRIVING FORCES

based ontology ontologies domain

Tanko Ishaya
The University of Hull, UK

INTRODUCTION

The sharing and reuse of digital information has been an important computing concern since the early 1960s. With the advent of the World Wide Web (from now on referred to as the Web), these concerns have become even more central to the effective use of distributed information resources. From its initial roots as an information-sharing tool, the Web has seen exponential growth in a myriad of applications, ranging from very serious e-business to pure leisure environments. Likewise, research into technology support for education has quickly recognised the potential and possibilities for using the Web as a learning tool (Ishaya, Jenkins, & Goussios, 2002). Thus, Web technology is now an established medium for promoting student learning, and today there are a great many online learning materials, tutorials, and courses supported by different learning tools with varying levels of complexity. It can be observed that there are many colleges and universities, each of which teaches certain concepts based on defined principles that remain constant from institution to institution. This results in thousands of similar descriptions of the same concept. This means that institutions spend a lot of resources producing multiple versions of the same learning objects that could be shared at a much lower cost. The Internet is a ubiquitous supporting environment for the sharing of learning materials. As a consequence, many institutions take advantage of the Internet to provide online courses (Ishaya et al.; Jack, Bonk, & Jacobs, 2002; Manouselis, Panagiotu, Psichidou, & Sampson, 2002). Many other agencies have started offering smaller and more portable learning materials defined as learning objects (Harris, 1999; PROMETEUS, 2002). While there are many initiatives for standardising learning technologies (Anido, Fernandez, Caeiro, Santos, Rodriguez, & Llamas, 2002) that will enable reuse and interoperability, there is still a need for the effective management, extraction, and assembling of relevant learning objects for end-user satisfaction.

What is required, therefore, is a mechanism and infrastructure for supporting a centralized system of individual components that can be assembled according to learners’ requirements.

The purpose of this paper is to examine current approaches used in managing learning objects and to suggest the use of ontologies within the domain of e-learning for effective management of interoperable learning objects. In the next section, a background of this paper is presented. The current state of e-learning metadata standards is examined and a brief overview of the semantic-Web evolution in relation to e-learning technology development is given. Then, the paper discusses the driving force behind the need for effective management of interoperability of learning objects. Next, the paper presents e-learning ontologies as the state-of-the-art way of managing interoperable learning objects. Finally, the paper concludes with further research.

BACKGROUND

The background of this paper is based on two different disciplines: developments in Web-based educational systems and the evolving vision of the semantic Web by Berners-Lee et al. (2001).

The Semantic Web

E-learning systems are made possible by the ubiquity of Internet standards such as TCP/IP (transmission-control protocol/Internet protocol), HTTP (hypertext transfer protocol), HTML (hypertext markup language, and XML (extensible markup language), an evolved representation format for interoperability. Additionally, emerging schema and semantic standards, such as XML schema, RDF and its extensions, and the DARPA (Defense Advanced Research Projects Agency) agent markup language and ontology inference layer (DAML + OIL), together provide tools for describing Web resources in terms of machine-readable metadata. This aims at enabling automated agents to reason about Web content and produce intelligent responses to unforeseen situations.

Two of these technologies for developing the semantic Web are already mature and in wide use. XML (http:www.w3.org/XML) lets everyone create their own tags that annotate Web pages or sections of text on a page. Programs can make use of these tags in sophisticated ways, but the programmer has to know what the page writer uses each tag for. So, XML allows users to add arbitrary structure to their documents but says nothing about what the structures mean (Erdmann & Studer, 2000). The meaning of XML documents is intuitively clear due to markups and tags, which are domain terms. However, computers do not have intuition. Tag names per se do not provide semantics. Both data-type definitions (DTDs) and XML schema are used to structure the content of documents but not the appropriate formalism to describe the semantics of an XML document. Thus, XML lacks a semantic model; it has only a tree model, but can play an important role in transportation mechanisms.

The resource description framework (http://www.w3.org/RDFs) provides means for adding semantics to a document. It is an infrastructure that enables the encoding, exchange, and reuse of information-structured metadata. The RDF + RDF schema offers modeling primitives that can be extended according to the needs. RDF also suffers from the lack of formal semantics for its modeling primitives, making interpretation of how to use them properly an error-prone process. Both XML and RDF have been touted as standard Web ontology languages, but they both suffer from expressive inadequacy (see Horrocks, 2002), that is, the lack of basic modeling primitives and the use of poorly defined semantics.

A third technology is the ontology representation languages. Several ontology representation languages and tools are now available—some in their early stages of development—in particular, the Web ontology language (OWL), the W3C (World Wide Web Consortium) recommendation for ontology language. However, DAML, OIL, and DAML + OIL are being used (Fensel, Horrocks, van Harmelen, McGuinness, & Patel-Schneider, 2001). All of these rely on RDF, the subject-predicate-object model, which provides a basic but extensible and portable representation mechanism for the semantic Web. Although ontology representation languages for the semantic Web are in early stages of development, it is fair to say that ontology specification would play an important role in the development of interoperable learning objects. This way, both producer and consumer agents can reach a shared understanding by exchanging ontologies that provide an agreed vocabulary.

DRIVING FORCES

Despite intensive developments in the area of Web-based learning technology and the wide variety of software tools available from many different vendors (e.g., WebCT, Blackboard, AudioGraph), there is increasing evidence of dissatisfaction felt by both instructors and learners (Jesshope, 1999; Jesshope, Heinrich, & Kinshuk, 2000). One of the causes of this dissatisfaction is that these software applications are not able to share learning resources with each other. There is evidence that the future growth of Web-based learning may well be constrained on three fronts: first, dissatisfaction with Web learning resources from students due to a lack of pedagogical underpinning in the design of existing Web learning materials (Govindasmy, 2002); second, the lack of standardisation of learning metadata schemas and course structures (Koper, 2002); and third, the lack of software interfaces that provide interoperability.

Lack of Pedagogical Consideration in the Design of Web-Based Learning Systems

Although the Internet has proved its potential for creating online learning environments to support education (Appelt, 1997; Berners-Lee, 1999; Fetterman, 1998; Harris, 1999; Jack et al., 2002), the full potential of the Internet for transforming education is only just being tapped.

The need to link pedagogy to the prevailing technological infrastructure for Web-based learning was highlighted by Ishaya et al. (2002), Koper (2001), and Mergendoller (1996). They emphasized the need for additional frameworks for Web-based learning. In answer to this requirement, several researchers have offered frameworks for learner-centred Web instruction (Bonk, Kirkley, Hara, & Dennen, 2001; Jack et al., 2002), the integration of the Web in one’s instruction, the role of the online instructor (Bonk et al.), and the types and forms of interaction made possible by the emergence of the Web (Jack et al.). The need and potential use of Web agents (Jennings, 2000; Wooldridge, 1997) to support students’ learning process by enabling an interactive Web-based learning paradigm has also been identified in Ishaya et al. and Jack et al. There is still evidence that pedagogical issues are neglected within the design of most e-learning systems. This may result in these systems failing due to teachers’ reluctance to incorporate their learning resources into those systems, learners avoiding e-learning situations, and the poor performance of learners who do use the systems (Deek, Ho, & Ramadhan, 2001; Govindasmy, 2002; Hamid, 2002; Koper, 2001, 2002). There is also evidence of the lack of consideration for users with learning difficulties in current Web-based learning environments (Koper, 2001; Manouselis et al., 2002). Most of the existing Web-based learning frameworks and models are at the theoretical level and address specific aspects of learning pedagogy (e.g., Bonk et al.; Ishaya et al.; Jack et al.).

Lack of Interoperability and Shareable Learning Objects

A wide variety of teaching materials have been made available in a number of specific formats that are no longer supported (Deek et al., 2001; Koper, 2002). These materials are therefore no longer usable without large investments in converting them into a usable format. The reusability of educational content and instructional components is often limited because existing components cannot easily be obtained for integration. The reusability of learning components involves a number of processes such as the identification of components, correct handling of intellectual property rights, isolation, decontextualisation, and the assembly of components (Koper, 2001, 2002). Making components reusable and manageable provides the advantage of efficiency in Web-based learning-system design. The technique, however, is not simple and requires clear agreements about the standards to be used. Software reuse is a key aspect of good software engineering. One of the current trends in this field is the component-based approach (Lim, 1998). Enterprise JavaBeans (EJB) and the common request broker architecture (CORBA) are examples of technologies that are based on the software-component concept. Software reuse allows programmers to focus their efforts on the specific business logic. The component-based software-engineering approach can be used to provide interoperable and shareable learning objects.

Learning-technology standardisation is taking the lead role in the research efforts surrounding Web-based education. Standardisation is needed for two main reasons. First, educational learning resources are defined, structured, and presented using different formats. Second, the functional modules that are embedded in a particular learning system cannot be reused by another one. Projects like IEEE’s LTSC (IEEE, 2002), IMS (IEEE), PROMETEUS (2002), GESTALK (1998), and many others are contributing to this standardisation process. The IEEE LTSC is the institution that is gathering recommendations and proposals from other learning-standardisation institutions and projects.

Lack of Industry Guidance for the Design of Manageable Systems

Industry and academic reports highlight the importance of defining metadata for learning (Anido et al., 2002; IEEE, 2002; Koper, 2002). Its purpose is to facilitate and automate the search, evaluation, acquisition, and use of Web-based learning resources. The result so far is the LOM specification (IEEE) proposed by IEEE LTSC, which is becoming a de facto standard.

Personalisation is increasingly being used in e-commerce as an aid to customer-relationship management (CRM) to provide better service by anticipating customer needs. This is because companies believe that this will make interaction more satisfying. In the educational sector, the aim is toward ensuring that Web resources improve students’ learning process. This, too, could be improved through personalisation. The semantic Web offers the possibility of providing the user with relevant and customised information (Berners-Lee, 1999). Furthermore, the recognition of the key role that ontologies are likely to play in the future of the Web has lead to the extension of Web markup languages in order to facilitate content description and the development of Web-based ontologies, for example, the XML schema (Horrocks & Tessaris, 2002), RDF (Horrocks & Tessaris; IEEE, 2002), and the recent DAML + OIL (IEEE). While the development of the semantic Web and of Web ontology languages still presents many challenges, it provides a means for creating a centralized and managed Web-based learning environment where software agents (Wooldridge, 1997) can be designed to carry out sophisticated tasks for users. This will provide an adaptive learning environment.

This brief review highlights the complexity of the factors influencing the effectiveness of Web-based learning. Despite the extent of the work mentioned above, there is a lack of an effective way of managing centralized and interoperable learning materials. Some work has addressed the content and sequencing of learning objects (Koper, 2002). However, without a comprehensive pedagogical analysis in the area of Web-based learning, it is difficult to develop learning resources that can be interoperable, interactive, and collaborative. The progress made in understanding and building flexible and interoperable subject-domain and course ontologies, and linking them with learning materials and outcomes has being the emphasis in recent research. Recent developments related to the semantic Web (Berners-Lee, 1999; Horrocks, 2002; Horrocks & Tessaris, 2002) and ontologies (Horrocks) have revealed new horizons for defining structures for authoring interoperable learning objects. This indicates that the models and frameworks drawn will have to be evaluated across different scenarios of use, which should be based on sound software engineering and learning pedagogy.

ONTOLOGIES: A WAY FORWARD

Ontology is not a new concept. The term has a long history of use in philosophy, in which it refers to the subject of existence and particularly a systematic account of existence (Erdmann & Studer, 2000; Gruber, 1995). It has been a co-opted term from philosophy used in computing to describe formal, shared conceptualizations of a particular domain (Gruber). Ontologies have become a topic of interest in computer science (Fensel et al., 2001). An ontology represents information entities such as people, artifacts, and events in an abstract way. They allow the explicit specification of a domain of discourse, which permits access to and reason about agent knowledge (Erdmann & Studer). Ontologies are designed so that knowledge can be shared with and among people and possibly intelligent agents. Tom Gruber defines ontology as “an explicit representation of a conceptualisation. The term is borrowed from philosophy, where Ontology is a systematic account of existence. For AI [artificial intelligence] systems, what ‘exists’ is that which can be represented” (p. 911).

A conceptualization refers to an abstract model of some phenomenon in the world made by identifying the relevant concept of that phenomenon. Explicit means that the types of concepts used and the constraints on their use are explicitly defined. This definition is often extended by three additional conditions. The fact that an ontology is an explicit, formal specification of a shared conceptualization of a domain of interest indicates that an onotology should be machine readable (which excludes natural language). It indicates that it captures consensual knowledge that is not private to an individual, but accepted as a group or committee of practice. The reference to a domain of interest indicates that domain ontologies do not model the whole world, but rather model just parts that are relevant to the task at hand.

Ontologies are therefore advanced knowledge representations that consist of several components including concepts, relations and attributes, instances, and axioms. Concepts are abstract terms that are organized in taxonomies. Hierarchical concepts are linked with an “is a” relation. For example, we can define two concepts: person and man. These can be hierarchically linked as “A man is a person.” Instances are concrete occurrences of abstract concepts. For example, we can have one concept, Man, with one instance of a Mike. Mike is a man and his first name is Mike. Axioms are rules that are valid in the modeled domain. There are simple symmetric, inverse, or transitive axioms consisting of several relations. For example, an inverse axiom is “If a person works for a company, the company employs this person.”

Ontologies enable semantic interoperability between information systems, thereby serving a central role for the semantic Web and, in particular, serving as a means for the effective management of e-learning services. They can be used to specify user-oriented or domain-oriented learning services. Intelligent mediators can also use them: a central notion in teaching and learning. Therefore, the development of ontology can be useful for object or service modeling for e-learning domains.

There exist numerous scientific and commercial tools for the creation and maintenance of ontologies that have been used to build applications based on them, including those from the areas of knowledge management, engineering disciplines, medicine, and bioinformatics. It should be noted that ontologies do not overcome any interoperability problems per se since it is hardly conceivable that a single ontology is applied in all kinds of domains and applications. Ontology mapping does not intend to unify ontologies and their data, but to transform ontology instances according to the semantic relations defined at the conceptual level.

CONCLUSION

The semantic Web constitutes an environment in which human and machine agents will communicate on a semantic basis. This paper has examined current approaches used in managing learning objects. While it is clear that there is a comprehensive suite of standards that seem to have addressed some aspects of the management of learning objects, it is still clear that the management of interoperable learning objects is yet to be fully achieved. There are a lot of driving forces and a need for the development of flexible, portable, centralized, managed, and interoperable learning objects. Many challenges abound.

To meet these challenges, the author puts forward a new approach toward the management of interoperable learning objects by exploiting the power of ontologies and existing semantic and Web-services technology. It defines a framework that is being used toward enabling the semantic interoperability of learning services within the domain of e-learning. Further work is being done toward a definition of an ontology-management architecture for e-learning services. The architecture will define three main layers—interface, service integration, and management—with service composition running across all three. The aim of the architecture will be to provide an integration service platform that offers learner-centric support for Web-based learning, thus defining semantic relations between source learning resources (which may have been described using an ontology). This will be developed using Web services, an ontology, and agent components.

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