Ontologies and Semantic Web in e-Learning

Handbook on Information Technologies for Education and Training, H. H. Adelsberger, Kinshuk, J. Pawlowski, D. Sampson (Eds.), 2nd Edition, 2008, Springer-Verlag Heidelberg, pp. 47-65

Ontologies and Semantic Web for e-Learning

Darina Dicheva Computer Science Department Winston-Salem State University Winston Salem, NC 27110, USA [email protected]

Summary. This article discusses the area of Ontologies and Semantic Web technologies in e-Learning and compares the state of research in years 2004 and 2006. It considers the impact of ontologies on the Web-based educational systems (WBES). It then presents an ontology of the area of Ontologies for Education along with a community Web Portal (O4E) driven by that ontology. Finally, it presents a use case of Semantic Web technologies as enabling technologies for building WBES: the case of TM4L. Topic Maps for e-Learning (TM4L) is an authoring environment for building ontology-aware standards-based repositories of learning materials (objects).

1 Introduction The Semantic Web (SW), envisioned as an extension of the current Web [1], was proposed to provide enhanced access to information based on the use of machine-processable metadata annotating the Web resources. A key enabling technology for the Semantic Web are ontologies. Ontologies offer a way to cope with heterogeneous representations of Web resources and their interoperability. An ontology, representing a model of a specific domain, can be used as a unifying structure for giving information a common representation and semantics. Ontologies are becoming very popular due to their promise to allow for a shared and common understanding of a domain that can be communicated between people and applications [2].

For educational system researchers and technologists, the Semantic Web vision opened a new venue promising to meet the increasing challenges eLearning was facing due to the fast growing Web. Although some early efforts of using ontologies in intelligent educational systems can be found (see for example [3] and [4]), the initial SW in Education-related activities can be linked to year 1999, when the first ontology-focused workshop (collocated with AIED’99) [5] took place. Among the pioneering projects employing ontologies and SW standards in education were SmartTrainer Authoring Tools [6], Edutella [7], the LOM RDF binding project [8], etc. Several SW-related projects were reported at the Workshop on Concepts and Ontologies in Web-Based Educational Systems [9] and at the Workshop on Semantic Web for Web-based Learning [10]. Year 2004, however, can be considered as the breakthrough point, when three workshops [11, 12, 13] took place and the first special journal issues focused on the application of Semantic Web and Ontologies in e-Learning (three in that single year!) were published [14, 15, 16]. In addition, a number of papers appeared in other related conferences (e.g. [17, 18]), journals (e.g. [19]]) and books (e.g. [20, 21]). In this article we present the area of Ontologies and Semantic Web technologies in e-Learning and compare the state of the art in years 2004 and 2006. We are further focusing on considering the impact of ontologies on the Web-based educational systems (WBES). We present an ontology of the area of Ontologies for Education and a community Web Portal “Ontologies for Education” (O4E) that is driven by that ontology. Finally, we present a use case of Semantic Web technologies as enabling technologies for building WBES. The use case is TM4L, an authoring environment for building ontology-aware standards-based repositories of learning materials.

2 Overview of WBES Web-based educational systems are employing Semantic Web technologies in an effort to serve better the increasing and complicating needs of the education community. 2.1 WBES at a Glance The development of WBES has distinct ‘generations’ that have distinguishing features with their own particular challenges:

1st generation WBES:  Challenges: Centralizing and unifying sporadically appearing online courses and learning materials in order to better support them from administrative, technical, software and authoring perspectives.  Distinguishing features: Centralized (typically client-server) architecture; employing Web technologies; a proprietary format for representing the maintained learning resources.  Representatives: o Learning Management Systems (LMS), aimed at supporting various teaching, learning and administrative activities to allow Web-enhanced courses (e.g. BlackBoard, WebCT [22], Moodle [23]); o Educational Portals and Digital Libraries, including online educational resources and functionality for manual indexing, annotation and archiving of content as well as for finding, accessing, and using the resources (e.g. Merlot [24], NSDL [25]). 2nd generation WBES:  Challenges: Intelligent support for the learners and authors, including adaptation and personalization to the users.  Distinguishing features: Centralized (typically client-server) architecture; employing AI and Web technologies; domain conceptualization and concept-based presentation of the maintained resources; ensuring personalization through adaptation to the learner’s needs and interests; still in a proprietary format.  Representatives: o Educational Adaptive Hypermedia (e.g. AHA! [26], InterBook [27]); o Task-centered Educational Information Systems (e.g. AIMS [28]); o Intelligent Web-Based Educational Systems, employing AI techniques to improve Web-based teaching and learning, e.g. for curriculum sequencing, solution analysis, and problem solving support (e.g. SQL-Tutor [29], ELM-ART [30], PAT Online [31], DCG [32]); o Web-based Collaborative Learning Environments, focusing on group formation, peers help, coaching, learning companions and/or others (e.g. PHelpS [33], Epsilon, etc). While the second generation WBES are ‘intelligent’ and adaptive, they are still small scale, ‘closed corpus’ projects. They work with a relatively

limited set of learning resources and employ proprietary internal representation of learning content, thus they are not interoperable. Typically they are used by one instructor or at best in one school. 3rd generation WBES: Semantic WBES (SWBES)  Challenges: Scalability, reusability of educational material, interoperability (across multiple tools and platforms), affordability (increasing learning efficiency and productivity while reducing time and costs), durability of educational material (across revisions of operating systems and software).  Distinguishing features: Typically service-based architecture; ontology-aware software; reusability, exchangeability, and interoperability of the maintained learning resources and components, based on the standardization brought by the use of ontologies and of the enabling Semantic Web standards and technologies.  Representatives: WBES employing Semantic Web technologies. 2.2 Semantic WBES The Semantic Web is a space understandable and navigable by both human and software agents. It adds structured meaning and organization to the navigational data of the current Web, based on formalized ontologies and controlled vocabularies with semantic links to each other. From the eLearning perspective, it aids learners in locating, accessing, querying, processing, and assessing learning resources across a distributed heterogeneous network; it also aids instructors in creating, locating, using, reusing, sharing, and exchanging learning objects (data and components). Devedzic describes in [19] a vision of SW-based e-Learning in which learners are supported by educational agents that access educational servers through educational services. The educational servers host repositories of standardized learning objects and services and support personalization. Aroyo & Dicheva suggest further that the Semantic Web-based Educational Systems need to interoperate, collaborate and exchange content or re-use functionality [34]. A key to enabling the interoperability is to capitalize on (1) semantic conceptualization and ontologies, (2) common standardized communication syntax, and (3) large-scale service-based integration of educational content and functionality provision and usage. This view is supported also by Anderson & Whitelock’ fundamental affordances for the Semantic Web: “The vision of the educational semantic web is based on three fundamental affordances. The first is the capacity for effective information storage and retrieval. The second is the capacity for

nonhuman autonomous agents to augment the learning and information retrieval and processing power of human beings. The third affordance is the capacity of the Internet to support, extend and expand communications capabilities of humans in multiple formats across the bounds of time and space.” [16] Thus the vision of the Semantic Web-based e-Learning is founded on the following major premises:  Machine-understandable educational content.  Shareable educational ontologies, including o Subject matter ontologies. o Instructional ontologies (representing different instructional models, learning theories, approaches). o Authoring ontologies (modeling authors’ activities).  Educational Semantic Web services, for supporting o Learning, e.g. information retrieval, summarization, interpretation (sense-making), structure-visualization, argumentation, etc. o Assessment, e.g. tests and performance tracking. o Collaboration, e.g. group formation, peer help, etc.  Semantic interoperability. Semantic interoperability, the key promise of the Semantic Web, is defined in [35] as a study of bridging differences between information systems on two levels: (1) on an access level, where system and organizational boundaries have to be crossed by creating standardized interfaces that share system-internal services in a loosely-coupled way; and (2) on a meaning level, where agreements about transported data have to be made in order to permit their correct interpretation. Interoperability requires the use of standard SW languages for representing ontologies, educational content, and services. The W3C standards include RDF, RDF-Schema, and OWL (Web Ontology Language) [36]. These languages are well supported with tools, such as APIs (e.g. Jena [37] and Sesame [38]), editors and browsers (e.g. Protégé [39] and KAON [40]), etc. An alternative SW technology is the ISO standard XML Topic Maps (XTM) [41], with similar supporting tools, such as APIs (e.g. TM4J, TMAPI) [42], editors and browsers (e.g. TM4L [43], Ontopoly [44]), etc. A comprehensive introduction to the Semantic Web standards can be found in [45], a comparison of available ontology editors in [20], and an introduction to engineering Semantic Web-based educational systems in [46]. In 2004 we analyzed the state of research in the field of Semantic Web in e-Learning by summarizing the tendencies exhibited in the papers pre-

sented at the three sessions of the Intl. Workshop on Semantic Web in eLearning (SWEL 2004), held in conjunction with the Intl. Conf. on Adaptive Hypermedia (AH’04), the Intl. Conf. on Intelligent Tutoring Systems (ITS’04), and the Intl. Semantic Web Conference (ISWC’04). (All papers are available at the SWEL Workshop website [47] and are not referenced individually here.) To present the results, we proposed a 2D classification of the research projects, with the following categories along the e-Learning and Semantic Web axes (see Fig. 1).  E-Learning related categories: o Learning Objects. o Learning Designs. o Educational Adaptive Hypermedia. o Learner Modeling. o WBES Frameworks/Architectures.  Semantic Web related categories: o Ontologies. o SW Annotation (including semantic annotation tools and (semi-) automatic generation of metadata). o Mapping educational standards to SW standards (including extending educational standards and binding educational with SW standards). o Agents/ Distributed Systems/ SW Services. 7

6

5 Learning Objects 4

Learning Designs Educational AH

3 Learner Modeling Architectures

2

1

0 Ontologies

SW Annotation

ES / SW Standards

Agents/Services

Fig. 1. 2004 Classification of SWEL projects, representing current tendencies.

The general picture suggested tendencies to novel modularized WBES architectures that:  Utilize concepts and ontologies to open, share, reuse and interchange educational content.

 Employ Semantic Web compliant educational standards to provide common syntax in the communication.  Make use of Semantic Web (educational) services targeting a largescale service-based integration of educational content and functionality. Two years later, we summarized the SWEL papers, presented at the 2005-2006 sessions of the workshop, held in conjunction with four other major conferences in the field: the Intl. Conf. on Artificial Intelligence in Education (AI-ED’05), the Intl. Conf. on Advanced Learning Technologies (ICALT’05), the Intl. Conf. on Knowledge Capture (K-CAP’05), and the Intl. Conf. on Adaptive Hypermedia (AH’06), as well as the articles published in the Special issue on Semantic Web for e-Learning of the British Journal of Educational Technology (BJET) [48]. In summarizing and clustering those works, we found out that some of the 2004 categories were not assigned any new projects, while new clusters appeared (see Fig. 2). 16 14 12 10 Learning Objects

8

Learning Designs

6

Personalization

4

Architectures

2 0 Ontologies

OntoWork

SW Annotation

Agents/Services

Fig. 2. 2006 Classification of SWEL projects, representing the current tendencies.

The first noticeable fact was the strong clustering of the current research and development work into two groups: creating/maintaining/use of subject ontologies, and semantic annotation of learning objects (resources). In a way, this indicated maturing of the field: after the initial inclination to proposing generic frameworks and abstract architectures with suggested hypothetical use of Semantic Web technologies for implementing some of their components, it was realized that the way to the Educational Semantic Web required concrete semantic annotation of learning resources, which in turn should be based on using ontologies. So, in this direction the results were not a surprise. The second noticeable fact was the departure from the initial enthusiasm to map or extend the existing educational standards, such as LOM [49] and

SCORM [50] to SW standards. This also didn’t come as a surprise, given that the current educational standards are not concerned with the actual meaning (i.e. semantics) of the annotated resources/activities. Very little interest was also shown in employing SW technologies for learning designs, or in the educational adaptive hypermedia. At the same time, two new tendencies were noticed which led to a new classification to better present the state of research: (1) The previous focus on learner modeling was shifted to personalization and contexts in SWES; (2) A distinctive branch of the general “ontologies” category appeared, related to comparing/integration/validation/evaluation of ontologies (labeled in Fig. 2 with “OntoWork”). The latter also indicates maturing of the SW in Education field.

3 Ontologies in Education The benefits of educational use of ontologies have been recognized relatively recently [51, 52, 53, 54, 55]. The term “ontology”, which is borrowed from philosophy, is defined as “a particular theory about being or reality” [56]. So, an ontology provides a particular perspective on some part of the world. While knowledge representation formalisms specify how to represent concepts, ontologies specify what concepts to represent and how they are interconnected. Thus an ontology can be seen as a well founded and broadly agreed upon system of concepts in a particular subject domain together with the relationships between those concepts. Specialized subject ontologies can be used as a semantic backbone for courseware or repositories of learning materials (objects). By providing agreed-upon vocabularies for domain knowledge representation, ontologies can support sharing, reuse, and exchange of courseware units. Ontologies also facilitate machine readability of Web content. A number of papers have been devoted to the analysis of the Ontologies in Education field, providing overviews of different aspects. Mizoguchi and Bourdeau in their seminal work [51] enlisted a number of challenges that have not yet been met by the AI-ED technologies and proposed a roadmap of how the application of ontological engineering could assist in dealing with those challenges. Similar work is done in [57, 58, 34], as well as in [59] for the more specific domain of Web-based Intelligent Systems. Several overviews of existing tools or created domain ontologies have also been performed. Examples of the former are the overview and comparison of ontology engineering environments in [20] and the analysis of semantic annotation tools for learning material made in [60]. An example from the

latter group is the overview of ontologies in the domain of engineering design [61]. In spite of the fact that the field of SWEL is fairly young, it is already quite broad and fuzzy, partly because of involving technologies from a variety of areas of information and pedagogical sciences. To facilitate the research, the authors of [62] collected and classified information in the field and used it to build an ontology-driven Web Portal - Ontologies for Education (O4E) [63]. 3.1 The O4E Ontology In the O4E project, as in many other ontology-based applications, we had to deal with two types of knowledge - subject domain and structural, which leads to two types of ontologies. A domain ontology represents the basic concepts of the domain under consideration along with their interrelations and basic properties. A structure ontology defines the logical structure of the content. It is generally subjective and depends greatly on the goals of the ontology application. It typically represents hierarchical and navigational relationships. While a domain ontology can be used as a mechanism for establishing a shared understanding of a specific domain, a structure ontology enforces a disciplined approach to authoring, which is especially important in collaborative and distributed authoring. The process, named in all methodologies just “create ontology”, is a time- and mind-consuming iterative procedure of categorization or laddering, together with disintegration or detailing. It is a totally informal analytical design, and output structures are rather subjective and sometimes awkward. One of the guidelines with regard to the structure ontology relates to the clarity and mapability of the structure. It should be taken into account that an ontology is to be used not only as a knowledge component of an information system but also as a mind tool for manual information search and navigation. Authors should thus try to follow the principles of clarity and good shape, which is an accepted practice in basic scientific abstraction and modeling (e.g. physics, chemistry, etc.) [62]. Figure 3 shows the O4E domain ontology. The top-level meta-concepts of the domain ontology divide the whole field according to the role ontologies play in the research. When an ontology is considered as an object (the result of an activity) the research is focused on the theoretical and/or practical issues of the ontological engineering that are specific to the educational context. Ontologies might also serve as a technology, facilitating the solution of some educational problems, such as interoperability of

knowledge-based systems and components, or assessment of structural knowledge. 3.1.1 Building Ontologies for Education

When analyzing resources focused on different tasks of educational ontology development, we identified two naturally separated areas of research. While some papers study mostly the theoretical issues of ontology engineering, another large set of resources relate to the practical aspects of ontology development. Three large groups could be identified within the latter part:  Automatic and semi-automatic ontology generation and extraction using different kinds of sources and technologies.  Manual ontology development, where the research is focused on problems either related to the ontology engineering process or specific to educational technology.  Research on using different standards and languages for ontology implementation, including attempt to bind Semantic Web and educational (e.g. LOM or SCORM) standards or reporting case studies on implementing general-purpose ontological formalisms in educational settings. 3.1.2 Using Ontologies in Education

This field combines diverse research on different educational applications of ontologies. We tried to look at this branch from two perspectives depending on what kind of technology is implemented (technological perspective) and what role an ontology plays within a project (application perspective). We defined three main areas within the technological perspective, two of which (knowledge representation and information retrieval) are like “technological donors” for the ontological research, while the third one (Semantic Web) benefits from it the most. As for the application perspective, ontologies have been considered for a long time only as a technical artifact acting as a knowledge base component. The field of education is one of the first in which the understanding of an ontology as a cognitive tool occurred. In many respects this was due to the wide spread of the constructivist paradigm of learning and the broad use of such knowledge technologies as concept maps, mind maps and others for learning purposes.

3.2 The Ontologies for Education Portal The created ontology was used in the development of the O4E Web Portal. The O4E Portal is aimed to serve as a single Web place, where relevant research publications, projects and successful practices are classified and annotated. The ontology is represented as a topic map [41], which is created and maintained with the TM4L Editor (see Section 4). The Topic Maps (TM) Semantic Web technology is very appropriate for formalization of lightweight ontologies and for structuring and representing ontology-based web information.

Fig. 4. The Ontologies for Education Portal.

3.3 The OMNIBUS Project: An Ontology of Learning, Instruction and Instructional Design Bourdeau and Mizoguchi’s idea of developing a framework for ontologybased intelligent systems, proposed in [51] has been implemented within the OMNIBUS project [64]. According to the authors, the OMNIBUS ontology is not a light-weight ontology but a heavy-weight ontology. It is built based on philosophical consideration of all the concepts necessary for understanding learning, instruction and instructional design. Although it is full of axioms, the Hozo GUI which is based on a frame structure makes it easier to read it. However, the readers are expected to have basic knowledge of ontology and the Hozo way of role representation. The ontology is released on the OMNIBUS site for evaluation and the complete ontology is discussed in [65].

4 Topic Maps for e-Learning (TM4L) TM4L is an authoring environment for building discipline-specific ontology-aware repositories of learning objects, which are efficiently searchable, reusable, and interchangeable. These repositories are based on topic maps. The two aspects, domain conceptualization, which supports findability, and ontologies, which support standardization and reusability are incorporated uniformly. With regard to reusability and interoperability, learning objects must comply not only with knowledge standardization (consensus on the meaning of the educational content) but also with technological standardization (use of standard formalisms, including educational standards such as LOM and SCORM). Domain conceptualization is used for structuring and classification of learning content. The classification involves linking learning objects (content) to the relevant ontology terms (concepts), i.e. using the ontological structure to index the repository content. Therefore, by browsing the map, learners gain insight into the domain. Moreover, understanding the relationships between the resources insure efficient topical access to them. The TM4L learning repository has a layered information structure consisting of three layers (see Fig. 5):  Resource layer: contains a collection of diverse information resources (learning objects) associated with the specific knowledge domain.  Semantic layer: contains a conceptual model of the knowledge domain in terms of key concepts and relationships among them.  Context layer: contains specifications of different views (contexts) on the repository resources depending on a particular goal, type of user, etc., by dynamically associating components from the other two layers. Context Layer

` Semantic Layer

`

Resource Layer

`

Fig. 5. The layered structure of a semantic learning object repository.

TM4L [64] provides support in conceptual structure design and maintenance through its functionality for editing, browsing, and combining such structures, coupled with support for relating concepts, linking concepts to resources, merging ontologies, external search for resources, defining perspectives, etc. The environment consists of a TM Editor and a TM Viewer. 4.1 TM4L Editor The TM4L Editor is an ontology editor allowing the user to build ontology-driven learning repositories using topic maps. It provides ontology and metadata engineering capabilities coupled with basic document management facilities. The TM4L Editor benefits from the Topic Maps’ fundamental feature to support easy and effective merge of existing information resources while maintaining their meaningful structure. This allows for flexibility and expediency in re-using and extending existing repositories. The learning content created by the Editor is fully compliant with the XML Topic Maps (XTM) standard and thus interchangeable with any standard XTM tools. The main objects that the TM4L Editor manipulates are topics (representing domain ontology concepts), relationships between them, resources, and contexts (represented by themes). Screenshots from the TM4L Editor interface is shown on Fig. 6.

Fig. 6. Screenshots from the TM4L Editor: Textual and Visual Topic editing.

The most largely used general ontology editor for creating educational ontologies is Protégé [39]. Other ontology editors include KAON [40], OntoEdit [67] and Hozo [68]. TM4L differs from them in two ways: (1) it allows direct indexing of resources with concepts from the ontology; (2) it

is designed with an educational use in mind; thus it contains pre-defined relationship and resource types, specifically useful for learning repositories.

4.2 TM4L Viewer In relation to topic maps browsing, authors and learners typically differentiate in their: (1) navigation and query formulation strategy; and (2) vocabulary knowledge. The different ways of tackling these questions reflects the gaps in terms of knowledge and perception between the authors and the learners. In general, learners need to alternate phases of browsing the topic map content with phases of querying it. In the latter they often need to refine their selection criteria according to the obtained results. To enable multi-purpose exploration, TM4L supports multiple views: Graph View, Text View and Tree View. Screenshot from the TM4L Viewer is shown on Fig. 7.

Fig. 7. A screenshot from the TM4L Viewer.

Acknowledgements The O4E ontology was created in collaboration with Sergey Sosnovsky, Tanya Gavrilova, and Peter Brusilovsky. TM4L and the O4E Portal resulted from the efforts of the Intelligent Information Systems group at WSSU. This work was supported in part by the NSF Grants DUE-0333069 and DUE-0442702.

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