July 2006 Volume 9 Number 3 - CiteSeerX

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Kiu, C.-C., & Lee, C.-S. (<strong>2006</strong>). Ontology Mapping and Merging through OntoDNA for Learning Object Reusability. Educational Technology & Society, 9 (3), 27-42. Ontology Mapping and Merging through OntoDNA for Learning Object Reusability Ching-Chieh Kiu Faculty of Information Technology, Multimedia University, Jalan Multimedia, 63100 Cyberjaya, Malaysia cckiu@mmu.edu.my Chien-Sing Lee Faculty of Information Technology, Multimedia University, Jalan Multimedia, 63100 Cyberjaya, Malaysia cslee@mmu.edu.my ABSTRACT The issue of structural and semantic interoperability among learning objects and other resources on the Internet is increasingly pointing towards Semantic Web technologies in general and ontology in particular as a solution provider. Ontology defines an explicit formal specification of domains to learning objects. However, the effectiveness to interoperate learning objects among various learning object repositories are often reduced due to the use of different ontological schemes to annotate learning objects in each learning object repository. Hence, structural differences and semantic heterogeneity between ontologies need to be resolved in order to generate shared ontology to facilitate learning object reusability. This paper presents OntoDNA, an automated ontology mapping and merging tool. Significance of the study lies in an algorithmic framework for mapping the attributes of concepts/learning objects and merging these concepts/learning objects from different ontologies based on the mapped attributes; identification of a suitable threshold value for mapping and merging; an easily scalable unsupervised data mining algorithm for modeling existing concepts and predicting the cluster to which a new concept/learning object should belong, easy indexing, retrieval and visualization of concepts and learning objects based on the merged ontology. Keywords Learning object, Semantic interoperability, Ontology, Ontology mapping and merging, Semantic Web Introduction The borderless classroom revolves not only around the breaking down of geographical and physical borders but also, of cultures and knowledge. Realizing this vision however, requires orienting next-generation e-learning systems to address three challenges: first, refocusing development of e-learning systems on pedagogical foundations; second personalizing human-computer interaction in a seamless manner amidst a seemingly faceless technological world and third increasing interoperability among learning objects and among technological devices (Sampson, Karagiannidis & Kinshuk, 2002). A corresponding ontological solution, which addresses all these three issues, is Devedzic’s (2003) GET-BITS framework for constructing intelligent Web-based systems. Ontology is applied at three levels. The first is to provide a means to specify and represent educational content (domain knowledge as well as pedagogical strategies). The second application of ontology is to formulate a common vocabulary for human-machine interaction, human-human interaction and software-to-software agent interaction in order to enable personalization in presentation of learning materials, assessment and references adapted to different student models. The third application lies in systematization of functions (Mizoguchi & Bordeau, 2000) to enable intelligent help in learning systems, especially in collaborative systems. The concerns mentioned above point towards Semantic Web technologies as solutions. Layers in Semantic Web technologies are as shown in Figure 1 (Arroyo, Ding, Lara, Stollberg & Fensel, 2004). These layers appear to provide the solution to the third aspect mentioned by Sampson et al and Devedzic, which is also the focus in this paper. At the top most layer is the Web of trust, which relies on the proof and logic layers. The proof layer verifies the degree of trust assigned to a particular resource through security features such as digital signatures. The logic layer on the other hand, is formalized by knowledge representation data, which provides ontological support to the formation of knowledge representation rules. These rules enable inferencing and derivation of new knowledge. Basic description of data (metadata) is defined in the Dublin core such as author, year, location and ISBN. RDF (Resource Description Framework) schema and XML (eXtensible Markup Language) schema both provide means for standardizing metadata descriptions. RDF’s object-properties-values (OAV) building block creates a semantic net of concepts whereas XML describes grammars to represent document structure either through data type definitions (DTD) or XML schemas. Finally, all resources are tagged with uniform resource ISSN 1436-4522 (online) and 1176-3647 (print). © International Forum of Educational Technology & Society (IFETS). The authors and the forum jointly retain the copyright of the articles. Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear the full citation on the first page. Copyrights for components of this work owned by others than IFETS must be honoured. Abstracting with credit is permitted. To copy otherwise, to republish, to post on servers, or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from the editors at kinshuk@ieee.org. 27
identifiers to enable efficient retrieval. Unicode, a 16-bit character scheme, creates machine-readable codes for all languages. Problem Definition and Background Background to the Problem The Semantic Web layers discussed above can be applied to the retrieval and reuse of learning objects as shown in Figure 2 (Qin & Finneran, 2002). According to Mohan and Greer (2003), “learning objects (LO) are digital learning resources which meet a single learning objective that may be reused in a different context”. Basic metadata to describe a learning object are defined in the Dublin core followed by contextual information that allows reuse of a learning object in different contexts. However, metadata expressed in XML addresses only lexical issues. XML does not allow interpretation of the data in the document. XML allows only lexical interoperability. Figure 1. The Semantic Web stack Figure 2. Representation granularity Furthermore, metadata schemas are user-defined. Due to differences in metadata specifications, metadata standardization initiatives such as ADL’s Sharable Content Object Resource Model (SCORM) and IEEE’s Learning Object Model (LOM) are introduced. However, LO reusability is complicated due to differences in LO metadata standards. For example, LO metadata standards such as IEEE LOM and Dublin Core are RDF binding whereas SCORM is XML binding. Hence, there are admirable efforts to map different learning object standards (Verbert, Gasevic, Jovanovic & Duval, 2005) in order to increase interoperability between learning object repositories. Significance of this work is the retrieval and assembly of parts of learning objects to create new learning objects suitable for different learning objectives and contexts. 28
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ac1 aci acc Class1 Classi Classc ar
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the non-fired linguistic terms. Thi
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various linguistic terms to describ
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Table 13. The discovered learning p
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Morimoto, Y., Ueno, M., Kikukawa, I
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Lesson form Assessment Portfolios W
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Formal description method Developme
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Figure 6. Screen of teacher’s por
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Von Brevern, H. & Synytsya, K. (200
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SSTA builds the necessary formal gr
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Task Decomposition Task decompositi
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Preliminary task decomposition buil
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Cutshall, R., Changchit, C., & Elwo
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Data Collection Surveys were distri
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Figure 2. The factors perceived as
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References Al-Khaldi, M. A., & Al-J
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Appendix 1: Critical Success Factor
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Cagiltay, N. E., Yildirim, S., & Ak
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as well as a way of approaching lea
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case studies also yield to better u
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opportunities to choose any subject
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If everything had been designed in
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Teacher or self-study preferences:
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go between these two approaches and
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Microsoft (2000). Microsoft's clip
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One of the visualization techniques
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the interrelationships among concep
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The control group. On the first day
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among concepts although they had no
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learners to be attentive to learnin
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Jonassen, D. H. (2000). Computers a
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1985; Voyer et al.,1995) show that
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Procedure This study employed a fac
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Total (Females+Males) Spatial Visua
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Given the absence of any significan
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Ellis, T. (2001). Animating to impr
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Delwiche, A. (2006). Massively mult
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Gee (2003) argues that all knowledg
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attempted to guess which students w
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computers. There might also have be
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On the whole, the gaming community
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Bradley, C., & Froomkin, M. (2003).
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Pearce, C. (2001) Story as play spa
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How to encourage students to do exe
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dependent on field. This is an impo
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(preferences, history, etc.). The u
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expected from them. The teaching te
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Hussain, S., Lindh, J., & Shukur, G
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Moore & Ray (1999) argue for more a
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Quantitative methods The quantitati
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marginal homogeneity by comparing g
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with the attitude questions include
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materials to help teachers instruct
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Appendix A Statement 1. LEGO materi
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alternatives to high-quality, but r
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Because number of statements made i
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questions. If the student was instr
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Participant 45.16 19 2.38 6.50 .000
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condition, however, do not drastica
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Breiter, A., & Light, D. (2006). Da
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(Drucker, 1989). Therefore, data, p
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Supporting Conversations: Most of t
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Gorry, G. A., & Scott Morton, M. S.
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Deng, Y.-C., Lin, T., Kinshuk, Chan
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component provider is not required
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Quality: researchers with higher po
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Gibbs, W. J., Olexa, V., & Bernas,
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Visualization of Online Discussions
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MTRDS Design MTRDS is a Web-based p
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dominance or apprehension, and part
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Jeong, A. (2004). Methods and tools
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Ng’ambi, D., & Johnston, K. (2006
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understanding of how students acqui
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Figure 2: DFAQ screen showing the n
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Comparing the results of the same s
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