• Journal of Korean Elementary Science Education
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• v.34 no.4
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• pp.447-457
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• 2015

This study has attempted to find the teaching methods for the elementary students with low science achievement by examining the differences of science learning motivation, academic stress and stress coping styles and the characteristics shown in the relationship between them. To achieve this, the differences of science learning motivation, academic stress and stress coping styles of the elementary students with low science achievement and their relationship was set up as a study problem. A science learning motivation using a science learning motivation questionnaire reconfigured with PALS along with underachievers diagnosis were measured targeting 660 elementary students located in Seoul. Using an academic stress questionnaire and stress coping style questionnaire, an academic stress and stress coping styles were measured. The results of analyzing the collected data are as follows. First, a science learning motivation of elementary students with low science achievement was lower than the general students but the academic stress was shown higher. Especially, the self-efficacy of science learning motivation was significantly lower and the school stress was highest. For stress coping styles, a tendency of passive and avoidment coping styles were shown higher than the general students. Second, among the science learning motivation of elementary students with low science achievement, the self-efficacy motivation and school stress have shown a negative correlation but had a positive correlation with the goal-oriented motivation centered on ability. In the correlation between the science learning motivation of elementary students with low science achievement and the stress coping styles, the pursuit of social support coping styles have shown a significant positive correlation with the science learning motivation and its subcategories. As a result of conducting a regression analysis on the influence of academic stress and stress copying styles on the science learning motivation of elementary students with low science achievement, among the academic stresses, the school stress was shown to have the biggest influence. Among the stress coping styles, the pursuit of social support coping styles had the biggest influence on the science learning motivation followed by active coping behaviors, passive and avoidment coping behaviors. Low science learning motivation as underachievement factors of elementary students with low science achievement was identified as having a relationship with high school stress and undesirable stress copying styles. Therefore, guidance and a program are required for the elementary student with low science achievement to have desirable stress coping methods on the stressful situations. In addition, for the improvement of science learning motivation, a learning environment is needed for the elementary students with low science achievement with seeking of relevant educational methods.

• Journal of the Institute of Convergence Signal Processing
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• v.14 no.3
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• pp.147-153
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• 2013

Functional game, which is the combination of play and learning and a futuristic tool, can minimize the dysfunction and maximize the proper functions, and furthermore, has taken root as a new alternative that can change the game industry and game culture. Recently, the focus of game and education markets is shifting to the development of more advanced learning contents, rather than emphasizing the self-control and motivation of users. Along with that, the game market has excluded the socially dysfunctional elements, such as the addiction and learning disabilities, and has witnessed a diversification into the human-friendly entertainment business that emphasizes the mental and physical health and pursues scientific educational effects. In addition, functional games are expanding its reach from the professional sectors - such as medical aide/medical learning, military simulation, health, auxiliary tools, special education and learning tools - to the realm of routine education, mental health, etc., and has seen a steady growth. However, most functional games, which are being currently planned and developed to cope with the special characteristics of the market, have not undergone accurate scientific assessment of their functions and have not proven their effectiveness. An overwhelming proportion of the functional games are being developed based on the intuition and experience of game developers. Moreover, the type of games, which involve the repetition of simple tasks or take the form of simple puzzles, cannot effectively combine the practically interesting factors and the learning effects. Most games incorporate unscientific methods leading to the vague anticipation of improvement in functions, rather than the assessment of human functions. In this paper, a study was conducted to present the measures that could maximize the effects of functional games in relation to the changes in the visual and auditory stimulations in order to maximize the effects of functional games, i,e., the immersion and concentration. To compare the degree of effects arising from the visual stimulation, the functional game contents made in the form of 2D and 3D were utilized. In addition. ultra sound and 3-dimensional functional game contents were utilized to compare the degree of effects resulting from the changes in the auditory stimulation. The brainwave of the users were measured while conducting the experiments related to the response to the changes in visual and auditory stimulations in 3 steps, and the results of the analysis were compared.

• Journal of Yeungnam Medical Science
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• v.14 no.1
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• pp.123-136
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• 1997

The purpose of this study was to examine the reliability and validity of a Korean form of Childhood Attention Problem(CAP) scale. CAP were administered to 98 normal elementary school students as control group and 98 attention deficit hyperactivity disorder patients. Male students showed high scores than female students in both subscale and total scores, but not statistically significant. There were no significant difference in CAP scale between male students and female students in attention deficit hyperactivity disorder patients. In the reliability test, the test-retest reliability coefficient was highly satisfactory and that of inattention subscale was 0.83, impulsivity subscale was 0.70 and total score was 0.82. In the reliability test by internal consistency, the Cronbach $\alpha$ coefficient was highly satisfactory and that of inattention subscale was 0.91, overactivity subscale was 0.89(p<0.05). The concurrent validity between CAP scale and ADDES-BV scale was 0.85 in attention deficit hyperactivity disorder patient group and 0.73 in normal control group(p<0.05). In discriminant validity test between attention deficit hyperactivity disorder patient group and normal control group, the patient group showed higher score(p<0.05). The total discriminant capacity of the patient group in CAP was 93.4%. In this point of view, CAP scale showed high reliability and validity in applying to Korean subjects and was proved to be the good and simple screening test tool for attention deficit hyperactivity disorder research and can help many young patient to treat early.

• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.1-32
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• 2018

In addition to stakeholders including managers, employees, creditors, and investors of bankrupt companies, corporate defaults have a ripple effect on the local and national economy. Before the Asian financial crisis, the Korean government only analyzed SMEs and tried to improve the forecasting power of a default prediction model, rather than developing various corporate default models. As a result, even large corporations called 'chaebol enterprises' become bankrupt. Even after that, the analysis of past corporate defaults has been focused on specific variables, and when the government restructured immediately after the global financial crisis, they only focused on certain main variables such as 'debt ratio'. A multifaceted study of corporate default prediction models is essential to ensure diverse interests, to avoid situations like the 'Lehman Brothers Case' of the global financial crisis, to avoid total collapse in a single moment. The key variables used in corporate defaults vary over time. This is confirmed by Beaver (1967, 1968) and Altman's (1968) analysis that Deakins'(1972) study shows that the major factors affecting corporate failure have changed. In Grice's (2001) study, the importance of predictive variables was also found through Zmijewski's (1984) and Ohlson's (1980) models. However, the studies that have been carried out in the past use static models. Most of them do not consider the changes that occur in the course of time. Therefore, in order to construct consistent prediction models, it is necessary to compensate the time-dependent bias by means of a time series analysis algorithm reflecting dynamic change. Based on the global financial crisis, which has had a significant impact on Korea, this study is conducted using 10 years of annual corporate data from 2000 to 2009. Data are divided into training data, validation data, and test data respectively, and are divided into 7, 2, and 1 years respectively. In order to construct a consistent bankruptcy model in the flow of time change, we first train a time series deep learning algorithm model using the data before the financial crisis (2000~2006). The parameter tuning of the existing model and the deep learning time series algorithm is conducted with validation data including the financial crisis period (2007~2008). As a result, we construct a model that shows similar pattern to the results of the learning data and shows excellent prediction power. After that, each bankruptcy prediction model is restructured by integrating the learning data and validation data again (2000 ~ 2008), applying the optimal parameters as in the previous validation. Finally, each corporate default prediction model is evaluated and compared using test data (2009) based on the trained models over nine years. Then, the usefulness of the corporate default prediction model based on the deep learning time series algorithm is proved. In addition, by adding the Lasso regression analysis to the existing methods (multiple discriminant analysis, logit model) which select the variables, it is proved that the deep learning time series algorithm model based on the three bundles of variables is useful for robust corporate default prediction. The definition of bankruptcy used is the same as that of Lee (2015). Independent variables include financial information such as financial ratios used in previous studies. Multivariate discriminant analysis, logit model, and Lasso regression model are used to select the optimal variable group. The influence of the Multivariate discriminant analysis model proposed by Altman (1968), the Logit model proposed by Ohlson (1980), the non-time series machine learning algorithms, and the deep learning time series algorithms are compared. In the case of corporate data, there are limitations of 'nonlinear variables', 'multi-collinearity' of variables, and 'lack of data'. While the logit model is nonlinear, the Lasso regression model solves the multi-collinearity problem, and the deep learning time series algorithm using the variable data generation method complements the lack of data. Big Data Technology, a leading technology in the future, is moving from simple human analysis, to automated AI analysis, and finally towards future intertwined AI applications. Although the study of the corporate default prediction model using the time series algorithm is still in its early stages, deep learning algorithm is much faster than regression analysis at corporate default prediction modeling. Also, it is more effective on prediction power. Through the Fourth Industrial Revolution, the current government and other overseas governments are working hard to integrate the system in everyday life of their nation and society. Yet the field of deep learning time series research for the financial industry is still insufficient. This is an initial study on deep learning time series algorithm analysis of corporate defaults. Therefore it is hoped that it will be used as a comparative analysis data for non-specialists who start a study combining financial data and deep learning time series algorithm.

• Journal of Digital Convergence
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• v.14 no.1
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• pp.465-477
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• 2016

ADHD (Attention Deficit Hyperactivity Disorder) appears most in childhood and shows attention deficit, hyperactivity and impulsive activity. If these symptoms are left untreated, they consistently remain as difficulties throughout the childhood and in some cases they will last until adolescence and adulthood. Various researches on music therapy as well as psychological therapy and Korean classical music appreciation have currently been conducted. However, a variety of programs have introduced in the name of treatment but the improvement results through the application of programs and teaching methods, in reality, have hardly been a specific case. Thus, music educators as well as this researcher should make the efforts to form holistic characters and have interest in countless children with ADHD. Accordingly, this researcher, who has taught piano lessons, has performed a wide variety of convergence art teaching methods by applying methods of Dalcroze, Kodaly, and Orff to children with ADHD, misanthropy or lack of affection. As a result, symptoms have lessened and been treated for ADHD children with attention deficit. On the basis of this experience, there have appeared more educational effects by applying these to other children. This researcher is certain that this study will a foundation of music therapy education for children with each kind of syndrome.

• Journal of The Korean Association For Science Education
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• v.37 no.6
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• pp.921-935
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• 2017

The purpose of this research is to investigate trends of science educational researches on underrepresented students by scrutinizing Korean science education research literatures. For this particular purpose, literatures on underrepresented students were extracted from both listed and candidate journals for KCI and theses from 1984 to February 2017, and analyzed criteria such as source, year of publication, design, method, and content of research. A total of 125 papers from journals and 147 theses were extracted. In these researches, 61%, 20%, 6% were about students with disability, underachievers, and North Korean defector students respectively. The ratio of the researches on other underrepresented students such as multicultural, low income families, students who are from rural areas, and other underrepresented students were less than 5%. According to the year of publication, it was found that the number of research papers on underrepresented students increased continuously by a single digit from 1984 by focusing on the students with disability and underachievers. After that, from around 2008, it showed a rapid increase and researches on underrepresented students carried out more than 20 times annually. With regards to research design, there were 58% quantitative, 28% qualitative and 14% hybrid research design. Through analysis of research methods, we found that 30% of experimental research, 22% of interpretive research, 20% of correlation analysis, and 14% of survey research. After going through the characteristics of the research contents by visualizing the relationship between the research groups and the keywords that were extracted, it was found that even though the science education researches on underrepresented students have various contents, there were no keywords that were researched continuously and intensively in this area. The structural relationship between the keywords and each research group on underrepresented students showed that 'academic achievement' is the keyword with the highest degree of mediateness and connectedness.