• 한국HCI학회:학술대회논문집
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• 2006.02a
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• pp.519-525
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• 2006

Advances in information and telecommunication technology increasingly reveal the potential of computer supported education. However, most computer supported learning systems until recently did not pay much attention to different characteristics of individual learners. Intelligent learning environments adaptive to learner's preferences and tasks are desired. Each learner has different preferences and needs, so it is very crucial to provide the different styles of learners with different learning environments that are more preferred and more efficient to them. This paper reports a study of the intelligent learning environment where the learner's preferences are diagnosed using learner models, and then user interfaces are customized in an adaptive manner to accommodate the preferences. In this research, the learning user interfaces were designed based on a learning-style model by Felder & Silverman, so that different learner preferences are revealed through user interactions with the system. Then, a learning style modeling is done from learner behavior patterns using Decision Tree and Neural Network approaches. In this way, an intelligent learning system adaptive to learning styles can be built. Further research efforts are being made to accommodate various other kinds of learner characteristics such as emotion and motivation as well as learning mastery in providing adaptive learning support.

• Journal of Engineering Education Research
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• v.20 no.3
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• pp.25-31
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• 2017

As the world enters the era of the Fourth Industrial Revolution, which is represented by advanced technology, it not only changes the industrial field but also the education field. In recent years, Smart Learning has enriched learning by using diverse forms and technologies that utilize vast amount of information about learners' individual knowledge through the emergence of realistic and intelligent contents that combine high technology such as artificial intelligence, big data and virtual reality and there is an increasing interest in intelligent adaptive learning, which can customize individual education. Therefore, the purpose of this study is to explore intelligent adaptive learning method through recent smart education environment, beyond traditional writing-based communication education which is highly dependent on the competency of instructors. In this study, we analyzed the various learner information collected in the communication course and constructed a concrete teaching and learning method of intelligent adaptive learning based on the instructor's intended smart contents. The result of this study is expected to be the basis of highly personalized teaching and learning method of digital method in communication education which is emphasized in the fourth industrial revolution era.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.1
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• pp.39-45
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• 2007

In this paper, we developed an intelligent web based e-learning system based on multi agents. To do development of the system, we applied an inclination test that is based on the education theory to do grouping the desirable e-learning community. The proposed system, Intelligent Web based e-learning Multi Agent System (IMAS), is used the multi agents paradigm including learning manner by neural network for grouping of e-learning community and a new distributed multi agent framework proposed here.

• The Journal of Information Systems
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• v.2
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• pp.194-206
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• 1993

Intelligent remote learning system is a system that incorporate communication technology and others : a database engine, an intelligent tutorial system. Learners can study by themselves through the intelligent tutorial system. The existence of a communication, database and artificial intelligence enhance the capability of IRLS. According to Parsaye, an intelligent databases should have the following features : 1) Knowledge discovery. 2) Data integrity and quality control. 3) Hypermedia management. 4) Data presentation and display. 5) Decision support and scenario analysis. 6) Data format management. 7) Intelligent system design tools. I hope that this research of framework for IRLS paves for the future research. As mentioned in the above, the future work will include an intelligent database, self-learning mechanism using neural network.

• 한국정보교육학회:학술대회논문집
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• 2021.08a
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• pp.37-44
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• 2021

This study is about a strategy to analyze learning activities in an intelligent learning system. To this end, the conceptual definition of the intelligent learning system and the type of learning using the intelligent learning system were analyzed. The learning types were presented as individual, adaptive, competency-based, and blended learning, and although there are some differences, most of them have similar characteristics. In addition, learning activity analysis is based on data such as mouse clicks, keyboarding, and uploads generated by the system. Through this, basic analysis such as viewing time and number of uploads can be performed. However, more diverse learning analysis is needed for personalization and adaptation. It can judge not only learning attitude and achievement level, but also metacognitive level and creativity level. However, since the level of metacognition includes complex human cognitive activities, the teacher's intervention is required in the judgment of the intelligent learning system.

• 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.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.10 no.1
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• pp.49-53
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• 2010

e-learning and m-learning have some problems that data transmission frequently discontinuously, communication cost increases, the computation speed of mass data drops, battery limitation in the mobile learning environments. In this paper, we propose the PULIMS for u-learning systems. The proposed system intellectualize the education environment using intelligent mobile agent, supports the customized education service, and helps that learners feasible access to the education information through mobile phone. We can see the fact that the efficience of proposed method is outperformed that of the conventional methods. The PULIMS is new technology that can be used to learn whenever and wherever learners want in Ubiquitous education environment.

• The Journal of Korean Association of Computer Education
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• v.9 no.3
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• pp.121-128
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• 2006

This study suggested the new e-learning systems based on agent to provide an adaptable learning. In Semantic Web environment, to develop an ontology and an intelligent agent is essential for an adaptable e-learning systems. Especially, to develop a reasoning engine using analysis of learning content and learners' information can offer an effective e-learning system. Therefore, we developed an applying model to an adaptable e-learning systems and the various ontologies for Semantic Web environment. Moreover, we analyzed and developed ontologies within the framework of learning domain, a learner and interface. Further, we implemented an intelligent e-learning for applying an agent's reasoning. Through this system proposed, we suggested the new e-learning systems model for Semantic Web environment.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2004.04a
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• pp.422-425
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• 2004

Many believe that electronic distance teaming education transform higher education, saving money and improving learning qualify So, the open University, which teaches around 280,000 students at a distance, is examining the adaption of its distance teaching methods for the internet. But, there are only one type of distance learning education of one way direction. To understand all of a student which selected some of e teaming course, teacher must check that how many student to understand and what is the difficult problems. Without checking this condition, It will be a very difficult and boring distance learning course. In this paper, we introduce of intelligent learning contents of full duplex direction that teach understanding student and not understanding student. The computer simulation results confirms that full duplex e learning system has been proven to be much more efficient than one way direction which not considering about understanding problems.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1996.10a
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• pp.27-34
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• 1996

GENIE is a learning-based engine for building intelligent systems. Learning in GENIE proceeds by incrementally modeling its human or technical environment using a neural network and a genetic algorithm. The neural network is used to represent the knowledge for solving a given task and has the ability to grow its structure. The genetic algorithm provides the neural network with training examples by actively exploring the example space of the problem. Integrated into the training examples by actively exploring the example space of the problem. Integrated into the GENIE system architecture, the genetic algorithm and the neural network build a virtually self-teaching autonomous learning system. This paper describes the structure of GENIE and its learning components. The performance is demonstrated on a robot learning problem. We also discuss the lessons learned from experiments with GENIE and point out further possibilities of effectively hybridizing genetic algorithms with neural networks and other softcomputing techniques.