• Journal of Digital Convergence
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• v.16 no.3
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• pp.527-532
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• 2018

This study aims to examine the effectiveness of leisure activity on learning agility and to verify the mediative effectiveness of interpersonal relationship between leisure activity and learning agility for youths. To achieve these purpose, 6,637 data that sampled by 'IEA ICCS 2016' were analyzed with structural equation. The results of study were as follow. First, leisure activity directly effects on learning agility for youths. Second, leisure activity directly effects on interpersonal relationship. Third, interpersonal relationship directly effects on learning agility and partial mediative effects between leisure activity and learning agility. These results implies that youths' leisure activity is not only directly affecting learning agility, but also affecting interpersonal relationship. And this interpersonal relationship also effects learning agility. Based on these results, some practical implications were proposed to develop learning agility in terms of leisure activity and interpersonal relationship.

• Journal of The Korean Association of Information Education
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• v.16 no.2
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• pp.173-180
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• 2012

This study looks for the requirements for adaptive courseware and provides background for developing effective adaptive courseware. Most adaptive courseware does not adequately reflect the requirements of learners. If it properly reflected learners' requirements well, it could be used more effectively. therefore, this study analyzes and suggests the requirements of an adaptive courseware for pre-service teachers who are majoring in computer education. The study methods were factor analysis and frequency analysis through survey. The results show that the important elements of an adaptive courseware are a learning style designed according to individual differences as well as an instruction model appropriate to the subject domain. The study examined adaption by learner level, by learning objective, by learning style, by method of learning content, and by learning history in that order. Therefore while the most of adaptive courseware support learning style; we propose that the adaptive courseware will support learning objects and instruction model as well.

• Proceedings of the Korean Information Science Society Conference
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• 2003.10a
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• pp.136-138
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• 2003

로봇이나 가상 캐릭터와 같은 지능형 에이전트가 자율적으로 살아가기 위해서는 주어진 환경을 인식하고, 그에 맞는 최적의 행동을 선택하는 능력을 가지고 있어야 한다. 본 논문은 이러한 지능형 에이전트를 구현하기 위하여, 외부 환경에 적응하면서 최적의 행동을 배우고 선택하는 방법을 연구하였다. 본 논문에서 제안한 방식은 강화 학습을 이용한 행동기반 학습 방법과 기호 학습을 이용한 인지 학습 방법을 통합한 방식으로 다음과 같은 특징을 가진다. 첫째, 외부 환경의 적응성을 수행하기 위하여 강화 학습을 이용하였으며. 이는 지능형 에이전트가 변화하는 환경에 대한 유연성을 가지도록 하였다. 둘째. 경험들에서 귀납적 기계학습과 연관 규칙을 이용하여 규칙을 추출하여 에이전트의 목적에 맞는 환경 요인을 학습함으로 주어진 환경에서 보다 빠르게, 확장된 환경에서 보다 효율적으로 행동을 선택을 하도록 하였다. 제안한 통합방식은 기존의 강화 학습만을 고려한 학습 알고리즘에 비하여 학습 속도를 향상 시킬수 있으며, 기호 학습만을 고려한 학습 알고리즘에 비하여 환경에 유연성을 가지고 행동을 적용할 수 있는 장점을 가진다.

• Proceedings of the Korean Information Science Society Conference
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• 2001.10b
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• pp.88-90
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• 2001

정보여과 에이전트는 자체의 적응성(adaptability)과 자율성(autonomy)을 특징으로 사용자의 선호도와 관심을 학습하여 사용자 프로파일을 지식베이스의 일부로 구축하는 기능을 수행한다. 이러한 사용자 프로파일은 사용자의 학습의도에 맞게 지식을 탐색하고 축적하는 적응성(adaptability)을 가져야 한다. 본 논문에서는 지능적 정보여과 에이전트가 사용자의 선호도와 관심을 학습하여 적응적인 사용자 프로파일을 구축하기 위한 기법으로서, 사용자가 제시한 학습예제로써의 웹 문서들로부터 사용자의 학습의도를 내포한 질의어를 중심으로 연관 지식을 탐색하여 추출하는 웹 도큐먼트 기반 사용자 중심 연된 객체 추출과 만유인력 모델을 기반으로 한 연관 객체 관계성 가중치 기법을 제시한다.

• The Journal of the Korea Contents Association
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• v.4 no.1
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• pp.8-17
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• 2004

In this paper, we design an intelligent loaming management logics which provide personalized teaming considering adaptive learning content dement and content sequencing. We enhance the existing functional model including adaptive learning management functions. Also, we present a system architecture to implement the adaptive learning management system. We realize the adaptive teaming management system based on the SCORM run-time engine.

• Journal of Convergence for Information Technology
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• v.9 no.6
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• pp.104-111
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• 2019

In this study, we examined the effects of University adaptation on career adaptation through self-leadership and learning commitment, and identified the regulatory effects of demographic characteristics (sex, grade, grade level, and willingness to work). To this end, we analyzed the data for 289 college students. The results of the study are as follows. First, adaptation to University life has a significant impact on learning commitment and self-leadership. However, adaptation to University life has an indirect effect on learning commitment and self-leadership rather than directly affecting career adaptation. Second, demographic characteristics have a regulatory effect on the path through which University adaptation affects career adaptation through self-leadership and learning commitment. The factors that were regulated were age, grade, and willingness to work. Therefore, in order to adapt to university life and adapt to career, we developed a program to improve self-leadership and learning commitment, and suggested that customized guidance is needed considering the differences in personal characteristics such as gender and academic background in university life and career counseling.

• The Journal of the Korea Contents Association
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• v.14 no.6
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• pp.529-538
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• 2014

This study was conducted as an need analysis through close and open semi-structured surveys, in order to identify the adaptive elements of the adaptive e-learning system. The study was conducted on students majoring computer education in teacher's college. In terms of the process of the need analysis, Rossett Model was applied. For the research method, responses on the open questionnaire were analyzed. In terms of the analysis method, coding was used to extract the theme of the content, and through the constant comparison method, categorizing took place. As the element that offers adaption in the adaptive learning system, it escapes from the existing learning style, and recognized the importance of providing adaptability for different elements such as the learner's level, learning objectives, and learning contents. Especially, An instructional model was identified as an important element that helps reach rationality as well as efficiently conduct the learning objectives.

• Journal of The Korean Association of Information Education
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• v.17 no.4
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• pp.383-391
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• 2013

This study developed an adaptive instruction system for individual learning. We applied Gregorc's learning style to support the adaption. This proposed Instruction system provides instruction content based on a more effective instruction method that takes into consideration the learner's individual learning style. We applied Gregorc's learning style to create a clear treatment of differing learning styles. Proposed adaptive instruction system was applied to college students studying computer learning. A t-test indicated that our proposed adaptive instruction system resulted in positive effects in an overall style comparison. In addition, our Abstract-Sequential learning style indicated the most effective results while the Abstract-Random learning style indicated no difference between the experimental and comparative groups in each style analysis for Gregorc's learning style.

• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2000.08a
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• pp.121-124
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• 2000

적응 학습 알고리즘으로 가중치를 변화시키는 단층 신경망의 출력부에 Ramp 임계 함수를 적용하여 입력이 zero-mean Gaussian random vector인 경우 가중치의 stationary point를 구하고, 적응 학습 알고리즘을 유도한다.

• 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.