• Journal of Korean Academy of Dental Administration
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• v.10 no.1
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• pp.9-21
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• 2022

This study was carried out to understand the influence of dental technicians' image and major satisfaction on dental technology students' dropout intention. A questionnaire survey was administered to 273 students in the Department of Dental Technology. Frequency analysis, independent sample t-test, one-way ANOVA, and multiple regression analysis were performed. The results are as follows: The image of dental technicians differed significantly depending on factors such as school year, personality, whether a family member is a dental technician or not, reason for choosing a department, difficulty in one's own academic life, and disciplinary life adaptation (p<0.05). The image of dental technicians was seen to be positively perceived when the respondent had an affirmative personality, or had a dental technician among family members or chose the department for its uniqueness, or found no difficulty in academic life, and adapted well to a situational or environmental change. Major satisfaction of dental technicians was seen a meaningful difference in items such as school year, personality, awareness of dental technicians, reason for choosing a department, the difficulty in own academic life, and the disciplinary life adaptation (p<0.05). Individuals with high major satisfaction and low dropout intention included those with a positive personality, who think affirmatively about dental technicians, who chose a department owing to suiting the aptitude and interest or to the uniqueness of the major. The influential factors on dropout intention included general satisfaction out of major satisfaction, perspective and self-confident image among dental technicians, and an awareness of dental technicians. The establishment of a desirable image of dental technicians and the improvement in major satisfaction are considered likely to contribute to reducing the dropout intention of dental technology students.

• Journal of Technologic Dentistry
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• v.35 no.3
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• pp.243-257
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• 2013

Purpose: This study aims to analyze the factors affecting the dropout intentions of the dental technology students of a college. Methods: The subject of this study was 76 freshmen and 74 sophomores of dental technician major in an anonymous college. Results from the questionnaire called K-vision diagnosis program were computed by means of t-test, One-Way ANOVA, and correlation analysis. Results: 1. Total points of the drop out intention came to 782.14 points. Of the five categories concerned with the drop out intention, complain in college satisfaction(50.12points) was the highest and department satisfaction(47.51points) was the lowest. Of 16 subcategories, complaining in administrative supporting system proved the highest as 50.80 points and Inquiry to Professor the lowest(45.56 points). 2. Among the general characteristic gender (p<. 01), student group (p<.01), and credit (p<.05) made a meaningful statistical difference; no statistical significance was found in grade, admission, and dwellings. 3. Of the five categories, statistical significance was shown as follows; Department satisfaction (p<.01), College satisfaction (p<.05) under gender, Department satisfaction (p<.05) in grade, Academic integration (p<.01), Department satisfaction (p<.01) in credit. No statistical meaning was found in admission and dwellings. 4. Statistical significance was found under 16 subcategories as follows: Career identification(p<.01), Academic support system(p<.01), Social activity II(p<.05) in gender area, Inquiry to professor(p<.01), Learning(p<.05), Understanding learning I(p<.05) in grade area, Learning(p<.001), Career identification(p<.001), Understanding learning I(p<.01), Understanding learning II(p<.01), Inquiry to professor (p<.01), Learning ability (p<.05), Occupation (p<.05), Social Activity II(p<.05), Administrative support system (p<.05) in student group area, Credit (p<.001), Career identification (p<.01), Understanding learning I(p<.05) in credit area; admission and dwellings was statistically meaningless. 5. Of the 5 categories academic integration (r=.766) was most relevant to the dropout intention of the subjects and followed by department satisfaction (r=.735), college satisfaction (r=.554), service acceptability (r=.373), and statistical significance was shown as p<.01. Conclusion: Considering the results of this study, we are in a pressing need for the introduction of policies and programmes aiming at preventing the dropout rates of the dental technician majors at college. In tandem with this, qualitative and viable human resource management of the dental technicians should be implemented.

• 한국정보컨버전스학회:학술대회논문집
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• 2008.06a
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• pp.89-92
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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 high school 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.

• 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.

• The Journal of the Korea Contents Association
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• v.19 no.6
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• pp.257-270
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• 2019

The purpose of this study is to identify the factors which influence dropout intention of college freshmen. For this purpose, the freshmen were collected from A University in Gyeonggi Province from October 16 to 30, 2017. We analyzed 1,369 data in terms of the individual, family, educational institution, and social factors using t-test, ANOVA and multiple regression. The major findings of the study are as follows. First, individual factors which influence dropout intention of freshmen are overall university satisfaction, the degree to which they think their majors fit them, the degree to which they are willing to recommend their department to others, a plan to dropout, and a plan to take a leave of absence. Second, the family factors are the encouragement of parents and family to do well in college. Third, educational institution factors are satisfaction with the curriculum of majors, a sense of belonging to department, the degree to which the professors instill career vision and self-esteem, and satisfaction with college facilities. Fourth, social factors are the degree they think going to college was a good choice in their life and the degree they think knowledge learned in college is helpful to them. Based on the results, the study suggested what to be considered at professors, school personnel, and university level and discussed how to reduce dropout rate of freshmen in university.

• Journal of the Korea Convergence Society
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• v.13 no.4
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• pp.541-552
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• 2022

This study is a secondary data analysis study using the 1^st Panel Survey of School Dropouts in Korea for investigating predictors of suicide attempts in out-of-school youths (OSYs). Data analysis were performed using the SPSS 26.0 statistical program. Suicide attempts were reported in 62 (8%) of the 776 participants included in the study. Logistic regression analysis revealed that suicide attempts before school dropout (OR=10.66), experience of violence victimization (OR=6.97), alcohol consumption (OR=3.73), depression (OR=2.62), parental attachment (OR=0.47), peer relationships (OR=0.63) before school dropout were significant predictors of suicide attempts. Prevention of suicide attempts by OSYs should be preceded by confirmation of their experience in suicide attempts before school dropout. In addition, it is required to establish a suicide prevention program considering psychological situations, interpersonal relationships, and violence experiences.

• The Journal of Korean Association of Computer Education
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• v.17 no.1
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• pp.1-12
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• 2014

Since students' behaviors during e-learning are automatically stored in LMS(Learning Management System), the LMS log data convey the valuable information of students' engagement. The purpose of this study is to develop a prediction model of e-learning course dropout by utilizing LMS log data. Log data of 578 college students who registered e-learning courses in a traditional university were used for the logistic regression analysis. The results showed that attendance and study time were significant to predict dropout, and the model classified between dropouts and completers of e-learning courses with 96% accuracy. Furthermore, the feasibility of early detection of dropouts by utilizing the model were discussed.

• Journal of the Korea Convergence Society
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• v.12 no.10
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• pp.37-43
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• 2021

This study proposed an effective automatic classification technique to identify dropout patterns of university students, and based on this, an intelligent recommender system to prevent dropouts. To this end, 1) a data processing method to improve the performance of machine learning was proposed based on actual enrollment/dropout data of university students, and 2) performance comparison experiments were conducted using five types of machine learning algorithms. 3) As a result of the experiment, the proposed method showed superior performance in all algorithms compared to the baseline method. The precision rate of discrimination of enrolled students was measured to be up to 95.6% when using a Random Forest(RF), and the recall rate of dropout students was measured to be up to 80.0% when using Naive Bayes(NB). 4) Finally, based on the experimental results, a method for using a counseling recommender system to give priority to students who are likely to drop out was suggested. It was confirmed that reasonable decision-making can be conducted through convergence research that utilizes technologies in the IT field to solve the educational issues, and we plan to apply various artificial intelligence technologies through continuous research in the future.

• Transactions on Electrical and Electronic Materials
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• v.17 no.3
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• pp.172-177
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• 2016

Miller feedback compensation is introduced in a low dropout regulator (LDO) in order to obtain a capacitor-free regulator and improve the fast transient response. The conventional LDO has a limited bandwidth because of the large-size output capacitor and parasitic gate capacitance in the power MOSFET. In order to obtain a stable frequency response without the output capacitor, LDO is designed with resistor-capacitor (R-C) compensation and this is achieved with a connection between the gain-stage and the power MOS. An R-C compensator is suggested to provide a pole and zero to improve the stability. The proposed LDO is designed with the 0.35 μm CMOS process. Simulation testing shows that the phase margin in the Bode plot indicates a stable response, which is over 100^o. In the load regulation, the transient time is within 55 μs when the load current changes from 0.1 to 1 mA.

• Journal of Korea Entertainment Industry Association
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• v.15 no.8
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• pp.279-290
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• 2021

As the university's academic management was not carried out smoothly due to COVID-19, and most of them were conducted as non-face-to-face classes, students' dropout is steadily increasing. In this study focus group interviews were conducted to analyze physics students and professors' recognition of the COVID-19 non-face-to-face class environment. Based on the results, the implications of non-face-to-face classes for physics education were presented. Physics students described their feelings about un-tact teaching as 'the class in which the body is comfortable but the mind is uncomfortable', 'a person who is smarter than me seems to explain a book, reading it' and 'a short clip lecture which may be comfortable but cause losses to me', while the professors also described them as 'a fully transformed class system' and 'a online class putting much burden on me'. Regarding school dropout, students said that the concerns about dropout during non-face-to-face classes were deepened about transfer or transfer. The professors said that the department atmosphere had lost vitality due to the increase in non-face-to-face classes and academic dropouts, and had a lot of worries because of the recruitment rate and external university evaluation. The implications of the COVID-19 non-face-to-face class situation for physics education suggest that it is required to strengthen the interaction between professors and students, finding ways to enhance the sense of reality to supplement laboratory classes and giving opportunities to professors to share their pedagogical contents knowledge in physics.