• Journal of Internet Computing and Services
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• v.4 no.5
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• pp.69-76
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• 2003

In this paper we show a new learning algorithm for pattern classification. This algorithm considered a scheme to find a solution to a problem of incremental learning algorithm when the structure becomes too complex by noise patterns included in learning data set. Our approach for this problem uses a pruning method which terminates the learning process with a predefined criterion. In this process, an iterative model with 3 layer feedforward structure is derived from the incremental model by an appropriate manipulations. Notice that this network structure is not full-connected between upper and lower layers. To verify the effectiveness of pruning method, this network is retrained by EBP. From this results, we can find out that the proposed algorithm is effective, as an aspect of a system performence and the node number included in network structure.

• Education of Primary School Mathematics
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• v.16 no.2
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• pp.107-122
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• 2013

The purpose of the study is finding the effective teaching and learning methods on the concepts of figures through exploring the change of students' cognitive structures before and after the peer teaching activities. The difference of the peer teacher's and student's cognitive structures was investigated for the activities. Three teams, six students of 5th grade, were selected from the S elementary school in Boyeon. To figure out the students' cognitive structures, pre and post in-depth interviews were conducted and analyzed. Both peer teachers' and learners' cognitive structures were changed. Peer teachers' cognitive structures were changed more positively than peer learners. A consistent systematic planation and continuous teacher support and effort are needed for the activities.

• Journal of Internet Computing and Services
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• v.16 no.6
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• pp.39-46
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• 2015

Knowledge space theory is a theory that provides a guidelines for human learners' possible education decisions and has been used in various educational environment. However, traditional methodologies using the knowledge space theory have always depended on handwork system and it is necessary to learn programming language such as Visual Basic and R, causing time consuming situations. In order to overcome those issues on the environment of education we propose a new Knowledge Structure Analysis System that not just analyzes learners' knowledge structures automatically but to provide critical learning path for the learners based on knowledge space theory. Proposed system is implemented by using rApache generating critical learning path computing Chi-square value. This provides an automatic way of analyzing knowledge structure in learners' knowledge space and shows systematic reviews for the knowledge space.

• The Journal of the Korea Contents Association
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• v.13 no.11
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• pp.1014-1023
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• 2013

Rapid developments of Information and Communication Technologies(ICT) have made people learn with online contents allowing learning at onilne universities. The environments of offering educational contents at online universities differ from those at offline-based ones, so that alternative variables need to be considered in order to enhance learning effectiveness in online settings. In this study, the effects of Self-Regulated Learning(SRL) and motivation on learner-instructor interactions and academic performance in an online university were addressed. As a result, SRL and motivation not only directly affected both interactions and achievements, but also indirectly affected achievements via interactions. Also, learner-instructor interactions were directly effective on learning achievements. The implications of the research included comprehensive understandings of the structural relationships of teaching- and learning-related variables on learning. Suggestions were made based on the results.

• Informatization Policy
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• v.22 no.2
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• pp.75-92
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• 2015

The purpose of this study was to analysis about the relationship among Cyber Home Learning System(CHLS) circumstance, learner construct, learning satisfaction in elementary school students. Base on the selected data which was gathered from Chungbuk & Deajeon elementary schools, Structural Equation Modeling was used to examine the relationship between factors. The main results of the research were as follow : First, CHLS circumstance factor was more effective on the CHLS satisfaction than CHLS learner construct. In CHLS circumstance factor, 'service' factor was more influential variable. Second, there is high correlation between CHLS circumstance factor and CHLS learner construct. In CHLS satisfaction, 'contents and design' factor was more influential variable. Third, the model fit, = 624.945 (p<.001), RMR .020, GFI .929, AGFI .878, NFI .927, CFI .930, RMSEA .102 was relatively satisfied with the standard using structural equation model. As based on this results, CHLS in elementary education needs elaborate circumstance for the effective achievement.

• Computational Structural Engineering
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• v.11 no.4
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• pp.361-370
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• 1998

본 논문에서는 역전파학습에 의한 신경망기법을 사용하여 구조물의 미지계수를 추정하는 기법을 연구하였다. 대형구조물의 경우 계측 또는 추정하여야 하는 자유도의 수가 많으므로 인하여 구조계수를 추정하는 데에는 많은 어려움이 존재한다. 이러한 어려움을 극복하기 위하여 부구조추정법과 부행렬계수를 사용하여 추정하고자 하는 미지계수의 수를 효율적으로 줄일 수 있도록 하였다. 구조물의 고유주파수 및 모드형상 등의 모드계수를 신경망의 입력자료로 사용하였으며, 추정하고자 하는 부재의 부행렬계수를 신경방의 출력자료로 사용하였다. 입력자료로 사용되는 모드계수에 포함되어 있는 계측오차 및 신호처리오차의 영향을 줄이기 위하여, 신경망의 학습과정에서 노이즈를 첨가하는 기법을 사용하였다. 일반적인 형태의 자켓구조물을 대상으로 수치해석을 수행함으로써 제안기법의 대형구조계에 대한 적용성을 검증하였다.

• Proceedings of the Korea Information Processing Society Conference
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• 2005.11a
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• pp.809-812
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• 2005

본 논문은 원격 교육 환경에서 자료구조 알고리즘을 학습자가 적극적인 상호작용을 통하여 학습자의 이해 능력에 맞추어 학습할 수 있도록 하는 시스템을 소개한다. 기존의 자료구조 알고리즘 학습 시스템들은 고정된 수치 데이터의 애니메이션을 보여주므로 여전히 일방적인 학습이며, 전시되는 예제들의 다양성이 부족하다. 본 시스템에서는 자료의 크기 및 알고리즘 실행속도의 조절과 알고리즘 실행시 코드추적 기능 등의 상호작용을 통하여 알고리즘에 대한 이해를 시각적으로 배가 시킨다. 이 시스템은 웹에서도 지원가능 하도록 플래시 액션스크립트 기반으로 구현하였다.

• Proceedings of the Korean Information Science Society Conference
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• 2005.07b
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• pp.724-726
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• 2005

베이지안망(Bayesian network)은 다수의 변수들 사이의 확률적 관계(조건부독립성: conditional independence)를 그래프 구조로 표현하는 모델이다. 이러한 베이지안망은 비감독학습(unsupervised teaming)을 통한 데이터마이닝에 적합하다. 이를 위해 데이터로부터 베이지안망의 구조와 파라미터를 학습하게 된다. 주어진 데이터의 likelihood를 최대로 하는 베이지안망 구조를 찾는 문제는 NP-hard임이 알려져 있으므로, greedy search를 통한 근사해(approximate solution)를 구하는 방법이 주로 이용된다. 하지만 이러한 근사적 학습방법들도 데이터를 구성하는 변수들이 수천 - 수만에 이르는 경우, 방대한 계산량으로 인해 그 적용이 실질적으로 불가능하게 된다. 본 논문에서는 그러한 대규모 데이터에서 학습될 수 있는 계층적 베이지안망(hierarchical Bayesian network) 모델 및 그 학습방법을 제안하고, 그 가능성을 실험을 통해 보인다.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2007.04a
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• pp.85-88
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• 2007

본 연구에서는 베이지안 망 구조 학습에서, 학습 데이터의 특정 부분집합이 학습된 망의 각연결 구조(edge)의 형성에 기여하는 정도를 정량화하는 방법을 제안한다. 생물학 정보의 분석 등에 베이지안 망 학습을 이용하는 경우, 제안된 방법은 망의 각 연결 구조의 형성에 특정 군집 데이터가 기여하는 정도의 정량화가 가능하다. 제안된 방법의 유효성을 보이기 위해, 벤치마크 베이지안 망을 이용하여 제안된 방법이 망 연결 구조에 대한 데이터 군집별 기여도를 효과적으로 정량화 할 수 있음을 보인다.

• Journal of Internet Computing and Services
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• v.10 no.1
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• pp.135-143
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• 2009

The purpose of this study was to explore factors that affected the satisfaction of students' experiences in an education course, with the educational method and educational technology designed with a blended learning strategy. Blended learning is currently recognized as a good solution for the problems posed by both online and face-to-face learning, because it has features like flexibility and accessibility by using tools supporting both individualization and socialization. This study is one case that illustrates how blended learning can be applied at the university level. Subjects were 56 students who had participated in the class and responded to the survey questions. The gathered data were analyzed by using Factor Analysis and the Structural Equation Model. Based on the results of Factor Analysis, data revealed 5 factors: learning motivation, previous experience, ability to use information & technology, capability of self-regulated learning, and learning satisfaction. The results of the Structural Equation Model revealed causal relationships among the aforementioned factors as follows: (a) there was a statistically meaningful causal relationship between "learning motivation" and "capability of self-regulated learning", (b) there was a statistically meaningful casual relationship between "previous experience" and "capability of self-regulated learning", and (c) "capability of self-regulated learning" directly affected "learning satisfaction".