Overexcitaility (OE) as a concept that is related to developmental potential, has been shown to differ by intelligence, gender, involvement in school programs and artistic interest in American populations of students. Overexitability, used to describe the five ways that people might experience developmental potential for emotional growth, are emotional, intellectual, imaginational, sensual, and psychomotor. Little is known about the profiles of groups of gifted learners outside of studies conducted in the United States. In order to better understand the emotional needs of Korean students, the purpose of this study was to determine the overexcitability profiles of students enrolled in four high schools, each with a different domain focus: math and sciences, visual and performing arts, and foreign languages. 341 subjects of this study completed the Overexcitability Questionnaire II. Multivariate Analysis of Variance (MANOVA) was conducted to determine statistical differences. The results showed that Mean scores of psychomotor, sensual and imaginational are highest in the Art High School, intellectual is highest in the Science High School and emotional is highest in the Foreign Language High School. There were significant differences among the schools. Each major also showed significant difference. The results showed that mean score of psychomotor is highest in the Dance major, sensual, imaginational and emotional are highest in the Drama majore and intellectual is highest in the Science major. The results showed that the mean scores of psychomotor, imaginational and intellectual are higher in the male students than female students. On the other hand the mean scores of sensual and emotional are higher in the female students than in the male students.
Students' perception of science-related career strongly influences the formation of career motivation in science. Especially, the high level of science gifted students' positive perceptions plays an important role in allowing them to continue to study science. This study compared perceptions of science-related career between general and gifted middle school students using semantic network analysis. To ensure this end, we first structuralize semantic networks of science-related careers that students perceived. Then, we identified the characters of networks that two different student groups showed based on the structure matrix indices of semantic network analysis. The findings illustrated that the number of science-related careers shown in science gifted students' answer is more than in general students' answer. In addition, the science gifted students perceived more diverse science-related careers than general students. Second, scientific career such as natural scientists and professors were shown in the core of science gifted students' perception network whereas non-research oriented careers such as science teachers and doctors were shown in the core of general students' perception network. In this study, we identified the science gifted students' perceptions of science-related career was significantly different from the general students'. The findings of current study can be used for the science teachers to advise science gifted students on science-related careers.
This study extracts topics related to university education through newspaper articles and analyzes the characteristics of each topic and the reporting patterns of each newspaper. The 9 topics were discovered using LDA. Topic 1 and Topic 3 are related to university support projects for education, but Topic 3 is focused on local universities. Topic 2 is about university education after COVID-19, Topic 4 teaching-learning methods, Topic 5 government policies, Topic 6 the high school education contribution university support projects, Topic 7 the university education vision, Topic 8 internationalization, and Topic 9 the entrance exam. The Chosun Ilbo, Kyunghyang, and Hankyoreh reported a lot of articles associated to lectures after COVID-19, government policies, and comments on university education. Relevant articles since 2016 have been analyzed by newspaper type and before/after COVID-19 through which differences in the topics were studied and discussed. These findings would suggest a basic policy guideline for university education and imply that the positive and negative effects of the media need to be considered.
In this paper, we analyzed the misconceptions and errors incurred during factorization learning. We also examined whether online individualization classes had a positive effect on students' mathematical achievement. The experiment was conducted for 4 weeks (16 times in total) on middle school juniors in rural areas of Gyeonggi Province, where the influence of private extra education was small. In the class, the 'Google Classroom' was used as a LMS, the video lecture was uploaded to YouTube, and the teacher interacted with the students through "Zoom" and "Facetalk". In the online class situation, students' assignments and test answers were checked in real time through 'Google Classroom', and immediate feedback was provided to the experimental class group's students. However, for the control group students, feedback was provided only to those who desired. A total of 7 achievement evaluations were conducted in the order of pre-test, formative evaluation (5 times), and post-test to confirm the change in students' ability improvement and achievement. Through the formative evaluation analysis, it was possible to grasp the types of errors and misconceptions that occured during the factorization process. Students' errors were divided into four types: theorem or definition distortion error, functional errors such as calculation, operation, and manipulation, errors that do not verify the solution, and no response. As a result of ANCOVA, the two groups did not show any difference from the 1st to 4th formative assessment. However, the 5th formative assessment and post-test showed statistically significant differences, confirming that online individualization classes contributed to improvemed achievement.
This article deals with one of the pansori master's life and activities. Bak Songhui(1927~2017), who was the holder of National Intangible Cultural Asset No. 5 for pansori Heungboga. She had played a significant role through the modern history of pansori genre including Yeoseong Gukkeuk(Korean classical opera by women) and Changgeuk(Korean traditional opera in pansori style) as well as original pansori itself. In the article, the early stage of her learnings and the way she got involved to pansori from Gwonbeon period are offered, and the activities by group, solo recitals, and educational activity lists are also provided. Bak Songhui began to learn pansori, Geommu(dance), Seungmu(dance), Gayageum, Yanggeum, and Gagok genres at her age of 13 in Gwangju. She fulfilled 5 years of study in Gwangju Gwonbeon, and entered to a Hyeomnyulsa-travelling theater company, led by Gim Yeonsu at her age around 19. Later, Bak used to be an actress in Yeoseong Gugak Donghohoe(Female Korean music fans' club) led by Gim sohui as well as in Haennim Gukkeukdan, and Saehan Gukkeukdan at around her age of 30. She took the main actress' role in several performances. And thanks to her effort, the Yeoseong Gukkeuk can be one of the representative genre in history. As she entered to the National Changgeuk company, her brilliant talents worked well by leading the company's big hit with her talents of taking many different characters, devotions, and know-hows from her experience. After her 70s, she kept the pansori go on its right way to pass down. She unfolded pansori performances as well as her own students' public presentations, recordings, TV and radio broadcasting activities as the holder of National Intangible Cultural Asset. The activities that Bak Songhui showed us can become another chance to make her a great master of pansori, especially in Dongpyeonje style.
Jeong, Jin-Lip;Won, Bo-Young;Yoo, Ho-Dong;Kim, Tag Gon;Kang, Dae-Hyun;Hong, Kyung-Jin
Journal of the Korea Society for Simulation
/
v.31
no.1
/
pp.29-41
/
2022
As one of the alternatives to solve the problem of unstable food supply and demand imbalance caused by abnormal climate change, the need for plant factories is increasing. Airflow in plant factory is recognized as one of important factor of plant which influence transpiration and heat transfer. On the other hand, Digital Twin (DT) is getting attention as a means of providing various services that are impossible only with the real system by replicating the real system in the virtual world. This study aimed to develop a digital twin model for airflow prediction that can predict airflow in various situations by applying the concept of digital twin to a plant factory in operation. To this end, first, the mathematical formalism of the digital twin model for airflow analysis in plant factories is presented, and based on this, the information necessary for airflow prediction modeling of a plant factory in operation is specified. Then, the shape of the plant factory is implemented in CAD and the DT model is developed by combining the computational fluid dynamics (CFD) components for airflow behavior analysis. Finally, the DT model for high-accuracy airflow prediction is completed through the validation of the model and the machine learning-based calibration process by comparing the simulation analysis result of the DT model with the actual airflow value collected from the plant factory.
Myung-Soo, Kim;Byung-Nam, Min;Seung-Hwan, Lee;Sung-Hee, Kim;Jae-Hoon, Kim
Journal of the Korea Society of Computer and Information
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v.28
no.1
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pp.129-136
/
2023
In this paper, we propose the positive effects of Virtual Reality(VR) sports classes and the foundation for VR sports to become the basis of lifelong sports education through the application of physical education classes in sports virtual reality programs are to be provided. For this purpose, the effect of VR sports experience on sports satisfaction, sports immersion, and sports attitude factors was investigated for 281 elementary school students in Busan. Results It was found that VR sports experience had a significant effect on sports satisfaction, sports satisfaction had a significant effect on sports immersion and sports attitude, and sports immersion had a significant effect on sports attitude. The great advantage of sports virtual reality is that sports activities for items that are difficult to deal with in physical education classes and unpopular items will be easily performed. In addition, by using a program that links physical education classes with English and mathematics, physical education will be recognized as a convergence subject by elementary school students, and at the same time, it will become an integrated subject that can acquire fun elements and learning elements at the same time through play or games.
The Journal of the Korea institute of electronic communication sciences
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v.18
no.6
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pp.1143-1150
/
2023
The global climate crisis and the implementation of low-carbon policies have led to a growing interest in renewable energy and a growing number of related industries. Among them, solar power is attracting attention as a representative eco-friendly energy that does not deplete and does not emit pollutants or greenhouse gases. As a result, the supplement of solar power facility is increasing all over the world. However, solar power is easily affected by the environment such as geography and weather, so accurate solar power forecast is important for stable operation and efficient management. However, it is very hard to predict the exact amount of solar power using statistical methods. In addition, the conventional prediction methods have focused on only short- or long-term prediction, which causes to take long time to obtain various prediction models with different prediction horizons. Therefore, this study utilizes a many-to-many structure of a recurrent neural network (RNN) to integrate short-term and long-term predictions of solar power generation. We compare various RNN-based very short- and long-term prediction methods for solar power in terms of MSE and R2 values.
Nguyen, Van Giang;Nguyen, Van Linh;Jung, Sungho;An, Hyunuk;Lee, Giha
Journal of Korea Water Resources Association
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v.56
no.12
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pp.939-953
/
2023
Shallow water equations (SWE) serve as fundamental equations governing the movement of the water. Traditional numerical approaches for solving these equations generally face various challenges, such as sensitivity to mesh generation, and numerical oscillation, or become more computationally unstable around shock and discontinuities regions. In this study, we present a novel approach that leverages the power of physics-informed neural networks (PINNs) to approximate the solution of the SWE. PINNs integrate physical law directly into the neural network architecture, enabling the accurate approximation of solutions to the SWE. We provide a comprehensive methodology for formulating the SWE within the PINNs framework, encompassing network architecture, training strategy, and data generation techniques. Through the results obtained from experiments, we found that PINNs could be an accurate output solution of SWE when its results were compared with the analytical method. In addition, PINNs also present better performance over the Artificial Neural Network. This study highlights the transformative potential of PINNs in revolutionizing water resources research, offering a new paradigm for accurate and efficient solutions to the SVE.
KIPS Transactions on Software and Data Engineering
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v.13
no.1
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pp.1-16
/
2024
Over recent years, 6 Sigma has become a key methodology in manufacturing for quality improvement and cost reduction. However, challenges have arisen due to the difficulty in analyzing large-scale data generated by smart factories and its traditional, formal application. To address these limitations, a big data-based 6 Sigma approach has been developed, integrating the strengths of 6 Sigma and big data analysis, including statistical verification, mathematical optimization, interpretability, and machine learning. Despite its potential, the practical impact of this big data-based 6 Sigma on manufacturing processes and management performance has not been adequately verified, leading to its limited reliability and underutilization in practice. This study investigates the efficiency impact of DX SS, a big data-based 6 Sigma, on manufacturing processes, and identifies key success policies for its effective introduction and implementation in enterprises. The study highlights the importance of involving all executives and employees and researching key success policies, as demonstrated by cases where methodology implementation failed due to incorrect policies. This research aims to assist manufacturing companies in achieving successful outcomes by actively adopting and utilizing the methodologies presented.
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