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A knowledge-driven model to personalize e-learning
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Source Journal on Educational Resources in Computing (JERIC) archive
Volume 6 ,  Issue 1  (March 2006) table of contents
Article No. 3  
Year of Publication: 2006
ISSN:1531-4278
Authors
Chao Boon Teo  Nanyang Technological University, Singapore
Robert Kheng Leng Gay  Nanyang Technological University, Singapore
Publisher
ACM  New York, NY, USA
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ABSTRACT

This article highlights basic issues that have hindered e-learning systems from becoming the revolutionary force it could be for education. While current systems aim to foster significant improvements in learning, this article argues that most systems are still limited to just being online repositories. This and the lack of learning personalization has become a topic for research. A knowledge-driven model to personalize e-learning is proposed in this article. A novel methodology for eliciting and personalizing tacit knowledge is presented. We focus on describing the complex information processing in terms of knowledge, rather than the details of its implementation.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
1
Ackoff, R. L. 1997. Transformational consulting. Management Consulting Times 28, 6.
 
2
Adler, C. and Rae, S. 2002. Personalized learning environments: The future of e-learning is learner-centric. E-learning 3, 1, 22--24.
3
 
4
Barr, R. B. and Tagg, J. 1995. From teaching to learning: A new paradigm for undergraduate education. Change 27, 6, 13-25.
 
5
Barritt, C. and Wieseler, W. 1999. Reusable Information Object Strategy, 1999. Cisco Systems, 1--32.
 
6
Berners-Lee, T., Hendler, J., and Lassila, O. 2001. The semantic web. Scientific American (May).
 
7
Chee, S. 2004. Distance education and e-learning in the digital age: Critical considerations. In (2004), Intelligent Virtual World: Technologies and Applications in Distributed World Environments, T. Shih, ed. 289--308.
 
8
 
9
Cole, R. J. and Eklund, P. W. 1996. Applications of Formal Concept Analysis to Information Retrieval Using a Hierarchically Structured Thesaurus Conceptual Structures: Knowledge Representation as Interlingua. P. Eklund et al. eds., Springer Verlag, New York.
 
10
 
11
EU-NSF Strategic Workshop Report. 2001. (Sophia-Antipolis, France, Oct. 3rd--5th).
 
12
 
13
Gardner, H. 1999. Intelligence Reframed: Multiple Intelligences for the 21st entury Basic Books, New York.
 
14
Gay, K. L. and Teo, C. B. 2006. Redefining E-learning. In Proceedings of Digital Learning Asia 2006 Conference (Bangkok, April 26--28).
 
15
Grant, R. M. 1997. The knowledge-based view of the firm: implications for management practice. Long Range Planning 30, 3, 450--454.
 
16
Henze, N., Dolog, P., and Nejdl, W. 2004. Reasoning and ontologies for personalized e-learning. ETS J. Special Issue on Ontologies and Semantic Web for eLearning.
 
17
Kostas, M., Psarras, J., and Papastefanatos, S. 2002. Knowledge and information management in e-learning environments: The user agent architecture. Inf. Manage. Comput. Security 10, 4, 165--170.
 
18
McCalla, G. 2004. The ecological approach to the design of E-learning environments: Purpose-based capture and use of information about learners. J. Interactive Media Education 7. Special issue on the educational semantic web.
 
19
Miltiadis, D. L. and Pouloudi, N. 2001. E-learning: Just a waste of time. In Proceedings of the Seventh Americas Conference on Information Systems (AMCIS 2001, Boston, MA, Aug. 3--5). D. Strong et al. eds., 216--222.
 
20
Polanyi, M. 1958/1962. Personal Knowledge. Corrected ed., Routledge, London.
 
21
Polsani, P. R. 2003. Use and abuse of reusable learning objects. JoDI--J. Digital Inf. 3, 4.
 
22
 
23
 
24
Sarker, S. and Nicholson, J. 2005. Exploring the myths about online education in information systems. Inf. Science J. 8, 55--73.
 
25
Teo, C. B. and Gay, K. L. 2004a. Concept map approach to E-learning. In Proceedings of the World Conference on E-Learning in Corporations, Government, Health, and Higher Education 2160--2165.
 
26
Teo, C. B. and Gay, K. L. 2004b. Concept-based system design to personalize E-learning. WSEAS Trans. Inf. Science Applications 1, 5 (Nov.), 1248--1255.
 
27
Teo, C. B. and Gay, K. L. 2005a. Content authoring system to personalize E-learning. In Proceedings of the 5th WSEAS International Conference on Distance Learning and Web Engineering (Corfu, Greece, August 23--25), 105--110.
 
28
Teo, C. B. and Gay, K. L. 2005b. Personalization issues in E-learning. WSEAS Trans. Inf. Science Applications 2, 10 (Oct.), 1514--1522.
 
29
Teo, C. B. and Gay, K. L. 2006a. Concept map provision for E-learning. Int. J. Instructional Technology Distance Learning 3, 7, 17--32.
 
30
Teo, C. B. and Gay, K. L. 2006b. Provision of self-directed learning using concept mapping. WSEAS Trans. Advances Eng. Education 3, 6 (June), 491--498.
 
31
Teo, C. B, Chang, S. C., and Gay, K. L. 2006. Pedagogy considerations for E-learning. Int. J. Instructional Technology Distance Learning 3, 5 (May), 3--26.
 
32
Valtchev, P., Missaoui, R., and Godin, R. 2004. Formal concept analysis for knowledge discovery and data mining: The new challenges. In Concept Lattices: Proceedings of the Second International Conference on Formal Concept Analysis, LNCS 2961. P. Eklund ed., Springer Verlag, Berlin, 352--371.
 
33
Wille, R. 1992. Concept lattices and conceptual knowledge systems. Computers and Math. Applications 23, 493--515.


Collaborative Colleagues:
Chao Boon Teo: colleagues
Robert Kheng Leng Gay: colleagues