• Journal of the Korea Convergence Society
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• v.9 no.1
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• pp.21-29
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• 2018

The purpose of this study is to explore supporting strategies for teaching and learning based on students' effect recognition and continuous intention in college flipped learning. It was analyzed 426 data by multivariate analysis of variance (MANOVA) by examining student's effect recognition and continuous intention on 15 flipped learning classes of K-university in Chungnam. The characteristics of learners were male, senior students, students who knew flipped learning, students who did not have previous experience, and students who were learning video at anytime. As a teaching strategy, it was found that effect recognition and continuous intention were high in the supplementary deepening flipped learning class and natural science or engineering area. As a teaching and learning supporting strategies, First, the university should develop and operate flipped class learning strategy program for females and low-grade students. Second, it should support the development of good flipped learning design and operation model of instructor. Third, it should support the development of high quality online learning contents that students can learn from time to time. Fourth, it should support the strengthening of teaching competency to develop and operate flipped learning classes. This study can be used as basic data to support and spread the effective flipped learning classes of the university in the future.

• Journal of Korean Home Economics Education Association
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• v.28 no.2
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• pp.21-39
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• 2016

The purpose of this study was to investigate the differences in the school life satisfaction of middle school students according to self-directed learning capability and emotion regulation strategy. The subject of this study were 499 middle school students in Daegu. In order to analyze the data, two way ANOVA were employed for analysis and $Scheff{\acute{e}}$ test for post-hoc analysis. The main finding of this study were as follows; First, there were significant differences in the school life satisfaction of middle school students according to self-directed learning capability and behavioral emotion regulation strategy. Second, there were significant differences in the school life satisfaction of middle school students by cognitive emotion regulation strategy. But there were no significant differences in the school life satisfaction according to self-directed learning capability and cognitive emotion regulation strategy. Third, there were significant differences in the school life satisfaction of middle school students according to negative avoidant and emotion regulation strategy. However there were no significant differences in the school life satisfaction according to self-directed learning capability and negative avoidant emotion regulation strategy.

• The Journal of Korean Association of Computer Education
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• v.14 no.1
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• pp.1-12
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• 2011

Educational Programming Language has been reported to expand thinking ability and to give help in creative problem solving ability by numerous researches. Researchers are verifying the educational effects of EPL by applying it to various area, but researches in effective application of EPL is yet incomplete. Thus, for effective application of EPL, this research has verified the project completing ability depending on studying style targeted to college senior students. As results of verification, first, the results showed significant differences in project completing abilities depending on information processing methods, and learners who preferred self-reflecting introspection showed high scores. Second, in learning style the divergers showed the highest scores. This research suggested the necessity of guidance and detailed planning of self-reflecting introspective activity in ideas that would be realized by learners through searching for factors that could enhance the degree of project completion in programming learning using EPL.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.32 no.2
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• pp.405-416
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• 2022

Current AI technology is improving the quality of life by using machine learning based on data. When using machine learning, transmitting distributed data and collecting it in one place goes through a de-identification process because there is a risk of privacy infringement. De-identification data causes information damage and omission, which degrades the performance of the machine learning process and complicates the preprocessing process. Accordingly, Google announced joint learning in 2016, a method of de-identifying data and learning without the process of collecting data into one server. This paper analyzed the effectiveness by comparing the difference between the learning performance of data that went through the de-identification process of K anonymity and differential privacy reproduction data using actual financial data. As a result of the experiment, the accuracy of original data learning was 79% for k=2, 76% for k=5, 52% for k=7, 50% for 𝜖=1, and 82% for 𝜖=0.1, and 86% for Federated learning.

• Journal of the Korean School Mathematics Society
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• v.13 no.4
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• pp.619-633
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• 2010

To offer insights in organizing professional development programs to promote teachers' substantial ongoing learning, this paper provides an overview of situative perspectives in terms of cognition as situated, cognition as social, and cognition as distributed. Then, it describes research findings on how mathematics teachers can enhance their knowledge and thus improve their instructional practices through participation in a professional development program that mainly provides opportunities to learn and analyze students' mathematical thinking and to perform mathematical tasks through which they interpret the understanding of students' mathematical thinking. Further, it shows that a knowledge of students' mathematical thinking is a powerful tool for teacher learning. In addition, it suggests that teacher-researcher and teacher-teacher collaborative activities influence considerably teachers' understanding and practice as such collaborations help teachers understand new ideas of teaching and develop innovative instructional practices.

• The Journal of Bigdata
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• v.5 no.1
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• pp.89-96
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• 2020

Deep learning using an artificial neural network has been recently researched and developed in various fields such as image recognition, big data and data analysis. However, federated learning has emerged to solve issues of data privacy invasion and problems that increase the cost and time required to learn. Federated learning presented learning techniques that would bring the benefits of distributed processing system while solving the problems of existing deep learning, but there were still problems with server-client system and motivations for providing learning data. So, we replaced the role of the server with a blockchain system in federated learning, and conducted research to solve the privacy and security problems that are associated with federated learning. In addition, we have implemented a blockchain-based system that motivates users by paying compensation for data provided by users, and requires less maintenance costs while maintaining the same accuracy as existing learning. In this paper, we present the experimental results to show the validity of the blockchain-based system, and compare the results of the existing federated learning with the blockchain-based federated learning. In addition, as a future study, we ended the thesis by presenting solutions to security problems and applicable business fields.

• Journal of the Korea Convergence Society
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• v.10 no.2
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• pp.13-20
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• 2019

For customized education, it is essential to search the learning path for the learner. The genetic algorithm makes it possible to find optimal solutions within a practical time when they are difficult to be obtained with deterministic approaches because of the problem's very large search space. In this research, based on genetic algorithm, the learning paths to learn 200 chords in 27 music sheets were optimized to maximize the learning effect by balancing and minimizing learner's burden and learning size for each step in the learning paths. Although the permutation size of the possible learning path for 27 learning contents is more than $10^{28}$, the optimal solution could be obtained within 20 minutes in average by an implemented tool in this research. Experimental results showed that genetic algorithm can be effectively used to design complex learning path for customized education with various purposes. The proposed method is expected to be applied in other educational domains as well.

• Journal of Digital Convergence
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• v.14 no.4
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• pp.251-258
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• 2016

Big data processing is a broad term for processing data sets so large or complex that traditional data processing applications are inadequate. Widespread use of smart devices results in a huge impact on the way we process data. Many organizations are contemplating how to incorporate or integrate those devices into their enterprise data systems. We have proposed a way to process big data by way of integrating Raspberry Pi into a Hadoop cluster as a computational grid. We have then shown the efficiency through several experiments and the ease of scaling of the proposed system.

• Annual Conference on Human and Language Technology
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• 1999.10e
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• pp.13-19
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• 1999

본 논문은 분산, 독립적인 다수의 문서 컬렉션으로부터의 검색결과를 병합하는 컬렉션 융합(collection fusion)문제에 대한 효과적인 랭킹방법을 제시한다. 일반적인 컬렉션 융합 문제란 분산되어 있는 다수의 문서 컬렉션에서 독립적이고 능동적인 검색기들의 검색결과를 효과적으로 랭킹(ranking) 병합하는 것인데, 각기 다른 특성을 가진 다수의 컬렉션을 동일한 검색기를 통하여 검색된 결과를 병합하는 환경과 서로 다른 알고리즘을 갖는 검색기를 통한 검색 결과 병합 환경으로 나누어 질 수 있다. 본 논문에서는 서로 다른 특성을 갖는 다수의 컬렉션을 서로 다른 알고리즘을 갖는 검색기들을 통하여 검색한 결과를 병합하는 방법을 제시한다. 각 컬렉션에 학습 질를 넣어 얻은 정보를 토대로, 실제 질의를 넣었을 때 각각의 컬렉션에서 나온 결과가 통합 결과 집합에서 차지하는 비율과 각 문서의 순위를 결정한다. 기존 연구에서 사용한 방법들은 랜덤성에 의존한 비결정적인 랭킹 방법을 제시하거나, 단순하게 검색결과 집합의 문서 수를 바탕으로 인터리빙(interleaving)하는 방법을 제시하였다. 본 논문에서는 학습 질의에서 나온 정보를 기반으로 결정적이면서도 보다 효과적인 랭킹 방법을 제시한다.

• Proceedings of the Korean Information Science Society Conference
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• 2004.10a
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• pp.85-87
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• 2004

유무선 인터넷 기술의 발전은 인터넷을 통한 개인 정보의 효율적인 공유 및 교환을 가능하게 하였다. 최근 이러한 분산 정보의 공유를 위한 네트워킹 기술로 P2P(Peer-to-Peer)가 많은 주목을 받고 있다. 현재 국내외의 많은 대학 및 기관에서 P2P에 관한 연구가 활발히 진행 중 이다. 하지만, 대부분의 P2P 시스템들은 파일공유 위주의 서비스를 제공하며 SETI@HOME을 필두로 한 일부 @HOME 프로젝트들만이 자원 공유 서비스를 제공하고 있다. 그러나 기존의 자원공유 P2P 서비스들은 특정한 목적을 위해 구성됨으로써 자원을 제공하는 일반 사용자는 단순히 자원을 제공할 뿐 그 이상의 역할을 수행할 수가 없다. 이에 본 논문에서는 P2P 시스템에 참여한 모든 사용자가 P2P의 자원 네트워크를 사용할 수 있도록 멀티 에이전트 기반의 자원 공유 P2P 시스템을 제안한다. 일반 사용자는 서비스 생성 프레임워크를 사용하여 자신에게 필요한 테스크 에이전트를 생성할 수 있으며, 스케줄러 및 분배 에이전트, 테스크 에이전트에 의해 수행되어진다. 또한 본 시스템은 group 및 peer의 관리를 위해 특성 학습 에이전트(Specific Learning Agent)의 학습기능을 사용함으로써 P2P가 가지는 불안전한 환경 및 신뢰성 문제를 해결하였다.