• Journal of the Korea society of information convergence
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• v.1 no.1
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• pp.47-54
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• 2008

This study analyzed the status and causes for the dropouts of college based on the survey conducted among 14,210 freshmen attending D College, other than the supernumerary special selection, from 2001 through 2005. A significant difference was shown in all items of general characteristics. The dropout rate of women, generally selected and general highschool graduated were higher than for men, specially selected and special high school graduated, respectively. The most dropouts were due to Not Return(40.16%), followed by Unenrolled(32.98%), Voluntary Leave(26.05%) and Expelled(0.81%) in order. In the distribution of the central tendency values measured from the entire subjects, the high school records and the days of absence showed a positive skewness, while the college records showed a negative skewness with the data mostly around a higher grade. The standard deviation indicating that the dropouts got the scores higher than those of the continuing students demonstrated that there was relatively insignificant difference in scores between two groups. It was demonstrated that both the high school records and the days of absence affected the dropout. The lower the high school records were, and the more the days of absence were, the more influence both items had on the dropout. The influence degree of each item was similar. Lower the scores were in terms other than the first term in the freshmen year, the more influence it had on the dropout. The most dropouts were influenced by the scores of the freshmen year, followed by the credits of the second term, the scores of the first term, the scores of the second term, and the credits of the first term in the freshmen year. Among the general characteristic items, the most dropouts were influenced by the course of study, followed by the gender. The effect of other items was insignificant.

• The Journal of the Korea Contents Association
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• v.19 no.1
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• pp.1-15
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• 2019

One of the biggest difficulties in the vocational training field is the dropout problem. A large number of students drop out during the training process, which hampers the waste of the state budget and the improvement of the youth employment rate. Previous studies have mainly analyzed the cause of dropouts. The purpose of this study is to propose a machine learning based model that predicts dropout in advance by using various information of learners. In particular, this study aimed to improve the accuracy of the prediction model by taking into consideration not only structured data but also unstructured data. Analysis of unstructured data was performed using Word2vec and Convolutional Neural Network(CNN), which are the most popular text analysis technologies. We could find that application of the proposed model to the actual data of a domestic vocational training institute improved the prediction accuracy by up to 20%. In addition, the support vector machine-based prediction model using both structured and unstructured data showed high prediction accuracy of the latter half of 90%.

• Journal of the Korea Convergence Society
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• v.9 no.6
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• pp.183-190
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• 2018

With decreasing the number of high school graduates, it is vital for each college to maintain its enrollment number as well as to preserve its dropout rate in a lower level. It is true that all universities and colleges have experienced inevitable dropouts that were in fact more serious in 2 to 3-year colleges. There have been prior studies to examine what factors affected to students' dropout in various ways. However, no specific programs were employed to mitigate the rates of dropout. In this study, new encouraging program is introduced for the students who were not ready to study and isolated from classroom. The result showed that the program led to the GPA enhancement in larger number of participants. Nevertheless, the sustainablity of the program would be unclear unless it combines with other existing programs.

• Journal of the Korea Society for Simulation
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• v.32 no.4
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• pp.19-26
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• 2023

The increase in the dropout rate of college students nationwide has a serious negative impact on universities and society as well as individual students. In order to proactive identify students at risk of dropout, this study built a decision tree, random forest, logistic regression, and deep learning-based dropout prediction model using academic data that can be easily obtained from each university's academic management system. Their performances were subsequently analyzed and compared. The analysis revealed that while the logistic regression-based prediction model exhibited the highest recall rate, its f-1 value and ROC-AUC (Receiver Operating Characteristic - Area Under the Curve) value were comparatively lower. On the other hand, the random forest-based prediction model demonstrated superior performance across all other metrics except recall value. In addition, in order to assess model performance over distinct prediction periods, we divided these periods into short-term (within one semester), medium-term (within two semesters), and long-term (within three semesters). The results underscored that the long-term prediction yielded the highest predictive efficacy. Through this study, each university is expected to be able to identify students who are expected to be dropped out early, reduce the dropout rate through intensive management, and further contribute to the stabilization of university finances.

• Communications for Statistical Applications and Methods
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• v.15 no.3
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• pp.353-365
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• 2008

The sample size in a clinical trial is determined by the hypothesis, the variance of observations, the effect size, the power and the significance level. Dropouts in clinical trials are inevitable, so we need to consider dropouts on the determination of sample size. It is common that some proportion corresponding to the expected dropout rate would be added to the sample size calculated from a mathematical equation. This paper proposes new equations for calculating sample size dealing with dropouts. Since we observe data longitudinally in most clinical trials, we can use a last observation to impute for missing one in the intention to treat (ITT) trials, and this technique is called last observation carried forward(LOCF). But LOCF might make deviations on the assumed variance and effect size, so that we could not guarantee the power of test with the sample size obtained from the existing equation. This study suggests the formulas for sample size involving information about dropouts and shows the properties of the proposed method in testing equality of means.

• Journal of Digital Convergence
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• v.12 no.8
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• pp.61-66
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• 2014

The study aimed to investigate the relative levels of grit and their relationship with potential dropping-out and university adjustment of foreign students in Korea. The subjects of this survey were gathered through purposive sampling, and 335 subjects were collected from university students in South Korea. First, the grit was significantly and positively correlated with emotional adjustment, social adjustment, university satisfaction, and academic adjustment, and was negatively correlated with potential dropping-out of university. Drop-out potential is negatively and significantly correlated with all subgroups of university life adjustment. Second, the grit is higher than the mid-point and drop-out potential is very low. Emotional adjustment and university satisfaction are the highest among the subgroups of university life adjustment but social adjustment is the lowest among them. Third, it was found that foreign students in the mid and high grit clusters are lower in mean drop-out potential rates than those in the low grit cluster. And foreign students in the mid and high grit clusters are higher than those students in the low university life adjustment group.

• Journal of The Korean Association of Information Education
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• v.13 no.1
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• pp.9-22
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• 2009

As information technology(IT) has been rapidly developed, e-learning is also growing to meet the need of lifelong education using internet. However, with the growth of e-learning has come the big problem of high dropout rates. The purpose of this present study was to identify the major factors influencing drop-out in corporate e-learning. 250 employees(persistence: n=157, dropout: n=93) who enrolled an e-learning course in S company were participated in this study. A logistic regression analysis was performed to identify predictors of dropout. It was determined that individual background(marriage, amount of study time, difficult to combine work and family), learners' characteristics and value of the course were able to predict dropout with nearly 75 percent accuracy.

• Journal of Korea Society of Industrial Information Systems
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• v.27 no.4
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• pp.81-92
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• 2022

In this study, interview scripts written in the dropout application was analyzed using BERTopic,, and parental influence, academic adaptation, university dissatisfaction was derived as major topics. An empirical study was conducted through a survey of 199 current students with researchmodel composed of those factors affecting dropout intention. The result shows that parental influence had a negative effect on academic adaptation and university satisfaction. Academic adaptation and university satisfaction had a negative effect on the dropout intention. parental influence did not directly affect the dropout intention, but had an indirect positive effect through academic adaptation and university/major satisfaction. The result shows that university satisfaction and academic adaptation is important factor to lower the dropout intention of students who chose current university by parental influence.

• Proceedings of the KIEE Conference
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• 2003.11a
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• pp.119-121
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• 2003

전력계통에 대해 적용되고 있는 수많은 안정화 방안으로 발전기 탈락, 부하차단, 계통분리 등의 방법 중에서, 우리나라 계통에서의 과도안정도 문제에 대한 안정화 대책으로는 주로 발전기 탈락이 많이 적용되어 왔다. 본 논문은 이러한 발전기 탈락량에 관한 주제를 다루고 있다. 이러한 발전기 탈락을 적용하기 위해서 과도안정도 취약지점에 대해 KPX 보고서를 참조하여 상정사고 지점을 선정하고, 해당지역 계통에 대해 등면적법을 적용, 적정 발전기 탈락량을 계산하였다. 이러한 사전 연산형(offline) 형 발전기 탈락 외에도 온라인 상에서의 발전기 탈락량 계산방법과 그 문제점에 대해서도 고려하였다.

• Journal of Industrial Convergence
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• v.19 no.1
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• pp.117-127
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• 2021

The purpose of this study was to identify the relationship between social support, academic self-efficacy, and learning agility on intention of academic dropout among nursing students. Data collection was conducted online surveys from November 9 to 27, 2020. The 363 students were conveniently sampled from the school of nursing in K-do in Korea. The contents of the self-reported questionnaire included social support, academic self-efficacy, learning agility, intention of academic dropout. As a result, The score of each variables were like this: social support 4.32, academic self-efficacy 3.66, learning agility 3.40, intention of academic dropout 2.08. The factors that affecting intention of academic dropout among nursing students are academic self-efficacy, learning agility, satisfaction on major, perceived mental health status, grade in score and grade, which explained 30.4% of the variances. Therefore in order to lower the intention of dropping out of nursing students, it is considered that the development of programs considering individual characteristics and systematic support are necessary.