• 비교교육연구
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• 제24권3호
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• pp.47-69
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• 2014

청소년기의 발달적 특성, 가족의 해체, 빈곤, 학업 스트레스 등으로 위기학생이 증가하고 있다. 이러한 위기상황은 학생 개인뿐만 아니라 사회전체에 역기능적인 영향을 미칠 가능성이 높다. 본 연구에서는 위기학생의 발생의 원인을 고찰하고 경상북도에서 시행되고 있는 위기학생 관리 지원체제 현황을 바탕으로 경상북도 위기학생 지원정책의 개선방향을 도출하였다. 경상북도의 위기 학생 지원 전략을 분석을 토대로 보완방안은 첫째는 위기학생의 예방 및 조기발견체제 구축이며, 둘째는 발견된 위기학생을 지도하기 위한 교육안전망을 구축, 셋째, 가정 지역사회와의 연계로 설정하고 각 영역별 보완 방안을 제시하였다.

• 자원ㆍ환경경제연구
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• 제16권4호
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• pp.947-978
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• 2007

본 연구에서는 원유시장의 변동성 분석에 적용될 수 있는 VaR(Value-at-Risk) 접근법을 고찰한다. 그리고 다양한 VaR 모형들(RiskMetrics, GARCH, IGARCH와 FIGARCH 모형)의 성과를 정규분포와 치우친 Student-t 분포 가정 하에서 평가한다. Brent 및 Dubai 시장의 일별가격 자료를 이용한 실증분석 결과에 따르면, FIGARCH 모형이 GARCH 모형이나 IGARCH 모형보다 원유시장의 변동성에 내재되어 있는 장기기억 특성을 잘 반영한다는 점에서 더 우월한 것으로 나타났다. 이러한 사실은 원유시장 수익률의 변동성에는 장기기억이 존재한다는 것을 의미한다. 그리고 VaR 분석 결과, 치우친 Student-t 분포 가정 하에서 추정되는 FIGARCH 모형이 롱 포지션과 숏 포지션 모두에서 정규분포 가정 하에서 추정되는 다른 변동성 모형들보다 원유시장에서의 투자 위험을 더 정확하게 예측하는 것으로 나타났다. 이러한 사실은 치우친 Student-t 분포 가정이 원유시장 수익률 분포에 내재되어 있는 비정상적 왜도와 첨도를 모형화하는데 더 적합하다는 것을 의미한다. 이와 같은 발견은 원유시장 구매자 및 판매자들이 원유가격의 움직임을 올바르게 측정하고 VaR을 정확하게 추정하는데 도움을 줄 것이다.

• 한국식품조리과학회지
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• 제22권3호통권93호
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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.

• 재무관리연구
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• 제24권3호
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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.

• 국제지역연구
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• 제12권2호
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• pp.3-26
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• 2008

본 논문은 호주 금융시장의 두 가지 시계열(ASX200 주가지수와 AUD/USD 환율)의 수익률 자료에 존재할 수 있는 장기기억 변동성 특성을 모형화하는 데 skewed Student-t 분포가 유용한지를 연구한다. 이러한 연구목적을 위하여 FIGARCH 및 FIAPARCH Value-at-Risk (VaR) 모형을 교란항에 대한 정규분포, Student-t 분포 및 치우친 Student-t 분포 가정하에서 평가한다. 실증분석 결과 skewed Student-t 분포 모형이 정규분포 모형이나 Student-t 분포 모형보다 호주 금융시장의 VaR을 더 정확하게 추정한다는 발견하였다. 따라서 자산 수익률 분포의 왜도 및 첨도를 고려하는 것은 호주 주식시장과 외환시장의 장기기억 변동성 모형을 검토할 때 적절한 모형선택 기준을 제공한다는 것을 알 수 있다.

• 대한통합의학회지
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• 제12권2호
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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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• 제17권8호
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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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• 제3권2호
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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.

• 한국학교보건학회지
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• 제25권1호
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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.

• 한국컴퓨터정보학회논문지
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• 제22권10호
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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.