• Journal of Gifted/Talented Education
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• v.19 no.3
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• pp.457-476
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• 2009

The research aimed to investigate characteristics of middle school students in a biology class as science gifted education in terms of self-regulated learning abilities, personality traits and learning preferences. The twenty subject in the study responded to questionnaires of a self-regulated learning ability instrument, a personality trait tool, and a learning preference survey in March, 2009. It was found that the research subjects showed higher levels of cognitive strategies, meta-cognition, and motivation than those students in a previous study(Jung et. al., 2004), while environment was opposite. The level of cognitive strategies was significantly correlated with meta-cognition(r=.610, p=.004) and motivation (r=.538, p=.014) and meta-cognition with environment(r=.717, p=.000). Those students who showed highest levels of self-regulated learning ability displayed various personality traits. One male student with the highest level of self-regulated learning ability showed a personality of hardworking, tender-minded, and conscientious traits and wanted to be a medical doctor. The female student with the second highest level of self-regulated learning ability presented a personality as creative, abstract and divergent thinker and she showed a strong aspiration to be a world-famous biologist with breakthrough contribution. The five students with highest levels of self-regulated learning ability showed a common preference in science learning: they dislike memory-oriented and theory-centered lecture with note-taking from teacher's writings on chalkboard; they prefer science learning with inquiry-oriented laboratory work, discussion among students as well as teachers. However, reasons to prefer discussion were diverse as one student wants to listen other students' opinions while the other student want to present his opinion to other students. The most favorable science teachers appeared to be who ask questions frequently, increase student interests, behave friendly with students, and is a active person. In conclusion, science teaching for the gifted should employ individualized teaching strategies appropriate for individual personality and preferred learning styles as well as meeting with individual interests in science themes.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.135-149
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• 2020

Artificial intelligences are changing world. Financial market is also not an exception. Robo-Advisor is actively being developed, making up the weakness of traditional asset allocation methods and replacing the parts that are difficult for the traditional methods. It makes automated investment decisions with artificial intelligence algorithms and is used with various asset allocation models such as mean-variance model, Black-Litterman model and risk parity model. Risk parity model is a typical risk-based asset allocation model which is focused on the volatility of assets. It avoids investment risk structurally. So it has stability in the management of large size fund and it has been widely used in financial field. XGBoost model is a parallel tree-boosting method. It is an optimized gradient boosting model designed to be highly efficient and flexible. It not only makes billions of examples in limited memory environments but is also very fast to learn compared to traditional boosting methods. It is frequently used in various fields of data analysis and has a lot of advantages. So in this study, we propose a new asset allocation model that combines risk parity model and XGBoost machine learning model. This model uses XGBoost to predict the risk of assets and applies the predictive risk to the process of covariance estimation. There are estimated errors between the estimation period and the actual investment period because the optimized asset allocation model estimates the proportion of investments based on historical data. these estimated errors adversely affect the optimized portfolio performance. This study aims to improve the stability and portfolio performance of the model by predicting the volatility of the next investment period and reducing estimated errors of optimized asset allocation model. As a result, it narrows the gap between theory and practice and proposes a more advanced asset allocation model. In this study, we used the Korean stock market price data for a total of 17 years from 2003 to 2019 for the empirical test of the suggested model. The data sets are specifically composed of energy, finance, IT, industrial, material, telecommunication, utility, consumer, health care and staple sectors. We accumulated the value of prediction using moving-window method by 1,000 in-sample and 20 out-of-sample, so we produced a total of 154 rebalancing back-testing results. We analyzed portfolio performance in terms of cumulative rate of return and got a lot of sample data because of long period results. Comparing with traditional risk parity model, this experiment recorded improvements in both cumulative yield and reduction of estimated errors. The total cumulative return is 45.748%, about 5% higher than that of risk parity model and also the estimated errors are reduced in 9 out of 10 industry sectors. The reduction of estimated errors increases stability of the model and makes it easy to apply in practical investment. The results of the experiment showed improvement of portfolio performance by reducing the estimated errors of the optimized asset allocation model. Many financial models and asset allocation models are limited in practical investment because of the most fundamental question of whether the past characteristics of assets will continue into the future in the changing financial market. However, this study not only takes advantage of traditional asset allocation models, but also supplements the limitations of traditional methods and increases stability by predicting the risks of assets with the latest algorithm. There are various studies on parametric estimation methods to reduce the estimated errors in the portfolio optimization. We also suggested a new method to reduce estimated errors in optimized asset allocation model using machine learning. So this study is meaningful in that it proposes an advanced artificial intelligence asset allocation model for the fast-developing financial markets.

• (The) Research of the performance art and culture
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• no.42
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• pp.47-75
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• 2021

This thesis looks into the interpretation of the Korean War and mystery genre in Korea in the 1970s by analyzing the special drama , in which the theme was directly related to the Korean War, airing through MBC in 1979. It begins by finding the change in direction in the 1970s when the world of TV was dictated through the heavy censorship and the memory of the war by the government. It also looks at the intentions of the producer who was taking in the new way and the viewers who also accepted this drama and its reflections. In order to gain some insights into these issues, it compares between the drama "The Last Witness" and the original novel by Seong-jong Kim who holds the same time to see the way in which this is dramatized. The drama, "The Last Witness", was produced with a plan to generate a high-quality special drama which combined both artistry and sense of purpose. Nevertheless, as watching TV became a leisurely past-time during this period, TV dramas become more aggressive and suggestive in order to attract viewers. This ultimately was encored with obstacles due to the regime and the heavy censorship at the time. The genre of special drama that is well known in South Korea, is designed as an art form to satisfy both their unique artistry and its purpose. The conflict is seen between the key elements of the artistic drama crated by the producers and the 'encouraged' elements that often are needed to engage the viewers. Thus, more often than not, special dramas defeat the original intention of national harmony, encouraged by the regime. This is due to the 'novelty' aspect which grows from the effort of bringing enjoyment to viewers whilst also trying to achieve the artistic drama to life. Alongside this, crime element in this drama is designed in a way that visually embodies the process of deduction, becoming a new possibility to secure the reality of the times. However, it was also a paradoxical existence since it was indicated as an example of unrefined culture that lost its original intention. In that way, it is worth to think that detective suspense stories, which were not popular in Korea, influenced viewers as a tv drama series in the 1970s through the various elements that compose the genre. They went through a process of transplantation and acceptance whilst also attempting to satisfy the viewers and their encouraged elements to engage them. As is well known, crime drama in Korea has its own style by mixing anticommunism and detective reasoning. This combination is found in the way in which the genre naturally forms through the elements selected and excluded in the dramatization of "The Last Witness". The point is that the special drama "The Last Witness" can be seen as an intermediate form that shows the tendency of transformation from the detective reasoning form alongside the crime aspects as TV dramas began to include anticommunism messaging and investigation in the 1970s. In conclusion, when the detective reasoning is used as an element in a TV drama, it shows the trust of the public system and it constantly seeks the possibility of circumventing the political interpretation. The memories of the war is seen as a tool that neutralizes the dismal imaginations inscribed on the dark side of society and the system. As a result, "The Last Witness", broadcasted at the end of the Yushin regime in Korea, is a strange result which combines the logic of a special drama and the encouraged characteristics of television dramas. The viewers' desire which is the discussion about the hidden traces from the texts needs to be restored again.