• The Journal of Korean Association of Computer Education
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• v.9 no.5
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• pp.31-39
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• 2006

The change into the knowledge based information society requires a transformation of educational paradigm. Accordingly, intelligent learning and distance education are attracting a fair amount of attention. To apply the instructional learning method in this field, we need to consider a individualization of learning, as it were, abstraction of fact and path through learning, which is based on learner's traits, this focus entails a argument for individualized reasoning strategy. Therefore, in this paper, we design a learner's cognitive union, which is based on X-Neuronet(eXtended Neuronet), represent learner's hierarchical knowledge is able to self-learn, and grows adaptive union by proprietor. Additionally, we propose a individualized reasoning strategy, which relies upon learner's cognitive union, and verify the validity.

• Korean Journal of Cognitive Science
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• v.16 no.4
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• pp.369-386
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• 2005

Although Intelligent Tutoring System(ITS) offers individualized learning environment that overcome limited function of existent CAI, and consider many learners' variable, there is little development to be using at the sites of schools because of inefficiency of investment and absence of pedagogical content knowledge representation techniques. To solve these problem, we should study a method, which represents knowledge for ITS, and which reuses knowledge base. On the pedagogical content knowledge, the knowledge in education differs from knowledge in a general sense. In this paper, we shall primarily address the multi-complex structure of knowledge and explanation of learning vein using multi-complex structure. Multi-Complex, which is organized into nodes, clusters and uses by knowledge base. In addition, it grows a adaptive knowledge base by self-learning. Therefore, in this paper, we propose the 'Extended Neural Logic Network(X-Neuronet)', which is based on Neural Logic Network with logical inference and topological inflexibility in cognition structure, and includes pedagogical content knowledge and object-oriented conception, verify validity. X-Neuronet defines that a knowledge is directive combination with inertia and weights, and offers basic conceptions for expression, logic operator for operation and processing, node value and connection weight, propagation rule, learning algorithm.

• The KIPS Transactions:PartB
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• v.13B no.4 s.107
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• pp.421-428
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• 2006

Intelligent Tutoring System(ITS), which offers individualized learning environment that consider many learners' variable, is realized by the effective alternative to take the place of domain expert. Accordingly, research on Learning Companion System(LC) is currently noticing. However, to develop LCS which applies effective interaction, it is necessary to combine several LCs, and personalized knowledge base have to be made first. Therefore, in this paper, we propose the 'Knowledge Base Object Medel', which is based on connectionist' in cognition structure, represents learner's knowledge to self-learnig object, and grows adaptive object by proprietor, verify the validity. This model lays the groundwork for design of personalized knowledge base, offers clue to development of adaptive ITS using knowledge base object.

• The Journal of Korean Association of Computer Education
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• v.10 no.6
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• pp.79-89
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• 2007

The advent of e-Learning paradigm requires a various type of e-Learning models and systems which are appropriate to support effective teaching-learning process. Accordingly, the teaching-learning system using the Internet and the intelligent tutoring system(ITS) in e-Learning environment has attracted a fair amount of critical attention. However there is a wide gap between infrastructure of a present educational site and the u-learning environment. Therefore, in this paper, an ITS teaching-learning model is proposed and system is developed for a school environment, which is based on a learner's cognitive structure and applies a concept of u-Learning, and then is verified for validity. X-Neuronet, the developed system, offers a method of representing a learner's cognitive structure so as to apply the method for the efficient individualized learning.