• 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.

• 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 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%.

• 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.

• The Journal of the Korea Contents Association
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• v.10 no.3
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• pp.404-412
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• 2010

The purpose of this study is to suggest the basic data for lowering cyber university students' dropout rate and fostering continuous learning environment through understanding that cyber university student's private variance, an education institute variance and social variance have the impact on a student's determining dropout. For this, we selected students in A cyber university and carried out surveys for 500 students from April first to May 31st, 2009 using convenience sampling. We excluded answers whose results are considered to be insufficient or overlapped among answers of 336 students and used 304 answers in this study. We carried out logistics regression analysis using SPSS for Winow 15.0 for data analysis. First, it proved that individual interest variance affects the dropout. Second, it turned out that educational institute's environment variance has impact on the dropout. Third, it proved that social environment factor affects the dropout. Fourth, only individual variance among individual, an educational institute and social variance has meaningful impact on the dropout in terms of statistics.

• Journal of Korea Entertainment Industry Association
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• v.14 no.3
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• pp.105-115
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• 2020

Recently, due to a decrease in the school age population, it is expected that there will be great difficulties in recruiting students. The dropout rate for the last three years of four-year universities nationwide announced in www.academyinfo.go.kr has been continuously increasing at 4.1% (2016), 4.3% (2017), and 4.6% (2018). It has emerged as the biggest issue facing the university. In this study, through a large-scale empirical study at H University, an analysis of the dropout intention of college students and educational factors affecting their intentions were derived. First, as a result of analyzing the intention to drop out, the dropout intention of students in the engineering department was higher than in the humanities, and it was higher in the upper grades. Students from specialized high schools were higher than general high schools, and the students who raised the tuition fees were higher than those who were not. As a result of factor analysis on dropout intention, it was analyzed that class difficulty, major satisfaction, parent satisfaction, internationalization satisfaction, and college education performance satisfaction had a significant effect on dropout intention.

• 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.

• Journal of the Institute of Convergence Signal Processing
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• v.25 no.2
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• pp.119-126
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• 2024

This study aims to evaluate the feasibility of an early warning system for college dropout by machine learning the main patterns that affect college student dropout and to suggest ways to implement a system that can actively prevent it. For this purpose, a performance comparison experiment was conducted using five types of machine learning-based algorithms using data from the Korean Educational Longitudinal Study, 2005, conducted by the Korea Educational Development Institute. As a result of the experiment, the identification accuracy rate of students with the intention to drop out was up to 94.0% when using Random Forest, and the recall rate of students with the intention of dropping out was up to 77.0% when using Logistic Regression. It was measured. Lastly, based on the highest prediction model, we will provide counseling and management to students who are likely to drop out, and in particular, we will apply factors showing high importance by characteristic to the counseling method model. This study seeks to implement a model using IT technology to solve the career problems faced by college students, as dropout causes great costs to universities and individuals.

• Asia-Pacific Journal of Business
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• v.13 no.3
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• pp.361-373
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• 2022

Purpose - This paper analyzes the student determinants of college non-completion and estimates the effects of each determinant on college non-completion. Design/methodology/approach - We use student panel data from a large Korean university from 2011 to 2021. Our results are from estimation of fixed-effects logit model. Findings - The results show that grade point average, participation in extracurricular activities, the number of counseling sessions with teachers, and financial aid are the main determinants of college non-completion. Academic probation, which is defined as any person who has a cumulative grade point average below a one point seven five, increases the non-completion rate by 2.6 percentage points and an one-point rise in extracurricular activities index reduces the rate by 0.1 percentage points. The effects of each determinant are heterogeneous across student sub-groups which are separated by gender, nationality, and academic discipline. Research implications or Originality - Tailored support programs for academically discouraged students that incorporate student characteristics and backgrounds are necessary to increase college completion rates and degree attainment.

• Journal of Information Technology Applications and Management
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• v.28 no.6
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• pp.97-116
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• 2021

Due to the decrease in the school-age population and government regulations, universities have made great efforts to secure their own competitiveness. In particular, the selection of universities with financial support based on the recent evaluation of the Ministry of Education has become a major concern enough to affect the existence of the university itself. This paper extracts three-year data from 124 major private universities nationwide, and quantitatively analyzes the variables of major universities selected as self-improvement universities, competency reinforcement universities, and universities with limited financial support. As a result of estimating the selection of self-powered universities using the ordered logit model by hierarchically inputting 12 variables, student competitiveness in the metropolitan area (1.318^**), Educational Restitution Rate (4.078^***), University operation expenditure index rate (1.088^***) values were found. Significant positive coefficient values were found in the admission enrollment rate (45.98^***) and the enrollment rate (13.25^***). As a result of analyzing the marginal effects, the increase in the rate of reduction of education costs has always been positive in the selection of self-powered universities, but it was observed that the rate of increase decreases in areas of increase of 150% or more. On the contrary, the probability of becoming a Em-powered university was negative in all sectors, but on the contrary, it was analyzed that marginal effects increased at the same time point. On the other hand, the employment rate of graduates was not able to find direct significance with the result of the selection of Self powered universities. Through this paper, it is expected that each university will analyze the possibility and shortcomings of the selection of Self powered universities in policy making, and in particular, the risk of dropout of selection for the vulnerable field can be predicted using marginal effects. It can be used as major research data for both university evaluators, university officials and students.