• Proceedings of the Korean Information Science Society Conference
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• 1999.10b
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• pp.679-681
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• 1999

최근 웹의 폭발적인 성장으로 인하여 웹을 교육의 매개체로 활용하려는 노력이 활성화되고 있다. 그러나 현재 대부분의 웹 기반 교육 시스템들은 대체로 수동적이며, 정적인 하이퍼텍스트 위주이기 때문에, 학습상황을 수시로 점검할 수 있는 상호작용기능이 부족하고, 특정 학습자의 학습결과에 따른 동적인 학습환경의 제시가 어렵다. 일반적으로 웹기반 교육시스템은 다양한 지식계층의 사람들에게 노출되어 있기 때문에 보다 상세한 학습전략이 요구되며, 따라서 최근에는 기존의 지능형 교수시스템(Intelligent Tutoring System: ITS)에서 연구된 풍부한 기술들을 웹 환경에 도입함으로서 보다 지능적이며 적응력 있는 시스템개발에 관한 연구가 활성화되고 있다. 본 연구에서는 이러한 웹 기반 교육시스템에서의 문제점들을 해소하기 위한 한가지 방안으로 ITS의 동적 교수계획기법을 웹 기반 시스템에 도입한다. 문제영역으로 C 프로그래밍 언어 학습을 선정하여 이를 웹 기반 교수시스템으로 설계하고 구현하였다. 또한 기존 시스템들의 서버 집중형 구조에서 탈피하여 CORBA를 이용한 분산기반구조로 시스템 개발에 접근하였다.

• Journal of Fisheries and Marine Sciences Education
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• v.20 no.3
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• pp.462-472
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• 2008

Education is a field that has tried to make use of the advantages of computers since they were introduced to the world. Intelligent Tutoring System and multimedia have become methods of teaching students of Computer Science, Education, Psychology, and Cognitive Science. Until now, they have been designed and produced only on the basis of a very specific domain and format. However, in the education field, most learners ask for integrated service that is practical, realizable, and sensitive to technological change. Therefore, in this study, we would like to present the technological and formal integration model as an ITS model which acknowledges changes in the fields of technology and education. As a technological integration model, the integration model of traditional Symbolic Artificial Intelligence and Artificial Neural Networks was presented. As a formal integration model, three integration models were presented according to (a) the process of learning diagnosis (b) learners' action behaviors (c) intelligence service respectively.

• 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.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.05a
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• pp.662-665
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• 2013

ITS(Intelligent Tutoring System) is a system that provides active and flexible tutoring conditions to learners, having adopted artificial intelligence to overcome the limitations of CAI(Computer Assisted Instruction). However, the existing ITS has a few problems; the system provides the same contents to every learner, not considering main variants of their learning and achievement, characters and levels, and therefore, it does not generate satisfactory results; the system does not offer a properly designed course schedule. Therefore, this thesis proposes ARS(Adaptive Recommendation System), founded on ITS, that provides contents designed based on the characters and levels of learners. To catch the characters of learners, the important variant for successful learning, ARS applies and embodies a module of self-assessment test. Also, it puts weighs according to the areas of learning which is different from the simplified assessment that asks for short and mechanical answers for the purpose of knowing the levels of the learners.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2004.10a
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• pp.1061-1065
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• 2004

The knowledge-based society of the 21st century requires effective education and learning methods in each professional field because the development of human resource determines its competence more than any other factors. It is highly desirable to develop an intelligent tutoring system, which meets ever increasing demands of education and learning. Such a system should be adaptive to each individual learner's demands as well as the continuously changing state of the learning process, thus enabling the effective education. The development of a learning environment based on learner modeling is necessary in order to be adaptive to individual learning variants. An intelligent learning environment is being developed targeting the heritage education, which is able to provide a customized and refined learning guide by storing the content of interactions between the system and the learner, analyzing the correlations in learning situations, and inferring the learning preference from the learner's learning history. This paper proposes a heritage learning system of Bulguksa temple, integrating the ontology-based learner modeling and the learning preference which considers perception styles, input and processing methods, and understanding process of information.

• Journal of KIISE:Computing Practices and Letters
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• v.15 no.3
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• pp.226-230
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• 2009

Intelligent Tutoring System (ITS) is a procedure of analyzing collected data for teaming, making a strategy and performing adequate service for learners. To perform suitable service for learners, modeling is the first step to collect data from the process of their learning. The model, however, cannot be authentic if collected data can contain learners' inconsistent behaviors or unpredictable learning inclination. This study focused on how to sort normal and abnormal data by analyzing collected data from learners through visualization. A model has been set up to assort unusual data from collected learner's data by using DOLLS-HI which makes possible to diagnose learner's learning propensity based on housing interior learning contents in the experiment. The created model has been confirmed its improved reliability comparing to previous one.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2006.05a
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• pp.310-313
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• 2006

지능형 학습 시스템(ITS, Intelligent Tutoring System)은 학습자의 학습 스타일을 인지하여 학습자에 맞는 학습전략을 세우고 적절한 학습 서비스를 제공하는 시스템이다. 기존의 학습시스템은 학습자의 학습 스타일 보다는 학습 컨텐츠에 중심을 두어 학습자에게 맞는 학습 전략을 적절히 세우는 과정이 부족했다. 이에 본 논문에서는 학습자의 학습과정에서 발생한 데이터를 기반으로 학습자의 학습 스타일을 파악하는 방법을 제안한다. 이를 위해 서양 건축양식 학습을 위한 교육 컨텐츠를 이용하였으며, 수집된 데이터를 분석하여 Folder & Silverman 이 제시한 학습 스타일에 근거한 학습자의 학습 스타일을 추출하였다. 실험에서는 70명의 데이터를 수집하였고, 학습자가 교육 컨텐츠를 학습한 순서에 대한 시계열 데이터를 기반으로 학습자 성향을 알아보기 위하여 은닉 마코프 모델(Hidden Markov Model)을 사용하였다. 은닉 마코프 모델을 적용하여 얻은 분석 결과를 가지고 각 학습자에게 맞는 학습 스타일을 진단하였다. 은닉 마코프 모델에서 얻은 학습 스타일 진단 모델은 향후에 학습자 학습 스타일을 파악하는데 사용할 수 있으며, ITS에 있어 학습자 성향 분석 모듈로 고려해볼 수 있다.

• Proceedings of the IEEK Conference
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• 2006.06a
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• pp.807-808
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• 2006

While use of computers to teach English in a conventional educational environment promotes motivation and effective learning in students, the method generates problems such as provision of learning materials without consideration of teaching methods and evaluation without consideration of individual differences in students. To solve these problems and produce a superior system, we propose knowledge representation of expert module for an Intelligent Tutoring System (ITS).

• Korean Journal of Cognitive Science
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• v.4 no.2
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• pp.85-114
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• 1994

One of the major research topics in the area of the development of intelligent tutoring system(ITS)is the control of instructional mechanism consisting of lesson plans,curriculum plans,and discourse plans.This paper describes a method of building the lesson plans among these three instructional plans based on the case-based planning.It is more efficient to retrieve the lesson plan from the plan memoru than to generate it whenever an instructional goal is selected.The retrieved lesson plan may be modified to build more adaptive plan for the current goal.We have developed a lesson plan generator that has such capabilities as a component of an ITS for teching indefinite intergration.We also have devised a description language to represeint the generalized form for the given arithmetic expression as an instructional goal and a curriculum tree to represent the lesson units required to master the subject matter.The result of this research could be used either by a developer of the lesson plan generator in the other area of ITS or by human teacher as a curriculum in the actual class.

• 한국HCI학회:학술대회논문집
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• 2008.02b
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• pp.78-84
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• 2008

The traditional intelligent teachable system has mainly focused on knowledge and cognition. It has overlooked motivational aspects of learners. Motivation is an important factor in learning making learners to have interests in a given task and persist it. Although the systems include cognitive as well as motivational factors, the effects of ITS on interest are not equivalent depending on individual characteristics. This study is to investigate how influence learners' response patterns to their interests and also examined effects of individual characteristics on interest in teachable agent (TA). In this experiment, we used KORI which is a new type of ITS that learner teach computer agent based on the instructional method of learning by teaching'. In the beginning of experiments, metacognition, achievement goal orientation and self-efficacy were measured as individual characteristics. Then, participants were asked to use KORI at home during 10 days. After using KORI the level of interest were measured. The result showed that metacognition was positively related with interest, whereas performance goal orientation and mastery goal orientation were negatively related to interest. It suggests t hat different individual characteristics should be considered to promote learners' intrinsic motivation in TA.