• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.5
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• pp.29-35
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• 2022

This paper proposes a fuel cell education framework installed on a Metaverse environment, which is to reduce the burden of education costs and improve the effect of education or learning. This Meta-Fuel cell platform utilizes the Unity 3D Web and enables not only theoretical education but also hands-on training. The platform was designed and developed to accommodate a variety of unit education contents, such as ppt documents, videos, etc. The platform, therdore, integrates ppt and video demonstrations for theoretical education, as well as software content "STACK-Up" for hands-on training. Theoretical education section provides specialized liberal arts knowledge on hydrogen, including renewable energy, hydrogen economy, and fuel cells. The software "STACK-Up" provides a hands-on practice on assembling the stack parts. Stack is the very core component of fuel cells. The Meta-Fuelcell platform improves the limitations of face-to-face education. It provides educators with the opportunities of non-face-to-face education without restrictions such as educational place, time, and occupancy. On the other hand, learners can choose educational themes, order, etc. It provides educators and learners with interesting experiences to be active in the metaverse space. This platform is being applied experimentally to a education project which is to develop advanced manpower in the fuel cell industry. Its improvement is in progress.

• Proceedings of the Korea Association of Information Systems Conference
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• 2004.05a
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• pp.265-268
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• 2004

최근 디지털 지식기반 사회에 대응하는 교육의 형태로 e-Learning이 교육적 대안으로 급부상하면서, 시스템의 상호 운영성 및 컨텐츠 명세, 활용을 지원하기 위한 표준화에 따른 연구가 국내외에서 급속도로 확산되고 있다. 특히, 국제표준기관에서 제시한 e-Learning 개발 환경을 위한 Learning Technology Standard Architecture(LTSA)와 Sharable Content Object Reference Model(SCORM)을 제 정하여 컨텐츠의 사용과 상호 호환을 가능하게 함으로써 e-Learning의 효율성을 증대시키고 산업 시장의 확장을 이룰 수 있다. 또한, 현재 많은 교육관련 업체에서는 SCORM 체계를 기반으로 한 학습 컨텐츠들을 개발하여 제공하고 있다. 따라서, 본 논문에서는 국제 표준 기술인 SCORM을 기반으로 개발된 학습 컨텐츠를 체계적으로 지원하기 위해 컨텐츠 관리 시스템 개발에 대한 기술을 정의하고, 다양한 관점의 컨텐츠 메타 데이터를 식별, 분류함으로써 컨텐츠의 생성과 저장, 검색 나아가 형상관리를 위한 기본 정보로 이용 가능하다. 또한 이들 메타 데이터를 기반으로 한 학습 컨텐츠 관리 시스템의 프로토타이핑을 제시함으로써 재사용성과 유지보수성 향상을 통해 컨텐츠 개발의 용이성과 품질 및 생산성을 높일 수 있다.

• Annual Conference of KIPS
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• 2004.05a
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• pp.951-954
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• 2004

최근 디지털 지식기반 사회에 대응하는 교육의 형태로 e-Learning이 교육적 대안으로 급부상하면서, 시스템의 상호 운영성 및 컨텐츠 명세, 활용을 지원하기 위한 표준화에 따른 연구가 국내외에서 급속도로 확산되고 있다. 특히, 국제표준기관에서 제시한 e-Learning 개발 환경을 위한 Learning Technology Standard Architecture(LTSA)와 Sharable Content Object Reference Model(SCORM)을 제정하여 컨텐츠의 사용과 상호 호환을 가능하게 함으로써 e-Learning의 효율성을 증대시키고 산업 시장의 확장을 이룰 수 있다. 또한, 현재 많은 교육관련 업체에서는 SCORM 체계를 기반으로 한 학습 컨텐츠들을 개발하여 제공하고 있다. 따라서, 본 논문에서는 국제 표준 기술인 SCORM을 기반으로 개발된 학습 컨텐츠를 체계적으로 지원하기 위해 컨텐츠 관리 시스템 개발에 대한 기술을 정의하고, 다양한 관점의 컨텐츠 메타 데이터를 식별, 분류함으로써 컨텐츠의 생성과 저장, 검색 나아가 형상관리를 위한 기본 정보로 이용 가능하다. 또한 이들 메타 데이터를 기반으로 한 학습 컨텐츠 관리 시스템의 프로토타이핑을 제시함으로써 재사용성과 유지보수성 향상을 통해 컨텐츠 개발의 용이성과 품질 및 생산성을 높일 수 있다.

• Journal of the Korean Society of Systems Engineering
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• v.19 no.2
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• pp.18-31
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• 2023

Exploring artificial intelligence and machine learning for nuclear safety has witnessed increased interest in recent years. To contribute to this area of research, a machine learning model capable of accurately predicting nuclear power plant response with minimal computational cost is proposed. To develop a robust machine learning model, the Best Estimate Plus Uncertainty (BEPU) approach was used to generate a database to train three models and select the best of the three. The BEPU analysis was performed by coupling Dakota platform with the best estimate thermal hydraulics code RELAP/SCDAPSIM/MOD 3.4. The Code Scaling Applicability and Uncertainty approach was adopted, along with Wilks' theorem to obtain a statistically representative sample that satisfies the USNRC 95/95 rule with 95% probability and 95% confidence level. The generated database was used to train three models based on Recurrent Neural Networks; specifically, Long Short-Term Memory, Gated Recurrent Unit, and a hybrid model with Long Short-Term Memory coupled to Convolutional Neural Network. In this paper, the System Engineering approach was utilized to identify requirements, stakeholders, and functional and physical architecture to develop this project and ensure success in verification and validation activities necessary to ensure the efficient development of ML meta-models capable of predicting of the nuclear power plant response.

• Journal of Science Education
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• v.41 no.3
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• pp.499-518
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• 2017

In this study, the meta-analysis technique was applied to investigate the effectiveness of gifted-mathematics programs on development of creativity. Studies conducted the outcomes form the 20 studies were used for meta-analysis. Research questions are as follows; first, what is the overall effect size of the gifted mathematics programs on development of mathematical creativity. Second, what are effect sizes of sub-group(fluency, flexibility, originality) analysis. Third, compare the effect sizes of those in compliance with the grade and the class type. Results from data analysis are as follows. First, the overall effect size for studies related the gifted-mathematical programs was .66, which is high. Second, it was found that each sub-group differed from its effect on learning outcomes. Fluency(.76) was the highest of all, which was followed by flexibility(.60) and originality(.50) in a row. Lastly, the overall effect size for gifted elementary school students related the gifted-mathematical programs was .69, which is high than gifted middle school students was .46.

• The KIPS Transactions:PartB
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• v.13B no.5 s.108
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• pp.533-540
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• 2006

The semantic web environment promise semantic search of heterogeneous data from distributed web page. Semantic search would resuit in an overwhelming number of results for users is increased, therefore elevating the need for appropriate personalized ranking schemes. Culture Finder helps semantic web agents obtain personalized culture information. It extracts meta data for each web page(culture news, culture performance, culture exhibition), perform semantic search and compute result ranking point to base user profile. In order to work efficient, Culture Finder uses five major technique: Machine learning technique for generating user profile from user search behavior and meta data repository, an efficient semantic search system for semantic web agent, query analysis for representing query and query result, personalized ranking method to provide suitable search result to user, upper ontology for generating meta data. In this paper, we also present the structure used in the Culture Finder to support personalized search service.

• Journal of Engineering Education Research
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• v.14 no.4
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• pp.11-19
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• 2011

The purpose of this study was to identify learning strategies by learning style of first-year engineering students in order to find implications for teaching and learning strategies in engineering education. This study was conducted with 273 first-year students in two universities in Korea. Following were the results: First, there were Sensing learners(72.2%), Visual learners(84.6%), Reflective learners(64.8%), and Sequential learners(58.2%) and the level of learning strategies was 3.28(SD=0.38). Secondly, the finding revealed that there was only significant difference in learning strategies on Information processing dimension and Active students demonstrated higher level of learning strategies than Reflective students. To be more specific, there were significant differences in cognitive, meta-cognitive, and internal and external management. For engineering education, implications for teaching strategies in classroom and self-regulated learning strategies were discussed.

• Journal of Intelligence and Information Systems
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• v.28 no.4
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• pp.27-40
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• 2022

Medical AI, which has lately made significant advances, is playing a vital role, such as assisting clinicians with diagnosis and decision-making. The field of chest X-rays, in particular, is attracting a lot of attention since it is important for accessibility and identification of chest diseases, as well as the current COVID-19 pandemic. However, despite the vast amount of data, there remains a limit to developing an effective AI model due to a lack of labeled data. A research that used federated learning on chest X-ray data to lessen this difficulty has emerged, although it still has the following limitations. 1) It does not consider the problems that may occur in the Non-IID environment. 2) Even in the federated learning environment, there is still a shortage of labeled data of clients. We propose a method to solve the above problems by using the self-supervised learning model as a global model of federated learning. To that aim, we investigate a self-supervised learning methods suited for federated learning using chest X-ray data and demonstrate the benefits of adopting the self-supervised learning model for federated learning.

• Journal of Gifted/Talented Education
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• v.22 no.3
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• pp.729-755
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• 2012

As an effort to understand alienated gifted students, we investigated learning characteristics and learning tactics of a scientifically gifted student with economic difficulty and physical disadvantage. The student we studied is attending the Saturday Physics Class which is an after school science activity offered by our university. We adopted techniques of qualitative case study. Participant observation was carried out at the field and the interview was done with the participant, his mother, and his teacher of 5th grade. Field documents and self-reports were used to understand the student synthetically. As a result, learning characteristics of the participant could be summarized as a spontaneous learning which originated from the internal motivation and struggle for learning to overcome the sense of inferiority and isolation from the peers. The participant adopted a strategic method for learning to satisfy his learning desire given the circumstance of socioeconomic and physical disadvantage: the three tactics we found were various learning routes, meta-cognitive ability and fervent response.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.10
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• pp.218-228
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• 2019

This study was conducted to review and analyze the academic-related satisfaction intervention programs for Korean nursing students and to provide evidence-based data. The data included in the meta-analysis were 25 studies published from 2001 to July 2018, and the design of the study consisted of 1 randomized controlled trial and 24 non-randomized controlled trials. The study subjects were nursing students from 1st to 4th grade, and the intervention program was conducted in theoretical and practical classes. The sample size was 1182 (mean: 47.3) in the experimental group and 1137 in the control group (mean: 45.5). The intervention program consisted of 1~16 weeks/1~16 sessions/7~240 minutes per session. Dependent variables were as follows: major satisfaction, learning satisfaction, satisfaction with the classroom practice, and satisfaction with the clinical practice. Satisfaction with the classroom practice (Hedges' g=0.876[95% CI: 0.405, 1.346]), satisfaction with the clinical practice (Hedges' g=0.515[95% CI: 0.312, 0.718]), and overall academic-related satisfaction (Hedges' g=0.630[95% CI: 0.371, 0.889]) were statistically significant and above intermediate levels in the meta-analysis. The study results are significant in that the objective results were confirmed by integrating the previous studies dealing with the academic-related satisfaction intervention program of nursing students.