• Korean Journal of Comparative Education
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• v.24 no.3
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• pp.47-69
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• 2014

At-risk students are increasing because of developmental trait of adolescence, dissolution of family, poverty, and study stress. These risk situations influence individual student and society negatively. The purpose of this study is explore the reason of occurrence of at-risk student and draw out improvement direction of Gyeongsangbuk-do at-risk student support policy based on current situation of at-risk control and support system implementing in Gyeongsangbuk-do. The improvement ways based on the analysis of Gyeongsangbuk-do's at-risk student support strategy are as the followings. First, it is the construction of at-risk student's prevention and early discovery system. Second, it is the construction of educational safety network for guiding discovered at-risk student, Third, it is the connection with family and community.

• Environmental and Resource Economics Review
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• v.16 no.4
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• pp.947-978
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• 2007

In this paper, we investigated a Value-at-Risk approach to the volatility of two crude oil markets (Brent and Dubai). We also assessed the performance of various VaR models (RiskMetrics, GARCH, IGARCH and FIGARCH models) with the normal and skewed Student-t distribution innovations. The FIGARCH model outperforms the GARCH and IGARCH models in capturing the long memory property in the volatility of crude oil markets returns. This implies that the long memory property is prevalent in the volatility of crude oil returns. In addition, from the results of VaR analysis, the FIGARCH model with the skewed Student-t distribution innovation predicts critical loss more accurately than other models with the normal distribution innovation for both long and short positions. This finding indicates that the skewed Student-t distribution innovation is better for modeling the skewness and excess kurtosis in the distribution of crude oil returns. Overall, these findings might improve the measurement of the dynamics of crude oil prices and provide an accurate estimation of VaR for buyers and sellers in crude oil markets.

• Korean journal of food and cookery science
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• v.22 no.3 s.93
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• pp.355-362
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• 2006

The purpose of this study was to investigate the influences of tangible clues on university students' perceived risks and satisfaction at family-restaurants in Seoul. The performances of 3 tangible clues(physical evidence, employee, service process) negatively influenced the student's perceived risks. This result indicated that tangible clues can reduce the negative characteristics of service (intangibility, inseparability, perishability and variability) toward student customers at family restaurants. Meanwhile, financial risk, performance risk and social risk negatively influenced their overall satisfaction. Performance risk had the strongest negative influence on student customers' overall satisfaction, indicating that university students were much more interested in performance and utility about menu, food and service quality than in other factors at family restaurants. As a result, food-service corporations need to manage suitably various tangible clues as an important marketing strategy to diminish their customers' perceived risk and raise their satisfaction.

• The Korean Journal of Financial Management
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• v.24 no.3
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• pp.153-186
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• 2007

It is well known that the distributional properties of financial asset returns exhibit fatter-tails and skewer-mean than the assumption of normal distribution. The correct assumption of return distribution might improve the estimated performance of the Value-at-Risk(VaR) models in financial markets. In this paper, we estimate and compare the VaR performance using the RiskMetrics, GARCH and FIGARCH models based on the normal and skewed-Student-t distributions in two daily returns of the Korean Composite Stock Index(KOSPI) and Korean Won-US Dollar(KRW-USD) exchange rate. We also perform the expected shortfall to assess the size of expected loss in terms of the estimation of the empirical failure rate. From the results of empirical VaR analysis, it is found that the presence of long memory in the volatility of sample returns is not an important in estimating an accurate VaR performance. However, it is more important to consider a model with skewed-Student-t distribution innovation in determining better VaR. In short, the appropriate assumption of return distribution provides more accurate VaR models for the portfolio managers and investors.

• International Area Studies Review
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• v.12 no.2
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• pp.3-26
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• 2008

This article investigates the usefulness of the skewed Student-t distribution in modeling the long memory volatility property that might be present in the daily returns of two Australian financial series; the ASX200 stock index and AUD/USD exchange rate. For this purpose we assess the performance of FIGARCH and FIAPARCH Value-at-Risk (VaR) models based on the normal, Student-t, and skewed Student-t distribution innovations. Our results support the argument that the skewed Student-t distribution models produce more accurate VaR estimates of Australian financial markets than the normal and Student-t distribution models. Thus, consideration of skewness and excess kurtosis in asset return distributions provides appropriate criteria for model selection in the context of long memory volatility models in Australian stock and foreign exchange markets.

• Journal of The Korean Society of Integrative Medicine
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• v.12 no.2
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• pp.155-166
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• 2024

Purpose : This study aims to identify key factors for predicting dropout risk at the university level and to provide a foundation for policy development aimed at dropout prevention. This study explores the optimal machine learning algorithm by comparing the performance of various algorithms using data on college students' dropout risks. Methods : We collected data on factors influencing dropout risk and propensity were collected from N University. The collected data were applied to several machine learning algorithms, including random forest, decision tree, artificial neural network, logistic regression, support vector machine (SVM), k-nearest neighbor (k-NN) classification, and Naive Bayes. The performance of these models was compared and evaluated, with a focus on predictive validity and the identification of significant dropout factors through the information gain index of machine learning. Results : The binary logistic regression analysis showed that the year of the program, department, grades, and year of entry had a statistically significant effect on the dropout risk. The performance of each machine learning algorithm showed that random forest performed the best. The results showed that the relative importance of the predictor variables was highest for department, age, grade, and residence, in the order of whether or not they matched the school location. Conclusion : Machine learning-based prediction of dropout risk focuses on the early identification of students at risk. The types and causes of dropout crises vary significantly among students. It is important to identify the types and causes of dropout crises so that appropriate actions and support can be taken to remove risk factors and increase protective factors. The relative importance of the factors affecting dropout risk found in this study will help guide educational prescriptions for preventing college student dropout.

• Asian Pacific Journal of Cancer Prevention
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• v.17 no.8
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• pp.3741-3745
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• 2016

Background: This study was carried out to examine breast cancer risk and its fertility predictors in women aged ${\geq}35$. Materials and Methods: This cross-sectional study was conducted on 560 healthy women referred to health centers of Tabriz-Iran, 2013-2014. Five-year and lifetime risk of developing breast cancer were determined using the Gail model. General linear modeling was applied to determine breast cancer predictors. Results: The mean age of the subjects was 42.7 (SD: 7.7) years. Mean 5-year and lifetime risks of developing breast cancer were determined to be 0.6% (SD: 0.2%) and 8.9% (SD: 2.5%), respectively. Variables of family history of breast cancer, age, age at menarche, parity, age at first childbirth, breastfeeding history, frequency of breastfeeding, method of contraception, marital status and education were all found to be predictors of breast cancer risk. Conclusions: According to the results of this study, screening programs based on the Gail model should be implemented for Iranian people who have a high risk for breast cancer in order to facilitate early detection and better plan for possible malignancies.

• Journal of East Asia Management
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• v.3 no.2
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• pp.65-82
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• 2022

The purpose of this study is to analyze the effects of start-up risks on university students's entrepreneurial intention. In particular, the start-up risk considers the recent ongoing Covid_19 Pandemic along with the level of awareness of the start-up risk of business administration university students. For this purpose, a total of 204 questionnaires collected for two months from October to November in 2020 were used to verify this relevance empirically through multiple regression analysis. The empirical analysis results are as follows. First, the level of students' awareness of start-up risks has no statistically significant relevance to their entrepreneurial intention, but the higher the level of negative perception of Covid_19 Pandemic, the lower the entrepreneurial intention. On the other hand, additional analysis showed that the students with low self-efficacy majoring in business administration, it was found that negative perceptions of start-up risk had a negative effect on start-up willingness. This study is timely and different from previous studies in that it empirically verified the effect of start-up risk on business administration university students' entrepreneurial intention at a time when negative perceptions of start-up risk increase and COVID_19 Pandemic make it increasingly difficult to start a business administration universit student.

• Journal of the Korean Society of School Health
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• v.25 no.1
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• pp.105-113
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• 2012

Purpose: The purpose of this study was to identify the statistical indicators of OECD and Korea for student health among Korea's approval statistics. Methods: Searching for health indicators by using Health at a Glance 2009, Society at a Glance 2009, and Education at a Glance 2009 through the formal OECD web site in 2010, and investigating the approval statistics through the Korean formal organizational web sites and published data in 2012. Results: Among OECD indicators, indicators for adolescent health were smoking and alcohol consumption, nutrition, physical activity, overweight and obesity, bullying, risk behaviors, and poverty children. However, most of Korea student health indicators were missing except poverty children and life satisfaction, because OECD has taken chiefly data from Health Behavior in School-aged Children survey (HBSC), international study, which has not been carried out in Korea. The Ministry Of Education, Science And Technology (MEST) and the Ministry of Health and Welfare, and National Youth Policy Institute in Korea have produced the major statistics for student health which was only 11 (1.3%) among 858 approval statistics. Conclusion: Identifying a current Korea school health is essential through participating actively to OECD whose statistic indicators are internationally comparable with Students Physical Development Survey, MEST's approval statistics, using Korea Student Health Examination. It was also suggested that quantitative and qualitative expansions for Korea student health statistics by the activation of approval statistics including processed statistics, and by researchers' easy expanded access to a raw data.

• Journal of the Korea Society of Computer and Information
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• v.22 no.10
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• pp.9-17
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• 2017

The student dropout prediction is an indispensable for many intelligent systems to measure the educational system and success rate of all university. Therefore, in this paper, we propose an intelligent dropout prediction system that minimizes the situation by adopting the proactive process through an effective model that predicts the students who are at risk of dropout. In this paper, the main data sets for students dropout predictions was used as questionnaires and university information. The questionnaire was constructed based on theoretical and empirical grounds about factor affecting student's performance and causes of dropout. University Information included student grade, interviews, attendance in university life. Through these data sets, the proposed dropout prediction model techniques was classified into the risk group and the normal group using statistical methods and Naive Bays algorithm. And the intelligence dropout prediction system was constructed by applying the proposed dropout prediction model. We expect the proposed study would be used effectively to reduce the students dropout in university.