• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.12
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• pp.1676-1681
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• 2020

Collecting and managing information on all-round activities of students not only helps to improve school life adjustment and satisfaction, but also serves as the basis for preventive student support for academic warnings or dropouts. Therefore, in guiding students, it is necessary to integrate various information of students and provide them to the instructor quickly and accurately. In this study, in the special environment of an organization in which student information is scattered in several systems, a system model is proposed that not only integrates and provides student information to instructors quickly, but also saves cost and effort. And we tested the effectiveness of the proposed system model by developing a simple web page. As a result, it was confirmed that the proposed model provided information necessary for student guidance in an integrated, multifaceted, and instantaneous manner, and that user satisfaction was improved through an improved UI.

• 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.

• Journal of Fashion Business
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• v.15 no.4
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• pp.87-109
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• 2011

This study was to examined the relationship between goal setting, self improvement, social support of parents, friendship, school adaptation, drop out. In our research model, goal setting, self improvement, social support of parents, and friendship is exogenous variable and school adaption and drop out is endogenous variable. A total of 323 undergraduate student(254 female, 69 male) complete the questionnaires. Structural equation modelling showed that, as hypothesized, establishment of goals, social support of parent and friendship have effect on school adaptation, and friendship also have direct effect on drop out. School adaptation mediate path from goal setting, social support of parents, and friendship. This study provides empirical evidence for a model that show how to control the drop out of students.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.11
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• pp.1608-1614
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• 2022

As competition between universities intensifies due to the recent decrease in the number of students, it is recognized as an essential task of universities to predict students who are underperforming at an early stage and to make various efforts to prevent dropouts. For this, a high-performance model that accurately predicts student performance is essential. This paper proposes a method to improve prediction performance by removing or amplifying abnormal data in a classification prediction model for identifying underperforming students. Existing anomaly data processing methods have mainly focused on deleting or ignoring data, but this paper presents a criterion to distinguish noise from change indicators, and contributes to improving the performance of predictive models by deleting or amplifying data. In an experiment using open learning performance data for verification of the proposed method, we found a number of cases in which the proposed method can improve classification performance compared to the existing method.

• Journal of the Korean Society of School Health
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• v.34 no.2
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• pp.123-132
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• 2021

Purpose: Adolescents are vulnerable to depression; however, many health policies for adolescents tend to target students in schools. This study aims to identify factors related to depression according to gender among adolescents who have ceased attending school either temporarily or permanently. Methods: The data were generated from the 5th Dropout Youth Panel Survey (2017), and this study included 318 students in the survey that had dropped out of school. The data were analyzed using hierarchical multiple linear regression to identify related factors in depression among the participants. The analyses were performed by SPSS 25.0 program. Results: The depression scores of the students who had ceased attending school were: 20.28±5.47 for boys; 21.23±5.88 for girls. Their depression scores are significantly associated with self-esteem (p<.001 for boys; p=.001 for girls) and social stigma (p=.002 for boys; p=.002 for girls). Among those, peer attachment (p=.050), community integration (p=.004), and community disorder (p<.001) were significantly associated with depression only in boys. Conclusion: The findings of this study suggest that strategies for managing depression in adolescents who have dropped out of school should address the differences in contributing factors according to gender. This study also suggests a basis for approaching such a strategy.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.6
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• pp.1255-1262
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• 2021

The proportion of students dropping out of universities is increasing year by year, and they are trying to identify risk factors and eliminate them in advance to prevent dropouts. However, there is a problem in the management of students at risk of dropping out and the forecast is inaccurate because crisis students are managed through the univariable analysis of specific risk factors. In this paper, we identify risk factors for university dropout and analyze multivariables through machine learning method to predict university dropout. In addition, we derive the optimization method by evaluation performance for various prediction methods and evaluate the correlation and contribution between risk factors that cause university dropout.

• 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%.

• 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 Digital Convergence
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• v.16 no.10
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• pp.333-342
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• 2018

The objective of this study was to investigate the problem behavior and its convergent factors in out-of-school adolescents, with a focus on gender differences. This study was a secondary data analysis study using out-of-school adolescents research data at Busan women and family development institute. The study was conducted in a total of 499 out-of-school adolescents (337 males, 162 females). The type of the 8 problem behaviors (run away from home, drop out, prostitution, violence, internet game addiction, theft, drug addiction, and smoking) were identified. The collected data were analyzed with multiple logistic regression. Among the problem behaviors of the participants, internet game addiction and theft were more significantly high in male out-of-school adolescents than female out-of-school adolescents. In internet game addiction, male out-of-school adolescents were 1.90 times higher than female out-of-school adolescents (p=.008, 95% CI=1.18-3.06). In theft, male out-of-school adolescents were 1.92 times higher than female out-of-school adolescents (p=.006, 95% CI=1.21-3.03). When the social measures were provided for those adolescents, a distinguished approach is required depending on the problem behavior and gender.

• Fashion & Textile Research Journal
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• v.25 no.2
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• pp.241-247
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• 2023

This study sought to investigate the effect of academic satisfaction on the dropout intention of cosmetology undergraduates. Analyzing the effect of academic satisfaction on career dropouts showed that the sub-factors of academic satisfaction-evaluation satisfaction, class satisfaction had a statistically significant part effect. Analyzing the effect of academic satisfaction on psychological factors for dropping out showed that the sub-factors of academic satisfaction have a statistically significant effect. Furthermore, regarding the effect of academic satisfaction on environmental factors, the sub-factors of academic satisfaction have a statistically significant effect on wealth. High satisfaction was shown to have no statistically significant effect on dropout intention. The results of the study showed that the higher the degree of satisfaction with the evaluation and the degree of satisfaction with the course of beauty majors, the more negative (-) the impact on dropout. For cosmetology majors, academic satisfaction is a subjective emotion felt through study at university and major. Students with high academic satisfaction are more likely to love their school and their work, and positively influence their intention to stay in school and reduce student dropout rates. Intention to drop out indicates the intention to lose interest and purpose in cosmetology college students. This is directly linked to the dropout rate of school students and requires steady research. Through this research, we hope that active discussions will be held on academic satisfaction and intention to drop out of university students specializing in cosmetology.