• Journal of Engineering Education Research
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• v.15 no.5
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• pp.98-107
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• 2012

As the dropout rate of university students is gradually increasing, the studies on exploring the status, characteristics, reasons, and countermeasures of dropout of university students are currently grabbing high attention. This paper analyzes the relationship between the admission types and dropout of university students, mainly focused on engineering students. The analysis shows that the dropout rate of engineering students admitted through the scheduled-time admission procedures is quite higher than that of students admitted through non-scheduled-time admission procedures, the dropout rate of engineering students admitted from the vocational high schools is higher than that of students from the academic high schools, and the dropout rate of engineering students admitted from the liberal art high school tracks is higher than that of students from the natural science high school tracks. From the results, we could find out that student-support programs need to be carefully provided for the engineering university students according to their admission types and underlying backgrounds.

• Journal of the Korea Society of Computer and Information
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• v.23 no.5
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• pp.65-72
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• 2018

This study analyzes college dropout cases to reduce its rate. The analysis is preferentially carried out by figuring out our current situation of college dropout rate in pertinent cases and all around country's. Based on the current states, statistical analysis is accomplished as follows; analyzing the characteristic differences between the being in school's and the dropouts' by T-Test, determining the influence factor by logistic regression analysis and drawing the target group for special treatments through these statistical analysis. To reduce dropout rate, several measures could be adopted; focused counseling for each target group, special monitoring for students on leave of absence and opening major subjects for improving relationship between students and professors. The measures suggested by the analysis through this study are expected to lower the dropout rates effectively in college or specific fields including engineering science.

• Journal of Korea Society of Digital Industry and Information Management
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• v.19 no.4
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• pp.137-146
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• 2023

The dropout of university freshmen is a very important issue in the financial problems of universities. Moreover, the dropout rate is one of the important indicators among the external evaluation items of universities. Therefore, universities need to predict dropout students in advance and apply various dropout prevention programs targeting them. This paper proposes a method to predict such dropout students in advance. This paper is about a method for predicting dropout students. It proposes a method to select dropouts by applying logistic regression using a shift sigmoid classification function using only quantitative data from the first semester of the first year, which most universities have. It is based on logistic regression and can select the number of prediction subjects and prediction accuracy by using the shift sigmoid function as an classification function. As a result of the experiment, when the proposed algorithm was applied, the number of predicted dropout subjects varied from 100% to 20% compared to the actual number of dropout subjects, and it was found to have a prediction accuracy of 75% to 98%.

• The Journal of Bigdata
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• v.4 no.2
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• pp.175-183
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• 2019

Drop-out issue is one of the challenges of cyber university. There are about 130,000 students enrolled in cyber universities, but the dropout rate is also very high. To lower the dropout rate, cyber universities invest heavily in learning analytics. Some cyber universities analyze the possibility of dropout and actively support students who are more likely to drop out. The purpose of this paper is to identify the learning data affecting the dropout prediction index. As a result of the analysis, it is confirmed that number of lessons(progress), credits, achievement and leave of absence have a significant effect on dropout rate. It is necessary to increase the accuracy of the prediction model through post-test on the student dropout prediction index.

• 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.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.7
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• pp.380-390
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• 2020

Cyber-university has a higher rate of dropout freshmen due to various educational factors, such as social background, economic factors, IT knowledge, and IT utilization ability than students in twenty offline-based university. These students require a different dropout prevention method and improvement method than offline-based universities. This study examined the main factors affecting dropout during the first semester of 2017 and 2018 A Cyber University. This included management and counseling factors by the 'Decision Tree Analysis Model'. The Management and counseling factors were presented as a decision-making method and weekly methods. As a result, a 'Dropout Improvement Model' was implemented and applied to cyber-university freshmen in the first semester of 2019. The dropout-rate in freshmen applying the 'Dropout Improvement Model' decreased by 4.2%, and the learning-persistence rate increased by 11.4%. This study applied a questionnaire survey, and the cyber-university students LMS (Learning Management System) learning results were analyzed objectively. On the other hand, the students' learning results were analyzed quantitatively, but qualitative analysis was not reflected. Nevertheless, further study is necessary. The 'Dropout Improvement Model' of this study will be applied to help improve the dropout rate and learning persistence rate of cyber-university.

• JSTS:Journal of Semiconductor Technology and Science
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• v.13 no.4
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• pp.263-271
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• 2013

A low dropout regulator (LDO) with fast transient responses is presented. The proposed LDO eliminates the trade-off between slew rate and unity gain bandwidth, which are the key parameters for fast transient responses. In the proposed buffer, by changing the slew current path, the slew rate and unity gain bandwidth can be controlled independently. Implemented in $0.18-{\mu}m$ high voltage CMOS, the proposed LDO shows up to 200 mA load current with 0.2 V dropout voltage for $1{\mu}F$ output capacitance. The measured maximum transient output voltage variation, minimum quiescent current at no load condition, and maximum unity gain frequency are 24 mV, $7.5{\mu}A$, and higher than 1 MHz, respectively.

• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• v.27 no.3
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• pp.173-180
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• 2016

Objectives: The purpose of this study was to explore the influence of high school students' mental health on their academic achievement and school dropout rate by using longitudinal data. Methods: For this study, 153 high schools were randomly sampled on a nationwide scale. The descriptive data was collected from a school information internet site which is organized by the Korean Ministry of Education. We used the schools' mean scores on the Adolescent Mental Health Problem-Behavior Questionnaire-II (AMPQ-II) scale which was used for estimating students' mental health in the 2013 school-based mental health screening test. The data analysis was conducted by using hierarchical regression analysis. Results: The results of this study showed that the AMPQ-II scale's mean scores have significant effects on the following year's academic achievement and annual dropout rates. Especially, the AMPQ-II scale's mean scores tend to increase the school dropout rate in vocational schools. Conclusion: The finding of this study is that Korean high school students' mental health states significantly influence their academic achievement and school dropout rate.

• 중국학논총
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• no.67
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• pp.157-177
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• 2020

The rapid change in the university environment due to the decrease in the school-age population calls for enhancing the competitiveness of China-related departments. In this paper, the university's competitiveness and dropout rate were studied in combination with various factors such as geographical location of Chinese-related departments set up at national universities, convergence with other departments, competition rate for entrance exams, scholarships, and employment rate that have a comprehensive impact on student satisfaction. In particular, the dropout rate presented research results that could help universities strengthen their competitiveness in China-related departments, such as by differentiating customized academic strategies according to the atmosphere of elimination through multiple regression analysis and quantile analysis. We hope this thesis will be the basis for policymaking and judgment in China-related departments.

• Nutrition Research and Practice
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• v.8 no.1
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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.