• Proceedings of the Korean Society of Computer Information Conference
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• 2021.01a
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• pp.345-347
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• 2021

본 연구는 최근 Horizon report 2020에서 소개하는 고등교육 분야에 영향을 줄 트렌드와 기술 사례들을 수집하여 고등교육의 미래 동향과 학습 분석적 측면에서 분석하였다. 고등 교육이 교수·학습을 중심으로 영향을 줄 트렌드로는 기술적 트렌드, 고등교육 트렌드, 경제적 트렌드 등이 있으며 기술 사례로는 적응형 학습, 인공지능/머신기능 기술의 교육적 활용, 학업 성취도 분석을 위한 학습 분석, 확장 현실 기술 등이 있다. 이 중 학습 분석 기술은 학습자의 학업 성취도를 높이기 위한 방법으로 사용되는 유용한 기술이기도 하며 고등교육에 영향을 줄 가장 핵심 트렌드이기도 하다. 그러나 현실 학습에 적용하는데는 데이터 격차, 품질 문제, 개인 정보보호에 대한 문제 등 윤리적 문제를 함께 고려해야한다. 본 연구를 기반으로 한 향후 학습 분석 시스템을 개발하고자 한다.

• Proceedings of the Korean Information Science Society Conference
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• 1999.10b
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• pp.333-335
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• 1999

일반적으로 자기구성 지도에서는 구조가 초기에 결정되어 학습이 끝날때까지 변하기 않기 때문에 각 문제에 대한 구조를 반복된 실험을 통해서 최적화시켜야 한다. 그러나, 지도의 구조가 학습중에 적절하게 변경된다면, 해당 문제에 가장 알맞은 구조의 지도를 생성할 수 있을 것이다. 이 논문에서는 기존의 적응형 자기 구성 지도의 비교사 학습방법에 LVQ 알고리즘을 이용한 교사 학습방법을 결합한 구조 적응형 자기 구성 지도 모델을 제안한다. 이 방법은 일반적인 자기구성 지도 알고리즘보다 작은 수의 노드를 가지고 높은 성능을 보일 뿐만 아니라, 자기 구성 지도의 특성인 위상 보존도 잘 이루어진다. 오프라인 필기 숫자 데이터로 실험한 결과, 제안한 방법이 유용함을 알 수 있었다.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.36S no.7
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• pp.81-89
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• 1999

Image obtained by incomplete communication always include noise, blur and distortion, etc. In this paper, we propose and apply the new spatial filter algorithm, called an optimal adaptive generalized stack filter(AGSF), which optimizes adaptive generalized stack filter(AGSF) using neural network weight learning algorithm of back-propagation learning algorithm for improving noise removal and edge preservation rate. AGSF divides into two parts: generalized stack filter(GSF) and adaptive multistage median filter(AMMF), GSF improves the ability of stack filter algorithm and AMMF proposes the improved algorithm for reserving the sharp edge. Applied to neural network theory, the proposed algorithm improves the performance of the AGSF using two weight learning algorithms, such as the least mean absolute(LAM) and least mean square (LMS) algorithms. Simulation results of the proposed filter algorithm are presented and discussed.

• Journal of the Korea Society of Computer and Information
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• v.9 no.3
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• pp.115-120
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• 2004

As a part of working on development of the learning management system, a adaptive learning management system which is able to provide individual learner with different learning contents or paths customized to learner's learning behaviors by expanding SCORM was proposed in this dissertation. In terms of instructional technology interrelated with technology of CBI and ITS, new learning environments and learner preferences were analyzed. A related laboratory system was implemented by packaging a process on how to expand the meta data for contents and a process on how to utilize the web-based learning contents dynamically. In order to evaluate the usability of the implemented system, a sample content was provided to some selected learners and their learning achievement resulted from the new learning environment was analysed. A result of the experiment indicated that the adaptive learning management system proposed in this dissertation could provide every learner with the different content tailored to their individual learning preference and behavior. and it worked also to promote the learning performance of every learner.

• The Journal of the Convergence on Culture Technology
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• v.8 no.6
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• pp.397-404
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• 2022

This study aims to examine the effects of college students' teaching-friendly learning activities on college life adaptation, and to examine whether these effects differ according to learner characteristics. To this end, 214 people who participated in the learning program of universities located in G area in the first semester of 2022 were targeted. In order to find out the effect of teaching-friendly learning activities on college students' adaptability to college life, frequency analysis, response sample t-verification, and one-way analysis of variance (one-way Anova) were conducted using SPSS 20.0. The results are as follows. First, it was found that teaching-friendly learning activities significantly increased the scores of academic adaptation, emotional adaptation, and social adaptation in college life adaptation. Second, there was no difference in adaptation to college life according to college. Third, The score of the level of attachment to universities was found to be higher in the female group than in the male group. Fourth, as a result of the analysis according to the grade, it was found that the third-grade group had a high level of attachment to universities. Conclusions and suggestions for these research results were presented.

• Journal of Internet Computing and Services
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• v.10 no.3
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• pp.151-157
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• 2009

A lot of E-Learning system is supplying the existent item difficulty based learning information to learner. And learner is doing learning contents according to the fixed learning course. It is difficult for learner to get efficient learning effect. Because learner has to belong to fixed item difficulty and learning course even thought learner has different degree that understand studying in learning course. This research proposed the learner adaptive E-learning system that is able to control the item difficulty and learning course to analyze the understanding degree of learner in learning course. In this result, learner is able to improve learning effect to get rid of fixed learning course using bi-directed learning such as off-line learning.

• The Journal of Korean Association of Computer Education
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• v.7 no.6
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• pp.37-45
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• 2004

With the rapid growth of the Web technologies, the Web has changed teaching and learning as well as our life in various ways. Web-based instruction (WBI) supports self-directed and creative learning at anytime anywhere. However, learners may experience the problems of disorientation and cognitive overload when the hyperspace that consists of learning materials is complex. In this paper, we present a Web-based adaptive hypermedia system based on cognitive load theory which can reduce cognitive loads that novices may experience when they learn programming

• Proceedings of the Korea Contents Association Conference
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• 2010.05a
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• pp.569-572
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• 2010

맞춤형 이러닝은 학습효과의 증진을 위한 방안으로 개인 맞춤형, 개인화 혹은 적응화 등의 개념이 제안되고 확장되었다. 본 논문에서는 초기 선호도, 흥미도 혹은 검색습관을 고려하는 방식으로부터, 특정한 학습객체를 자율 선택하여 반복 학습할 수 있도록 개념단위를 적용한 방식, 학습자의 능력을 고려한 최적 난이도 학습객체를 제공하는 방식 등 다양하게 제안되고 있는 현대적 개인 맞춤형 이러닝 체계들을 비교 분석한다. 개별 시스템에 따라 '평가'에 국한되거나 '평가'와 '학습'을 연계하는 경우가 존재하며, 이에 따른 적용에 의하여 학습환경과 맞춤형 제공방식 및 학습효과를 상호 연계할 수 있음을 밝힌다.

• Journal of KIISE:Software and Applications
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• v.35 no.1
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• pp.33-40
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• 2008

In this paper, we propose a method to enhance adaptability in a dialogue system using the reinforcement learning that reduces response errors by trials and error-search similar to a human dialogue process. The adaptive dialogue strategy means that the dialogue system improves users' satisfaction and dialogue efficiency by loaming users' dialogue styles. To apply the reinforcement learning to the dialogue system, we use a main-dialogue span and sub-dialogue spans as the mathematic application units, and evaluate system usability by using features; success or failure, completion time, and error rate in sub-dialogue and the satisfaction in main-dialogue. In addition, we classify users' groups into beginners and experts to increase users' convenience in training steps. Then, we apply reinforcement learning policies according to users' groups. In the experiments, we evaluated the performance of the proposed method on the individual reinforcement learning policy and group's reinforcement learning policy.

• The KIPS Transactions:PartB
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• v.10B no.7
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• pp.795-802
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• 2003

To live autonomously, intelligent agents such as robots or virtual characters need ability that recognizes given environment, and learns and chooses adaptive actions. So, we propose an action selection/learning mechanism in intelligent agents. The proposed mechanism employs a hybrid system which integrates a behavior-based method using the reinforcement learning and a cognitive-based method using the symbolic learning. The characteristics of our mechanism are as follows. First, because it learns adaptive actions about environment using reinforcement learning, our agents have flexibility about environmental changes. Second, because it learns environmental factors for the agent's goals using inductive machine learning and association rules, the agent learns and selects appropriate actions faster in given surrounding and more efficiently in extended surroundings. Third, in implementing the intelligent agents, we considers only the recognized states which are found by a state detector rather than by all states. Because this method consider only necessary states, we can reduce the space of memory. And because it represents and processes new states dynamically, we can cope with the change of environment spontaneously.