semantic web technologies as enabling technologies for building WBES. The use case is ... are used by one instructor or at best in one school. 3rd generation ...
dP roo f
3 Ontologies and Semantiv Web for E-Learning
D. Dicheva
3.1
rre
cte
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 ontologyaware standards-based repositories of learning materials (objects).
Introduction
Un
co
The semantic web (SW), envisioned as an extension of the current web (Berners-Lee et al. 2001), 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 a shared and common understanding of a domain that can be communicated between people and applications (Davies et al. 2003). For educational system researchers and technologists, the semantic web vision opened a new venue promising to meet the increasing challenges E-Learning was facing due to the fast-growing web. Although some early efforts of using ontologies in intelligent educational systems can be found
48
D. Dicheva
Overview of WBES
co
3.2
rre
cte
dP roo f
(see Ikeda et al. 1995; and Mizoguchi et al. 1996), the initial SW in education-related activities can be linked to year 1999, when the first ontologyfocused workshop (collocated with AIED’99) (AIED 1999) took place. Among the pioneering projects employing ontologies and SW standards in education were SmartTrainer Authoring Tools (Jin et al. 1999), Edutella (Nejdl et al. 2001), the LOM RDF binding project (Nilsson et al. 2003), etc. Several SW-related projects were reported at the Workshop on Concepts and Ontologies in Web-Based Educational Systems (ICCE 2002) and at the Workshop on Semantic Web for Web-based Learning (CAISE 2003). The year 2004, however, can be considered the breakthrough point, when three workshops (Adaptive Hypermedia 2004; ITS 2004; ISWC 2004) took place, and the first special journal issues focused on the application of Semantic Web and Ontologies in E-Learning (three that year alone!) were published (Sampson et al. 2004; Dicheva and Aroyo 2004b; Anderson and Whitelock 2004). In addition, a number of papers appeared in other related conferences (e.g., Dolog et al. 2004; Gašević et al. 2004), journals (e.g., Devedžic 2004a) and books (e.g., Mizoguchi 2004; Brase and Wolfgang 2004). In this article, I present the area of ontologies and semantic web technologies in E-Learning and compare the situation in 2004 and 2006. I will further focus on considering the impact of ontologies on the web-based educational systems (WBES). I present an ontology of the area of ontologies for education and a community web portal, Ontologies for Education, (O4E) which is driven by that ontology. Finally, I present a use case of semantic web technologies as enabling technologies for building WBES. The use case is TM4L, an authoring environment for building ontologyaware standards-based repositories of learning materials.
Un
Web-based educational systems are employing semantic web technologies in an effort to better serve the increasing and complicated needs of the education community. 3.2.1
WBES at a Glance
The development of WBES has distinct generations that have different features with their own particular challenges:
Ontologies and Semantiv Web for E-Learning
49
1st generation WBES:
cte
2nd generation WBES:
dP roo f
• 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 (Blackboard n.d), Moodle (Moodle 2007)) 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 (Merlot 2007), NSDL (NDSL n.d))
Un
co
rre
• 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! (De Bra and Calvi 1998), InterBook (Brusilovsky et al. 1998)) o Task-centered educational information systems (e.g., AIMS (Aroyo and Dicheva 2001)) 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 (Mitrovic and Hausler 2000), ELMART (Weber and Brusilovsky 2001), PAT Online (Ritter 1997), DCG (Vassileva and Deters 1998)) o Web-based collaborative learning environments, focusing on group formation, peer help, coaching, learning companions and/ or others (e.g., PHelpS (Greer et al. 1998), Epsilon, etc.)
50
D. Dicheva
dP roo f
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)
3.2.2
cte
• 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, ontologyaware 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 Semantic WBES
Un
co
rre
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 E-Learning 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). Devedžic describes a vision of SW-based E-Learning in which learners are supported by educational agents that access educational servers through educational services (Devedžic 2004a). The educational servers host repositories of standardized learning objects and services and support personalization. Aroyo and Dicheva suggest further that the semantic web-based educational systems need to interoperate, collaborate and exchange content or re-use functionality (Aroyo and Dicheva 2004). 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.
Ontologies and Semantiv Web for E-Learning
51
dP roo f
This view is also supported by Anderson and Whitelock’s 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” (Anderson and Whitelock 2004). Thus the vision of the semantic web-based E-Learning is founded on the following major premises:
rre
cte
• 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 Collaboration, e.g., group formation, peer help, etc. • Semantic interoperability
Un
co
Semantic interoperability, the key promise of the semantic web, is defined as a study of bridging differences between information systems on two levels (Aroyo et al. 2006): (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) (RDF 2007). These languages are well supported with tools such as APIs (e.g., Jena (Jena 2007) and Sesame (Sesame 2007)), editors and browsers (e.g., Protégé (Protégé 2007) and KAON (KAON 2007)). An alternative SW technology is the ISO standard XML Topic Maps (XTM) (XML Topic Maps 2007), with similar supporting tools, such as APIs (e.g., TM4J, TMAPI) (TMAPI n.d.), editors and browsers (e.g., TM4L (TM4L n.d.), Ontopoly (Ontopoly 2007)). A comprehensive introduction to
52
D. Dicheva
dP roo f
the semantic web standards can be found in various paper such as (Antonio and van Harlemen 2004); a comparison of available ontology editors (Mizoguchi 2004); an introduction to engineering semantic web-based educational systems (Devedžic 2006). 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 presented at the three sessions of the International Workshop on Semantic Web in E-Learning (SWEL 2004), held in conjunction with the International Conference on Adaptive Hypermedia (AH’04), the International Conference on Intelligent Tutoring Systems (ITS’04) and the International Semantic Web Conference (ISWC’04). (All papers are available at the SWEL Workshop website (SWEL n.d.) 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. 3.1).
co
rre
cte
• 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
Un
5
Learning Objects
4
Learning Designs Educational AH
3
Learner Modeling Architectures
2 1 0
Ontologies
SW Annotation
ES / SW Standards
Agents/Services
Fig. 3.1. 2004 Classification of SWEL projects, representing current tendencies
Ontologies and Semantiv Web for E-Learning
53
The general picture suggested tendencies to novel modularized WBES architectures that:
dP roo f
• 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
Un
co
rre
cte
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 International Conference on Artificial Intelligence in Education (AI-ED’05), the International Conference on Advanced Learning Technologies (ICALT’05), the International Conference on Knowledge Capture (K-CAP’05), and the International Conference on Adaptive Hypermedia (AH’06), as well as articles published in the special issue on semantic web for E-Learning of the British Journal of Educational Technology (BJET) (Naeve et al. 2006). 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. 3.2). The first noticeable fact was the strong clustering of the current research and development work into two groups: creating/maintaining/using subject ontologies, and semantic annotation of learning objects (resources). In a way, this indicated maturing of the field. After the initial inclination to propose 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
Fig. 3.2. 2006 Classification of SWEL projects, representing the current tendencies
54
D. Dicheva
3.3
cte
dP roo f
web required concrete semantic annotation of learning resources, which in turn should be based on using ontologies. Thus, 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 (LOM 2007) and SCORM (SCORM 2007) 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; and (2) A distinctive branch of the general “ontologies” category appeared, related to comparing/integration/validation/evaluation of ontologies (labeled in Fig. 3.2 with “OntoWork”). The latter also indicates maturing of the SW in Education field.
Ontologies in Education
Un
co
rre
The benefits of educational use of ontologies have been recognized relatively recently (Mizoguchi and Bourdeau 2000; Mitrovic and Devedžic 2002; Dicheva and Aroyo 2002; Devedžic 2003). The term “ontology”, which is borrowed from philosophy, is defined as “a particular theory about being or reality” (Gruber 2003). 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 the education field, providing overviews of different aspects. Mizoguchi and Bourdeau, in their seminal work (Mizoguchi and Bourdeau 2000), 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
Ontologies and Semantiv Web for E-Learning
55
3.3.1
The O4E Ontology
dP roo f
engineering could assist in dealing with those challenges. Similar work is reported in several publications (Devedžic 2001), and (Devedžic 2004b) 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 (Mizoguchi 2004) and the analysis of semantic annotation tools for learning material made (Azouaou et al. 2004). An example from the latter group is the overview of ontologies in the domain of engineering design (Kitamura and Mizoguchi 2004). 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, Dicheva et al. (2005) collected and classified information in the field and used it to build an ontology-driven web portal, Ontologies for Education (O4E) (The O4E Portal 2007).
Un
co
rre
cte
In the O4E project, as in many other ontology-based applications, we dealt with two types of knowledge, subject domain and structural, which led to two types of ontologies. A domain ontology represents the basic concepts of the domain under consideration 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 merely creates ontology, and 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.) (Dicheva et al. 2005).
56
D. Dicheva
Un
co
Fig. 3.3. The O4E domain ontology
rre
cte
dP roo f
Figure 3.3 shows the O4E domain ontology. The top-level metaconcepts 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
Ontologies and Semantiv Web for E-Learning
57
dP roo f
educational context. Ontologies might also serve as a technology, facilitating the solution of some educational problems such as the interoperability of knowledge-based systems and components, or the assessment of structural knowledge. 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 relates to the practical aspects of ontology development. Three large groups could be identified within the latter part:
rre
cte
• 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 Using Ontologies in Education
Un
co
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 understanding of ontology as a cognitive tool occurred. In many respects, this was due to the widespread use of the constructivist paradigm of learning and the broad use of such knowledge technologies as concept maps, mind maps and others for learning purposes.
58
D. Dicheva
3.3.2
dP roo f
The created ontology was used in the development of the O4E Web Portal (Fig. 3.4). The O4E Portal is aimed to serve as a single web access point, where relevant research publications and projects and successful practices are classified and annotated. The ontology is represented as a topic map (XML Topic Maps 2007), which is created and maintained with the TM4L Editor (see Sect. 3.4). The Topic Maps (TM) semantic web technology is very appropriate for formalization of lightweight ontologies and for structuring and representing ontology-based web information.
cte
AQ: Please check whether the inserted citation of Fig. 3.4 is appropriate.
The Ontologies for Education Portal
3.3.3
rre
Fig. 3.4. The ontologies for education portal
The OMNIBUS Project: An Ontology of Learning, Instruction and Instructional Design
Un
co
Bourdeau and Mizoguchi’s idea of developing a framework for ontologybased intelligent systems, proposed in (Mizoguchi and Bourdeau 2000), has been implemented within the OMNIBUS project (The Omnibus Project 2007). According to the authors, the OMNIBUS ontology is not a lightweight ontology but a heavyweight ontology. It is 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, 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 (Mizoguchi et al. 2007).
Ontologies and Semantiv Web for E-Learning
3.4
59
Topic Maps for E-Learning (TM4L)
cte
dP roo f
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 the structuring and classification of learning content. Classification involves linking learning objects (content) to the relevant ontology terms (concepts), that is, 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 ensures efficient topical access to them. The TM4L learning repository has a layered information structure consisting of three layers (see Fig. 3.5):
Un
co
rre
• 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
Fig. 3.5. The layered structure of a semantic learning object repository
60
D. Dicheva
3.4.1
dP roo f
TM4L (The Omnibus Project 2007) 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. TM4L Editor
Un
co
rre
cte
The TM4L Editor is an ontology editor that allows 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 merging 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 is thus interchangeable with other 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 are shown in Fig. 3.6.
Fig. 3.6. Screenshots from the TM4L Editor: textual and visual topic editing
Ontologies and Semantiv Web for E-Learning
61
3.4.2
TM4L Viewer
dP roo f
The most widely used general ontology editor for creating educational ontologies is Protégé (Protégé 2007). Other ontology editors include KAON (KAON 2007), OntoEdit (OntoEdt. 2007) and Hozo (Hozo Ontology Editor 2007). 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.
co
rre
cte
In relation to topic map browsing, authors and learners typically differentiate in their: (1) navigation and query formulation strategy; and (2) vocabulary knowledge. The different ways of tackling these issues reflects the gaps in terms of knowledge and perception between authors and learners. In general, learners need to alternate phases of browsing the topic map content with phases of querying it. In querying, they often need to refine their selection criteria according to the obtained results. To enable multipurpose exploration, TM4L supports multiple views: graph view, text view and tree view. A screenshot from the TM4L Viewer is shown in Fig. 3.7.
Un
Fig. 3.7. 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.
62
References
dP roo f
Adaptive Hypermedia (2004). Workshop on applications of Semantic Web technologies for educational adaptive hypermedia, in conjunction with AH’04, Eindhoven, The Netherlands, August 23–26, 2004 http://www.win.tue.nl/~ laroyo/swel/2004/swel-ah.html. AIED (1999).Workshop on ontologies for intelligent educational systems, in conjunction with AI-ED’99, Le Mans, France, July 18–19, 1999. http://aied. inf.ed.ac.uk/abstracts/Vol_10/ontologies.html. Anderson, T., & Whitelock, D. (Ed.) (2004). The educational Semantic Web: visioning and practicing the future of education. Journal of Interactive Media in Education, (1), special issue. Antonio, G., & van Harlemen, F. (2004). A Semantic Web Primer. Cambridge, MA: MIT. Apted, T., & Kay, J. (2004). MECUREO ontology and modelling tools. IJCEELL, 14(3), 55–62. Aroyo, L., & Dicheva, D. (2001). AIMS: learning and teaching support for WWW-based education. IJCELLL, 11(1/2), 152–164. Aroyo, L., & Dicheva, D. (2004). The new challenges for e-learning: the educational Semantic Web. Journal of Educational Technology and Society, 7(4), 59–69. Aroyo, L., Dolog, P., Houben, G.-J., Kravcik, M., Naeve, A., Nilsson, M., & Wild, F. (2006). Interoperability in personalized adaptive learning. Journal of Educational Technology and Society, 9(2), 4–18. Azouaou, F., Desmoulins, C., & Chen, W. (2004). Semantic annotation tools for learning material. In Workshop SW-EL’04, Eindhoven, The Netherlands, August 23–26, 2004. Berners-Lee, T., Hendler, J., & Lassila, O. (2001). The Semantic Web. Scientific American, 284, 34–43. Blackboard (n.d.). Blackboard & WebCT. http://www.blackboard.com/us/index.Bb. Brase, J., & Wolfgang, N. (2004). Ontologies and metadata for e-learning. In Staab, S., & Studer, R. (Ed.). Handbook on Ontologies (pp. 555–573). Berlin Heidelberg New York: Springer. Brusilovsky, P., Eklund, J., & Schwarz, E. (1998). Web-based education for all: a tool for developing adaptive courseware. In Proceedings of Seventh International World Wide Web Conference (pp. 291–300). CAISE (2003).Workshop on Semantic Web for Web-based Learning, in conjunction with CAISE’03, Klagenfurt/Velden, Austria, June 2003. http://www. sw-wl03.bessag.net/. Davies, J., Fensel, D., & van Harmelen, F. (2003). Towards the Semantic Web: Ontology-Driven Knowledge Management. New York: Wiley, pp. 4–5. De Bra, P., & Calvi, L. (1998). AHA!: a generic adaptive hypermedia system. In Workshop on Adaptive Hypertext & Hypermedia, Pittsburgh, PA, USA. Devedžic, V. (2001). The Semantic Web – implications for teaching and learning. In Proceedings of ICCE 2001 (pp. 26–28). Seoul, Korea.
Un
co
rre
cte
AQ: References Apted and Kay (2004), Dicheva and Aroyo (2004a), and Dicheva and Dichev (2006) are not cited in text. Please cite.
D. Dicheva
Ontologies and Semantiv Web for E-Learning
63
Un
co
rre
cte
dP roo f
Devedžic, V. (2003). Next-generation Web-based education. International Journal for Continuing Engineering Education and Life-long Learning, 11(1/2), 232– 247. Devedžic, V. (2004a). Education and the Semantic Web. International Journal of Artificial Intelligence in Education, 14, 39–65. Devedžic, V. (2004b). Web Intelligence and artificial intelligence in education. Journal of Educational Technology and Society, 7 (4), 29–39. Devedžić, V. (2006). Semantic Web and Education. Berlin Heidelberg New York: Springer. Dicheva, D., & Aroyo, L. (2002). Concept and ontologies in WBES. In Proc. ICCE Workshop Concepts & Ontologies in WBES (pp. 3–4). Auckland, NZ, 2002. Dicheva, D., & Aroyo, L. (2004a). Concepts and ontologies in Web-based educational systems. IJCELLL, 14(3), 187–190. Dicheva, D., & Aroyo, L. (Ed.) (2004b). Concept and ontologies in web-based educational systems. Intlernatonal Journal of Continuous Engineering Education and Life-long Learning, 14 (3), special issue. Dicheva, D., & Dichev, C. (2006). TM4L: creating and browsing educational topic maps. British Journal of Educational Technology – BJET, 37(3), 391–404. Dicheva, D., Sosnovsky, S., Gavrilova, T., & Brusilovsky, P. (2005). Ontological Web portal for educational ontologies. In Workshop on Applications of Semantic Web in E-Learning (SWEL) AIED’05. Amsterdam, The Netherlands. Dolog, P., Henze, N., Nejdl, W., & Sintek, M. (2004). Personalization in distributed e-learning environments. In Proceedings of the 13th International World Wide Web Conference. New York, May 2004. Gašević, D., Jovanović, J., & Devedžić, V. (2004). Enhancing learning object content on the Semantic Web. In Proceedings of the 4th IEEE Intl Conference on Advanced Learning Technologies (pp. 714–716). Joensuu, Finland. Greer, J., McCalla, G., Collins, J., Kumar, V., Meagher, P., & Vassileva, J. (1998). Supporting peer help and collaboration in distributed workplace environments. International Journal of AI and Education, 9, 159–177. Gruber, T. (2003). A Translation Approach to Portable Ontology Specifications. San Diego: Academic. Hozo Ontology Editor (2007). http://www.hozo.jp/. Accessed 14 June 2007. ICCE (2002). Workshop on concepts and ontologies in Web-based educational systems, in conjunction with ICCE’02, Auckland, New Zealand, Dec. 3–6, 2002. http://icce2002.massey.ac.nz/workshop_4.html. Ikeda, M., Hoppe, H. U., & Mizoguchi, M. (1995). Ontological issues of CSCL systems design. In Proceedings of Intl Conf. on Artificial Intelligence in Education (pp. 242–249). AIED’1995. ISWC (2004). Workshop on applications of Semantic Web technologies for e-learning, in conjunction with ISWC’04, Hiroshima, Japan, November 7–11, 2004. http://www.win.tue.nl/~laroyo/swel/2004/swel-iswc.html. ITS (2004). Workshop on applications of Semantic Web technologies for Webbased ITS, at ITS’04, 30 August – 03 September 2004, Maceió-Alagoas, Brazil. http://www.win.tue.nl/~laroyo/swel/2004/swel-its.html. Jena (2007). http://jena.sourceforge.net/. Accessed 14 June 2007.
64
D. Dicheva
Un
co
rre
cte
dP roo f
Jin, L., Chen, W., Hayashi, Y., Ikeda, M., Mizoguchi, M., Takaoka, Y., & Ohta, M. (1999). An ontology-aware authoring tool – functional structure and guidance generation. In Proc. of AIED’99 (pp. 85–92). Le Mans France, 1999. KAON (2007). http://kaon.semanticweb.org/. Accessed 14 June 2007. Kitamura, Y., & Mizoguchi, R. (2004). Ontology-based systematization of functional knowledge. Journal of Engineering Design, 15(4), 327–351. LOM (2007). Learning object metadata. http://ltsc.ieee.org/wg12. Accessed 14 June 2007. Merlot (2007). http://www.merlot.org/merlot/index.htm. Accessed 14 June 2007. Mitrovic, T., & Devedžic, V. (2002). A model of multitutor ontology-based learning environments. In ICCE Workshop on Concepts and Ontologies in WBES (pp. 15–22). Auckland, New Zealand, 3–6 December, 2002. Mitrovic, A. & Hausler, K. (2000). Porting SQL-tutor to the Web. In Proc. ITS’2000 Workshop on Adaptive and Intelligent WBES (pp. 37–44). Mizoguchi, R. (2004). Ontology engineering environments. In Staab, S., & Studer, R. (Ed.). Handbook on Ontologies (pp. 275–295). Berlin Heidelberg New York: Springer. Mizoguchi, R., & Bourdeau, J. (2000). Using ontological engineering to overcome common AI-ED problems. International Journal of Artificial Intelligence in Education, 11(2), 107–121. Mizoguchi, R., Sinitsa, K., & Ikeda, M. (1996). Knowledge engineering of educational systems for authoring system design – a preliminary results of task ontology design. In Proc. of European Conf on AIED (pp. 329–335). Lisbon. Mizoguchi, R., Hayashi, Y. & Bourdeau, J. (2007). Inside theory-aware and standards-compliant authoring System. In Intl AIED’07 Workshop on Ontologies and Semantic Web for e-Learning (SWEL’07), Los Angeles, California, July 9–13, 2007. Moodle (2007). http://moodle.org/. Accessed 14 June 2007. Naeve, A., Lytras, M., Nejdl, W., Balacheff, N., & Hardin, J. (Ed.) (2006). The Semantic Web for e-learning. British Journal of Educational Technology, 27 (3), special issue. NDSL (n.d.). The National Science Digital Library (NSDL). http://nsdl.org/. Nejdl, W., Wolf, B., Staab, S., & Tane, J. (2001). Edutella: searching and annotating resources within an RDF-based P2P network. In WWW2002 Semantic Web Workshop, Hawaii, USA, May 2002. Nilsson, M., Palmer, M., & Brase, J. (2003). The LOM RDF binding – principles and implementation. In 3rd Annual Ariadne Conference, Leuven, Belgium, 20–21 November 2003. OntoEdt. (2007). http://ontoedit.com/. Accessed 14 June 2007. Ontopoly (2007). http://www.ontopia.net/. Accessed 14 June 2007. Protégé (2007). http://protege.stanford.edu/. Accessed 14 June 2007. RDF (2007). http://www.w3.org/RDF. Accessed 14 June 2007. Ritter, S. (1997). PAT online: a model-tracing tutor on the World-Wide Web. In Proc of Workshop “Intelligent Educational Systems on the World Wide Web”. 8th World Conference of the AIED Society, Kobe, Japan, 18–22 August.
Ontologies and Semantiv Web for E-Learning
65
Un
co
rre
cte
dP roo f
Sampson, D., Lytras, M., Wagner, G., & Diaz, P. (Ed.) (2004). Ontologies and the Semantic Web for e-learning. International Journal on Education Technology and Society, 7(4), special issue. http://www.ifets.info/others/issues.php?id=25. SCORM (2007). Sharable Content Object Reference Model. ttp://www.adlnet.gov/. Accessed 14 June 2007. Sesame (2007). http://www.openrdf.org/. Accessed 14 June 2007. SWEL (n.d.). Workshop on application of the Semantic Web technologies in e-learning (SWEL). http://compsci.wssu.edu/iis/swel/index.html. The O4E Portal (2007). http://iiscs.wssu.edu/o4e/. Accessed 14 June 2007. The Omnibus Project (2007). http://edont.qee.jp/omnibus/doku.php. Accessed 14 June 2007. TM4L (n.d.). http://compsci.wssu.edu/iis/nsdl/ download.html. TMAPI (n.d.). http://tmapi.org/. Vassileva, J., & Deters, R. (1998). Dynamic courseware generation on the WWW. British Journal of Educational Technologies, 29(1), 5–14. Weber, G., & Brusilovsky, P. (2001). ELM-ART: an adaptive versatile system for Web-based instruction. International Journal of Artificial Intelligence in Education, 12(4), 351–384. XML Topic Maps (XTM) (2007). http://www.topicmaps.org/xtm.
