• Communications of Mathematical Education
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• v.8
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• pp.247-255
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• 1999

대부분의 수학 교사들은 국내외에서 개최되는 많은 학술 행사에 참여하기를 꺼려하고 있으며 수학 교육의 새로운 정보에 접촉하려는 의지가 부족한 실정이다. 이 때문에 세계의 수학 교육의 흐름이 어떤지, 우리 나라의 수학 교육과정이나 교수 ${\cdot}$ 학습법이 외국의 것과는 어떻게 다른지 또는 수준에 차이가 있다면 얼마나 차이가 있는지 별 관심을 갖지 않고 있으며 구태여 많은 노력을 들여 이러한 것을 알려고 하지도 않는다. 필자의 판단으로는 우리 나라의 수학 교육이 당면하고 있는 가장 큰 문제는 수학 교사들은 많으나 우수한 자질을 가진 수학 교사들이 많지 않기 때문에 창의성 교육이 제대로 이루어지지 않는 것과 학교 수학 교육에서 능력별 반 편성이 무엇보다도 필요한 줄 알면서도 수십년 동안 제대로 실행되지 않아 학생들의 수준에 맞도록 효율적으로 수학을 지도할 수 없는 것이라 생각한다. 이 두 문제는 모두 몇몇 수학 교사들의 의지와 노력만으로는 해결할 수 없는 문제들이다. 그러나 많은 교사들이 모여 이러한 문제점들을 공동으로 인식하고 함께 해결하기를 노력한다면 시일이 좀 걸리더라도 언젠가는 해결되리라고 믿는다. 장기적으로 수학 교사의 자질을 향상시키기 위해서는 교사 양성 기관(사범대학과 교육대학교)의 개선이 필요하며, 능력별 반 편성은 교육정책자들이나 교육행정가들이 마음만 먹으면 1${\sim}$2년내에 이룰 수 있다. 이제 전국수학교육연구대회와 같은 행사는 단순한 수학교육이론의 전달이나 현장연구에서 발견한 새로운 사실들만은 발표하는 곳이 아니라, 될 수 있는 데로 많은 수학 교육자들이 모여 수학 교육의 문제점을 찾고, 함께 풀어 나가기 위한 토론의 장(場)이 되어야한다. 또 필요에 따라서는 수학 교육에 관련한 어떤 결의도 하고 교육부 또는 각 교육청이나 교육연구기관에 보내는 건의문도 만들어야 할 것이다. 어떻든 이와 같이 전국 수학교육자들이 모일 때는 꼭 참여하여 우리의 문제를 적극적으로 해결하도록 힘을 합치는 것이 수학교육자의 올바른 태도라고 생각한다.

• Journal of Convergence for Information Technology
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• v.10 no.4
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• pp.73-80
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• 2020

The purpose of this study is to find out the usefulness of color contrast program for 50 high school students through comparing the traditional class and the class using the entry software : understanding, utilization, satisfaction, and interest. The results of the study were as follows: First, in the case of the class results, there were no limitations on colors due to materials in the class using the entry software, so students used various colors that match the theory. Second, the activity class using the entry software showed a positive response in terms of utilization, satisfaction, and interest except for the comprehension area, compared to the traditional class using the paper activity. Therefore, this study proved the effectiveness of the practice activity using the entry software in the color management class.

• Journal of The Korean Association of Information Education
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• v.14 no.1
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• pp.69-77
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• 2010

As the knowledge and information society has emerged, paradigm of school education has moved to improvement of creativity and problem-solving skills of a learner. However, up to the present, ICT education in schools has shown distorted features, which are focusing on the use of application programs, out of its original purpose. This study also allows digital technology to be displayed in analog ways by applying the paper prototyping technique, which can be substituted for the programming phase that elementary school students feel difficulty to handle with. In addition, It enables practical and useful ideas to be designed, constructed and evaluated as a system, based on User Centered Design (UCD), which encourages users actively to participate in the development, rather than focus on developer. To verify the effects of education, we evaluated and analyzed concept models of a learner before and after the application development activity by applying the mental model theory. The framework suggested in this study can be applied to courseware of programming in elementary schools.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.1
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• pp.1-7
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• 2021

Investor mood from social media is gaining increasing attention for leading a price movement in stock market. Based on the behavioral finance theory, this study argues that sentiment extracted from social media using big data technique can predict a real-time (short-run) price momentum in Chinese stock market. Collecting Sina Weibo posts that related to COVID-19 using keyword method, a daily influential weighted sentiment factors is extracted from the sizable raw data of over 2 millions of posts. We examine one supervised and 4 unsupervised sentiment analysis model, and use the best performed word-frequency and BiLSTM mdoel. The test result shows a similar movement between stock price change and sentiment factor. It indicates that public mood extracted from social media can in some extent represent the investors' sentiment and make a difference in stock market fluctuation when people are concentrating on a special events that can cause effect on the stock market.

• Journal of the Society of Disaster Information
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• v.18 no.3
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• pp.495-506
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• 2022

Purpose: When a serious disaster occurred in East Japan on March 11, 2011, some evacuees in shock failed to avoid danger to the best of their ability. Why did they hesitate and waste their time? And why didn't they choose correct escaping routes? This study attempts to classify human behavior through psychological point of view and cognitive science and to interpret behavioral patterns based on animal behaviors from the field of biology. Method: This study first conceptually categorized walking behavior into intellectualization, automaticity and instinct based on the existing literature and matched these with empirical data. Result: The actual walking patterns observed failed to be compatible with these categories and consequently, this study suggests the following five categories: normal, busy, fast & straight, freezing and tizzy. This new classification of walking behavior is based on speed, variation of speed and change of direction. Conclusion: The method used in this study and the results can be applied to simulations of walking behavior and analysis of behavior in emergency situations.

• The Journal of the Convergence on Culture Technology
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• v.9 no.1
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• pp.649-654
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• 2023

This paper proposes the design of a neural network structure search model using graph convolutional neural networks. Deep learning has a problem of not being able to verify whether the designed model has a structure with optimized performance due to the nature of learning as a black box. The neural network structure search model is composed of a recurrent neural network that creates a model and a convolutional neural network that is the generated network. Conventional neural network structure search models use recurrent neural networks, but in this paper, we propose GC-NAS, which uses graph convolutional neural networks instead of recurrent neural networks to create convolutional neural network models. The proposed GC-NAS uses the Layer Extraction Block to explore depth, and the Hyper Parameter Prediction Block to explore spatial and temporal information (hyper parameters) based on depth information in parallel. Therefore, since the depth information is reflected, the search area is wider, and the purpose of the search area of the model is clear by conducting a parallel search with depth information, so it is judged to be superior in theoretical structure compared to GC-NAS. GC-NAS is expected to solve the problem of the high-dimensional time axis and the range of spatial search of recurrent neural networks in the existing neural network structure search model through the graph convolutional neural network block and graph generation algorithm. In addition, we hope that the GC-NAS proposed in this paper will serve as an opportunity for active research on the application of graph convolutional neural networks to neural network structure search.

• Journal of Korean Society of Archives and Records Management
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• v.1 no.1
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• pp.37-52
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• 2001

1. establishment and development of China archives science: With the centuries-old history of archives and archives management, early China archives science came into being in 1930s, and the research pushed forward by archives enterprise has made great achievements since then. 1.1 Expanding research fields: Foundation

• 대한공업교육학회지
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• v.43 no.1
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• pp.134-157
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• 2018

This study surveyed and analyzed satisfaction and issues about an intellectual property education system, and educational needs for intellectual property NCS (intellectual property management, intellectual property information survey analysis, intellectual property assessment trade), in order to introduce and operate NCS-based education system that trains practical manpower in the field. The results of this study are as follows. First, satisfaction for intellectual property education system showed education contents (M=3.86), followed by lecture (M=3.79), teaching method and environment (M=3.66) and education assessment (M=3.50). The issues to be improved are low application in the current occupation due to no reflection of demands of industrial fields, as well as insufficient education contents system, lectures who fall short of education capability and interactions with students tend to stress theoretical knowledge more than practical ability, teaching method lacks application of educational medium, insufficient interest and motivation, assessment methods that fall short of theoretical knowledge and practical ability achievement, and that is theory-centered. Second, educational needs for intellectual property NCS showed intellectual property assessment trade (4.33), followed by intellectual property management (3.68), and intellectual property information survey analysis (2.99), which should be reviewed to reform or newly develop NCS-based education course. Conclusively, intellectual property education showed satisfaction above the average, but a job-centered education is demanded to elevate application in the industrial field, which puts emphasis more on practical ability than theory. For this, it is necessary to introduce intellectual property NCS reflecting demands of industrial field, and to reform or newly develop into NCS-based education course. In addition, intellectual property education needs to be operated by changed education paradigm, such as user-centered teaching method, not provided-centered, and performance and course-centered assessment method, not theoretical knowledge-centered.

• Korean Journal of Social Welfare
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• v.59 no.2
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• pp.5-38
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• 2007

This study attempts to develop a theory on the evolving process of wealthy major donors' sharing lives in Korea through a grounded theory approach. To conduct this study, the researchers have in-depth interviews with 11 exemplary wealthy major donors who have more than one million US dollars in his or her own asset and donate more than ten thousand US dollars annually. In data analysis, this study identifies 161 concepts on the evolving process of wealthy major donors' sharing lives; and the concepts are categorized with 33 sub-categories and 14 categories. In the paradigm model on the evolving process of wealthy major donors' sharing lives, it is identified that the central phenomenon, 'practicing sharing lives as noblesse oblige', is related with the causal conditions such as 'learning through memories and observation', 'realizing my duties', and 'emphasizing'; and the central phenomenon is related with the contingent conditions such as 'being sensitive to external evaluation', 'having limited information on giving', 'distrusting donation related environments'. The action/interactional sequences such as 'utilizing relationships' and 'strengthening active participation' are accomplished by moderating conditions such as 'having internal and external supports' and 'guiding by firm conviction'. It reveals that as a result, wealthy major donors enjoy the feeling of becoming a ideal and true wealthy person, establish sharing lives as firm and major parts of overall lives, and experience strong desires for better future and society. In this study, 'generous sharing that shares personal heritages and social benefits' is analyzed as a core category; it shows that sharing of wealthy major donors is related to the characteristics of generosity practice based on moral self-benefiting rather than complete altruistic characteristics or self-sacrificial characteristics. The process analysis reveals that it has the following stages: first, initial giving by exposure to causes or requests; second, routine practice of giving; third, evolution of practice of giving with gradual expansion in quantities and qualities; and fourth, living with giving. In the process, the following four types are identified: devoted wealthy donors for sharing, wealthy donors practicing sharing in daily life, wealthy donors practicing sharing with learning on external stimulus, and wealthy donors practicing sharing on empathy. Finally, this study discusses both meanings of identifying and developing a theory on the evolving process of wealthy major donors' sharing lives and implications of the research results in cultivating and developing potential wealthy major donors in Korea.

• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.1-17
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• 2019

Because stock price forecasting is an important issue both academically and practically, research in stock price prediction has been actively conducted. The stock price forecasting research is classified into using structured data and using unstructured data. With structured data such as historical stock price and financial statements, past studies usually used technical analysis approach and fundamental analysis. In the big data era, the amount of information has rapidly increased, and the artificial intelligence methodology that can find meaning by quantifying string information, which is an unstructured data that takes up a large amount of information, has developed rapidly. With these developments, many attempts with unstructured data are being made to predict stock prices through online news by applying text mining to stock price forecasts. The stock price prediction methodology adopted in many papers is to forecast stock prices with the news of the target companies to be forecasted. However, according to previous research, not only news of a target company affects its stock price, but news of companies that are related to the company can also affect the stock price. However, finding a highly relevant company is not easy because of the market-wide impact and random signs. Thus, existing studies have found highly relevant companies based primarily on pre-determined international industry classification standards. However, according to recent research, global industry classification standard has different homogeneity within the sectors, and it leads to a limitation that forecasting stock prices by taking them all together without considering only relevant companies can adversely affect predictive performance. To overcome the limitation, we first used random matrix theory with text mining for stock prediction. Wherever the dimension of data is large, the classical limit theorems are no longer suitable, because the statistical efficiency will be reduced. Therefore, a simple correlation analysis in the financial market does not mean the true correlation. To solve the issue, we adopt random matrix theory, which is mainly used in econophysics, to remove market-wide effects and random signals and find a true correlation between companies. With the true correlation, we perform cluster analysis to find relevant companies. Also, based on the clustering analysis, we used multiple kernel learning algorithm, which is an ensemble of support vector machine to incorporate the effects of the target firm and its relevant firms simultaneously. Each kernel was assigned to predict stock prices with features of financial news of the target firm and its relevant firms. The results of this study are as follows. The results of this paper are as follows. (1) Following the existing research flow, we confirmed that it is an effective way to forecast stock prices using news from relevant companies. (2) When looking for a relevant company, looking for it in the wrong way can lower AI prediction performance. (3) The proposed approach with random matrix theory shows better performance than previous studies if cluster analysis is performed based on the true correlation by removing market-wide effects and random signals. The contribution of this study is as follows. First, this study shows that random matrix theory, which is used mainly in economic physics, can be combined with artificial intelligence to produce good methodologies. This suggests that it is important not only to develop AI algorithms but also to adopt physics theory. This extends the existing research that presented the methodology by integrating artificial intelligence with complex system theory through transfer entropy. Second, this study stressed that finding the right companies in the stock market is an important issue. This suggests that it is not only important to study artificial intelligence algorithms, but how to theoretically adjust the input values. Third, we confirmed that firms classified as Global Industrial Classification Standard (GICS) might have low relevance and suggested it is necessary to theoretically define the relevance rather than simply finding it in the GICS.