• 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.

• 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.

• 한국전자통신학회논문지
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• 제16권6호
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• pp.1255-1262
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• 2021

학업을 중단하는 학생들의 비율이 해마다 증가하고 있어 대학은 학업중단을 막기 위하여 위험요소를 파악하고 이를 사전에 제거하기 위해 노력하고 있다. 그러나 특정 위험요소의 단변수 분석을 통해 위기학생을 관리하고 있어 예측이 부정확한 문제가 발생하고 있다. 본 연구에서는 이러한 문제점을 해결하기 위하여 학업중단 위험요소를 파악하고 학업중단 예측을 위해 머신러닝 방법을 통해 다변수 분석을 실시한다. 또한 다양한 예측방법별로 성능평가를 수행하여 최적화 방법을 도출하고 학업중단을 발생시키는 위험요소간의 연관성과 기여도를 평가한다.

• Nutrition Research and Practice
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• 제8권1호
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• pp.94-102
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• 2014

Diet attrition and failure of long term treatment are very frequent in obese patients. This study aimed to identify pre-treatment variables determining dropout and to customise the characteristics of those most likely to abandon the program before treatment, thus making it possible to modify the therapy to increase compliance. A total of 146 outpatients were consecutively enrolled; 73 patients followed a prescriptive diet while 73 followed a novel brief group Cognitive Behavioural Treatment (CBT) in addition to prescriptive diet. The two interventions lasted for six months. Anthropometric, demographic, psychological parameters and feeding behaviour were assessed, the last two with the Italian instrument VCAO Ansisa; than, a semi-structured interview was performed on motivation to lose weight. To identify the baseline dropout risk factors among these parameters, univariate and multivariate logistic models were used. Comparison of the results in the two different treatments showed a higher attrition rate in CBT group, despite no statistically significant difference between the two treatment arms (P = 0.127). Dropout patients did not differ significantly from those who did not dropout with regards to sex, age, Body Mass Index (BMI), history of cycling, education, work and marriage. Regardless of weight loss, the most important factor that determines the dropout appears to be a high level of stress revealed by General Health Questionnaire-28 items (GHQ-28) score within VCAO test. The identification of hindering factors during the assessment is fundamental to reduce the dropout risk. For subjects at risk, it would be useful to dedicate a stress management program before beginning a dietary restriction.

• 한국학교보건학회지
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• 제34권3호
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• pp.151-160
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• 2021

Purpose: The purpose of this study was to identify predictors of suicidal attempts in adolescents over 5 years after school dropout. Methods: The data of the Panel Survey of School Dropouts (of 2013 to 2017) conducted by the National Youth Policy Institute were analyzed. The analysis used the 2013 survey data as the baseline and examined suicidal attempts from 2013 to 2017. A total of 776 adolescents were included in the analysis. Descriptive statistics, 𝝌² test, t-test, and multiple logistic regression were carried out using SAS 9.2. Results: About 11% (87 out of 776) of the adolescents with an experience of dropout attempted suicide between 2013 and 2017. The risk of suicidal attempts was significantly lower in female (AOR: 0.57, 95% CI: 0.87~0.93) than in male adolescents. The higher the self-esteem, the lower the risk of suicidal attempts (AOR: 0.87. 95% CI: 0.78~0.97). The higher the depression level (AOR: 1.10, 95% CI: 1.05~1.16) and the rate of parental abuse (AOR: 1.09, 95% CI: 1.02~1.18), the higher the risk of suicidal attempts. Conclusion: The findings of the study suggest that those who are male, depressed, have low self-esteem or have been abused by their parents are at high risk of suicidal attempts among the adolescents with dropout experiences. Therefore, early intervention is necessary for those at high risk.

• 한국콘텐츠학회논문지
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• 제15권4호
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• pp.277-290
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• 2015

본 연구는 비행 청소년이 학교폭력과 가정폭력, 학업중단, 자살이라는 위기로부터 일상의 건전한 청소년으로 회귀시키거나 선제적 개입 또는 사전 예방을 위해 실제로 위기에 내몰린 청소년을 분석하여 위기문제간의 연관된 패턴을 찾아내는데 목적이 있다. 연구 결과, 학업중단에 영향을 미치는 요인으로는 보호관찰, 범법, 흡연, 음주, 가출, 가정폭력_피해, 자살 등이 발견되었다. 특히 청소년이 가출을 해서 음주와 흡연을 하는 경우에는 학업중단의 위험이 그렇지 않은 청소년보다 2.76배 높은 것으로 예측되었고, 흡연보다 음주가 청소년의 학업중단에 더 크게 작용하는 것으로 밝혀졌다. 본 연구는 위기 청소년 문제를 해결하기 위해 당면한 문제에만 초점을 맞추기 보다는 잠재적 위기청소년의 가정과 학교, 지역사회 전반을 아우르는 복합적 위기관리가 필요함을 입증한 과학적 근거자료로서의 활용가치를 가진다.

• Communications for Statistical Applications and Methods
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• 제9권3호
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• pp.703-713
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• 2002

In longitudinal studies, outcomes are repeatedly measured over time for each subject. It is common to have missing values or dropouts for longitudinal data. In this study time trend in longitudinal data with dropouts is of concern. The confounding of time trend with dropout process is investigated through simulation studies. Some simulation results are reported for binary responses as well as continuous responses with patterns of dropouts varying. It has been found that time trend is not confounded with random dropout process for binary responses when it is estimated using GEE.

• 한국시뮬레이션학회논문지
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• 제32권4호
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• pp.19-26
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• 2023

전국 대학생의 중도 탈락 비율의 증가는 학생 개인 뿐만 아니라 대학과 사회에 심각한 부정적 영향을 끼친다. 본 연구에서는 중도 탈락이 예상되는 학생을 사전에 식별하기 위하여, 각 대학의 학사관리 시스템에서 손쉽게 얻을 수 있는 학적 데이터를 기반으로 머신러닝 분야의 결정트리, 랜덤 포레스트, 로지스틱 회귀 및 딥러닝 기반의 중도 탈락 예측 모델을 구축하고, 그 성능을 비교·분석하였다. 분석 결과 로지스틱 회귀 기반 예측 모델의 재현율이 가장 높았으나 f-1 및 auc 값이 낮은 한계를 보였고, 랜덤 포레스트 기반의 예측 모델의 경우 재현율을 제외한 다른 모든 지표에서 가장 우수한 성능을 보였다. 또한 예측 기간에 따른 예측 모델의 성능을 확인하기 위하여 예측 기간을 단기(1개 학기 이내), 중기(2개 학기 이내) 및 장기(3개 학기 이내)로 나누어 분석해 본 결과, 장기 예측 시 가장 높은 예측력을 보였다. 본 연구를 통해 각 대학은 중도 탈락이 예상되는 학생들을 조기에 식별하고, 이들에 대한 집중 관리를 통해 중도 탈락 비율을 줄이며 나아가 대학 재정 안정화에 기여할 수 있을 것으로 기대된다.

• Korean Journal of Acupuncture
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• 제35권1호
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• pp.41-45
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• 2018

Objectives : To investigate minimum pressure by verifying changes in pressure due to bleeding amount during bloodletting-cupping therapy. Methods : (1) We compared adhesion performance of four different cupping cups of same size: two disposable cupping cups(A, B) and two reusable cupping cups(A, B) each were vacuumed three times and kept in place for 10 minutes. (2) We vacuumed two different sized disposable cupping cups(A), size.1(InnerDiameter 48.8 mm) and size.3(InnerDiameter 39.1 mm), twice each(-200 mmHg) on silicon plate. We injected water and air at regular intervals in cupping cups by using a syringe, and then measured change of pressure in cupping cups and pressure at the time of dropout. Results : (1) Pressure reduction was $4.75{\pm}2.78%$ on average in the order of 'Disposable[A]>reusable[B]>Disposable[B]>reusable[A]', so that pressure retention performance of disposable cups can't be regarded as inferior to that of reusable cups. (2) Pressure of disposable cupping B(size.1) decreased by an average of -40.08 mmHg per 5 ml of water. At -24.8 mmHg, when 22 ml of water has been injected, cup has come off. Pressure of disposable cupping B(size. 3) decreased by an average of -99.4 mmHg per 5 ml of water. At -48.6 mmHg, when 13 ml of water was injected, cupping came off. Conclusions : Considering reduction rate of pressure due to water injection, in case of bleeding more than 15 ml, size.3 cup always comes off, therefore it needs to be re-operated at least once. Meanwhile, size.1 cup does not always come off in the same condition, depending on the initial pressure and therefore, re-operation may be considered.

• Journal of Acupuncture Research
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• 제38권1호
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• pp.41-46
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• 2021

Background: Pressure changes related to temperature variation during cupping may lead to dropout. This study aimed to investigate pressure changes related to temperature variations in the cup during the cupping procedure. Methods: Changes in temperature and pressure were measured for 15 minutes after the procedure was performed using the alcohol rub method with glass cups and with the addition of infrared irradiation. Changes in temperature and pressure were also measured for 15 minutes after pumping 3 times using the valve suction method, and with the addition of infrared irradiation. Results: In a comparison between the alcohol rub method with glass cups and with the addition of infrared irradiation, the negative pressure increased over time in the absence of infrared irradiation, whereas it decreased when performed with infrared irradiation p = 0.094. However, in a comparison between pumping 3 times using the valve suction method, and with the addition of infrared irradiation, the negative pressure decreased in both cases, but this was more significant with infrared irradiation p = 0.172. There was a significantly higher temperature in the glass cups (p = 0.004) and the valve cups (p = 0.001) exposed to infrared radiation, compared with no infrared irradiation. Conclusion: The reduction in negative pressure inside the cups exposed to infrared radiation was greater than without infrared irradiation. Temperature increases inside the cup can lead to the risk of dropout.