• Journal of The Korean Association For Science Education
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• v.38 no.6
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• pp.765-780
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• 2018

The purpose of this study is to synthesize the theoretical perspectives, research methods, and research results of teachers' professional vision by reviewing and analyzing previous research papers and to suggest implications for science teacher education and research. Three databases were used to search peer reviewed journal articles published between 1997-2017, which include 'teachers' and 'professional vision' explicitly in abstracts and empirical studies only. 21 articles in total were analyzed and review results are as follows. First, researchers regarded professional vision as a new concept of teacher professionalism. Previous research viewed professional vision as integrated structure of teachers' knowledge or ability activated at specific moment. Second, the analytical framework of professional vision included two aspects; 'selective attention' and 'reasoning'. Several aspects of lessons or the desirable teaching and learning factors are suggested as the subcategories of selective attention. Hierarchical levels or independent reasoning ability factors are suggested as the subcategories of reasoning process. Third, research on teachers' professional vision focused more on middle school teachers than elementary teachers and on various subject areas. Most studies used video clips and more cases of using videos of non-participants were found. In case of measurement of professional vision, most quantitative scoring methods were whether the responses of experts and teachers on video clips were consistent. Last, most studies examined or assessed teachers' professional vision. It is reported that in-service teachers' professional vision was evaluated higher than novice teachers' and using video clips were effective to examine and improve teachers' professional vision.

• 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.

• Journal of Korean Home Economics Education Association
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• v.33 no.3
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• pp.85-105
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• 2021

The purpose of this study is to develop and implement cooperative consumption education programs using design thinking in middle school home economics education classes to understand the impact on students' cooperative problem solving competency. Accordingly, a cooperative consumption education program based on design thinking was developed according to the ADDIE model, and the evaluation was conducted on a total of 25 students. The results of the study were as follows. First, based on prior research, we developed a consumption education program based on D. school's design thinking process under the theme of 'Creating a Shared School' for the practice of cooperative consumption. As a result of expert validity verification of the teaching/learning course plan and workbook for the eight sessions, the average question was 4.72 (out of 5 points) and the average CVI was 0.93, indicating that the content validity and field suitability were excellent. Second, to summarize the results achieved from the implementation of the cooperative consumption education program, the pre-/post-test using the revised and supplemented cooperative problem-solving competency tool, and the open-ended survey, It was confirmed that the developed program had a significant effect on improving not only the students' knowledge and perceived necessity for cooperative consumption along with the awareness of practice, but also the cooperative problem-solving competency. As a follow-up study, we propose to expand the research to a wider audience, and to further conduct research and develop programs applied with design thinking in home economics curriculum and in consumer competency development. This study confirmed that cooperative consumption education programs using design thinking are effective in improving youth's cooperative problem-solving competency and is meaningful in that they developed consumption education programs under the theme of 'cooperative consumption' in response to changing consumer education needs.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.66 no.2
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• pp.138-145
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• 2021

Perilla contains more than 60% of fatty acids. Linolenic acid is effective in preventing heart disease, improving learning ability, treating allergies, and preventing cancer. This study was carried out to improve the cultivation method to aid the stable production of perilla by developing a suitable inter-cropping system with sesame in the central region as well as to report a suitable planting time. The test results are summarized as follows. As the planting time of perilla in the inter-cropping system with sesame was delayed, the number of clusters and capsules decreased. The perilla yields in this system showed significant differences compared to that with the previous crops (sesame varieties) and planting period. The yield of perilla was significantly lower in the characteristic-Type B variety than in the characteristic-Type A variety and decreased significantly as the planting time was delayed. With regards to the quality characteristics of perilla, such as crude protein, crude fat, etc., there were no differences between previous perilla crops and those inter-cropped with sesame. The perilla composition did not show any difference during the planting period; however, with delay in the planting time, crude protein content increased but crude fat content decreased. Yield of perilla was 38% higher in a two-row (40 x 40 cm) system, compared to a single-row cultivation (110 x 20 cm) of perilla inter-cropped with sesame. These results suggest that the suitable method for inter-cropping perilla with sesame in the central region is to sow the characteristic-Type A variety in early May, and cultivate the perilla in two lines (40 x 40 cm) in mid-June. This was judged to be the best cultivation method in the central region.

• (The) Research of the performance art and culture
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• no.32
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• pp.595-628
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• 2016

Pansori of a creative group pansori 2006 demonstration factory floor sound brutal sorigeuk the home of is a legend 'deokttaegol' in pansori, a creative for adaptation to remakes Work is. Evil Twin 'deokttaegol' called "Give me my leg back" in of Ghost Stories, broadcast on a kbs of lines from breakneck work is considered to be a pronoun. Sound and shadow play and playing drums and payments sentiments of the cruelty I've come across in this 'Give me my leg back' audience to be deployed to the cruel is formed by the center. Based on emotional horror of cruelty. When I was little, ever heard of Korean Ghost Stories, a bedrock of the main feeling revulsion of value in a short time and is contained in a story of filial piety, while in education, to the target Provided. Done in our lives using genre called 'pansori' sentiment and efficient learning can move about the value education can know. Sound and stories, many carefree a stimulus such as Pansori is a great gesture can be a means of education. Valued with any information, work is performed in pansori, depending upon efficient and the various, education and made an emotional cultivation resulting from the value. In my life friendly, our own via a variety of materials that can easily access many values and sentiments, and to culture for each age group on languages and customs Each age groups and instructive preferred allowing them access through their rhythm, pansori, access to the target is persistent about it with curiosity and interest. Can have interest. This wealth not belong to the traditional pansori and new together private and to the tune called creative work for the Pansori. Therefore, our language and customs, their poems span a friendly, the pansori and created using the vocabulary for each age group creative content is educational effects if used in education It is expected to be big thing. These effective approach for each age group and based on the vocabulary by the content easily understood lessons by causing only a smoothly acquired Can to provide an opportunity. Therefore, the Pansori of a creative education is important to take advantage of educational value.

• Journal of Science Education
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• v.45 no.3
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• pp.292-303
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• 2021

This study identifies the terms frequently used together with energy in online science news articles and topics of the news reports to find out how the term energy is used in everyday life and to draw implications for science curriculum and instruction about energy. A total of 2,171 online news articles in science category published by 11 major newspaper companies in Korea for one year from March 1, 2018 were selected by using energy as a search term. As a result of natural language processing, a total of 51,224 sentences consisting of 507,901 words were compiled for analysis. Using the R program, term frequency analysis, semantic network analysis, and structural topic modeling were performed. The results show that the terms with exceptionally high frequencies were technology, research, and development, which reflected the characteristics of news articles that report new findings. On the other hand, terms used more than once per two articles were industry-related terms (industry, product, system, production, market) and terms that were sufficiently expected as energy-related terms such as 'electricity' and 'environment.' Meanwhile, 'sun', 'heat', 'temperature', and 'power generation', which are frequently used in energy-related science classes, also appeared as terms belonging to the highest frequency. From a network analysis, two clusters were found including terms related to industry and technology and terms related to basic science and research. From the analysis of terms paired with energy, it was also found that terms related to the use of energy such as 'energy efficiency,' 'energy saving,' and 'energy consumption' were the most frequently used. Out of 16 topics found, four contexts of energy were drawn including 'high-tech industry,' 'industry,' 'basic science,' and 'environment and health.' The results suggest that the introduction of the concept of energy degradation as a starting point for energy classes can be effective. It also shows the need to introduce high-tech industries or the context of environment and health into energy learning.

• Research in Plant Disease
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• v.28 no.3
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• pp.113-121
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• 2022

Rice blast is a major plant disease that occurs worldwide and significantly reduces rice yields. Rice blast disease occurs periodically in Korea, causing significant socio-economic damage due to the unique status of rice as a major staple crop. A disease outbreak prediction system is required for preventing rice blast disease. Epidemiological investigations of disease outbreaks can aid in decision-making for plant disease management. Currently, plant disease prediction and epidemiological investigations are mainly based on quantitatively measurable, structured data such as crop growth and damage, weather, and other environmental factors. On the other hand, text data related to the occurrence of plant diseases are accumulated along with the structured data. However, epidemiological investigations using these unstructured data have not been conducted. The useful information extracted using unstructured data can be used for more effective plant disease management. This study analyzed news articles related to the rice blast disease through text mining to investigate the years and provinces where rice blast disease occurred most in Korea. Moreover, the average temperature, total precipitation, sunshine hours, and supplied rice varieties in the regions were also analyzed. Through these data, it was estimated that the primary causes of the nationwide outbreak in 2020 and the major outbreak in Jeonbuk region in 2021 were meteorological factors. These results obtained through text mining can be combined with deep learning technology to be used as a tool to investigate the epidemiology of rice blast disease in the future.

• Journal of Intelligence and Information Systems
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• v.28 no.1
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• pp.329-352
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• 2022

It is reported that particulate matter(PM) penetrates the lungs and blood vessels and causes various heart diseases and respiratory diseases such as lung cancer. The subway is a means of transportation used by an average of 10 million people a day, and although it is important to create a clean and comfortable environment, the level of particulate matter pollution is shown to be high. It is because the subways run through an underground tunnel and the particulate matter trapped in the tunnel moves to the underground station due to the train wind. The Ministry of Environment and the Seoul Metropolitan Government are making various efforts to reduce PM concentration by establishing measures to improve air quality at underground stations. The smart air quality management system is a system that manages air quality in advance by collecting air quality data, analyzing and predicting the PM concentration. The prediction model of the PM concentration is an important component of this system. Various studies on time series data prediction are being conducted, but in relation to the PM prediction in subway stations, it is limited to statistical or recurrent neural network-based deep learning model researches. Therefore, in this study, we propose four transformer-based models including spatiotemporal transformers. As a result of performing PM concentration prediction experiments in the waiting rooms of subway stations in Seoul, it was confirmed that the performance of the transformer-based models was superior to that of the existing ARIMA, LSTM, and Seq2Seq models. Among the transformer-based models, the performance of the spatiotemporal transformers was the best. The smart air quality management system operated through data-based prediction becomes more effective and energy efficient as the accuracy of PM prediction improves. The results of this study are expected to contribute to the efficient operation of the smart air quality management system.

• Asia Marketing Journal
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• v.7 no.3
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• pp.53-76
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• 2005

With rapid technological and market change, new product development (NPD) complexity is a significant issue that organizations continually face in their development projects. There are numerous factors, which cause development projects to become increasingly costly & complex. A product is more likely to be successfully developed and marketed when the complexity inherent in NPD projects is clearly understood and carefully managed. Based upon the previous studies, this study examines the nature and importance of complexity in developing new products and then identifies several issues in managing complexity. Issues considered include: definition of complexity : consequences of complexity; and methods for managing complexity in NPD projects. To achieve high performance in managing complexity in development projects, these issues need to be addressed, for example: A. Complexity inherent in NPD projects is multi-faceted and multidimensional. What factors need to be considered in defining and/or measuring complexity in a development project? For example, is it sufficient if complexity is defined only from a technological perspective, or is it more desirable to consider the entire array of complexity sources which NPD teams with different functions (e.g., marketing, R&D, manufacturing, etc.) face in the development process? Moreover, is it sufficient if complexity is measured only once during a development project, or is it more effective and useful to trace complexity changes over the entire development life cycle? B. Complexity inherent in a project can have negative as well as positive influences on NPD performance. Thus, which complexity impacts are usually considered negative and which are positive? Project complexity also can affect the entire organization. Any complexity could be better assessed in broader and longer perspective. What are some ways in which the long-term impact of complexity on an organization can be assessed and managed? C. Based upon previous studies, several approaches for managing complexity are derived. What are the weaknesses & strengths of each approach? Is there a desirable hierarchy or order among these approaches when more than one approach is used? Are there differences in the outcomes according to industry and product types (incremental or radical)? Answers to these and other questions can help organizations effectively manage the complexity inherent in most development projects. Complexity is worthy of additional attention from researchers and practitioners alike. Large-scale empirical investigations, jointly conducted by researchers and practitioners, will help gain useful insights into understanding and managing complexity. Those organizations that can accurately identify, assess, and manage the complexity inherent in projects are likely to gain important competitive advantages.

• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.4
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• pp.427-435
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• 2023

Changes in flowering time due to weather fluctuations impact plant growth and ecosystem dynamics. Accurate prediction of flowering timing is crucial for effective forest ecosystem management. This study uses a process-based model to predict flowering timing in 2023 for five major tree species in Korean forests. Models are developed based on nine years (2009-2017) of flowering data for Abeliophyllum distichum, Robinia pseudoacacia, Rhododendron schlippenbachii, Rhododendron yedoense f. poukhanense, and Sorbus commixta, distributed across 28 regions in the country, including mountains. Weather data from the Automatic Mountain Meteorology Observation System (AMOS) and the Korea Meteorological Administration (KMA) are utilized as inputs for the models. The Single Triangle Degree Days (STDD) and Growing Degree Days (GDD) models, known for their superior performance, are employed to predict flowering dates. Daily temperature readings at a 1 km spatial resolution are obtained by merging AMOS and KMA data. To improve prediction accuracy nationwide, random forest machine learning is used to generate region-specific correction coefficients. Applying these coefficients results in minimal prediction errors, particularly for Abeliophyllum distichum, Robinia pseudoacacia, and Rhododendron schlippenbachii, with root mean square errors (RMSEs) of 1.2, 0.6, and 1.2 days, respectively. Model performance is evaluated using ten random sampling tests per species, selecting the model with the highest R². The models with applied correction coefficients achieve R² values ranging from 0.07 to 0.7, except for Sorbus commixta, and exhibit a final explanatory power of 0.75-0.9. This study provides valuable insights into seasonal changes in plant phenology, aiding in identifying honey harvesting seasons affected by abnormal weather conditions, such as those of Robinia pseudoacacia. Detailed information on flowering timing for various plant species and regions enhances understanding of the climate-plant phenology relationship.