• 한국컴퓨터정보학회논문지
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• 제22권10호
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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.

• 디지털산업정보학회논문지
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• 제19권4호
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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%.

• 대한통합의학회지
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• 제12권2호
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• pp.155-166
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• 2024

Purpose : This study aims to identify key factors for predicting dropout risk at the university level and to provide a foundation for policy development aimed at dropout prevention. This study explores the optimal machine learning algorithm by comparing the performance of various algorithms using data on college students' dropout risks. Methods : We collected data on factors influencing dropout risk and propensity were collected from N University. The collected data were applied to several machine learning algorithms, including random forest, decision tree, artificial neural network, logistic regression, support vector machine (SVM), k-nearest neighbor (k-NN) classification, and Naive Bayes. The performance of these models was compared and evaluated, with a focus on predictive validity and the identification of significant dropout factors through the information gain index of machine learning. Results : The binary logistic regression analysis showed that the year of the program, department, grades, and year of entry had a statistically significant effect on the dropout risk. The performance of each machine learning algorithm showed that random forest performed the best. The results showed that the relative importance of the predictor variables was highest for department, age, grade, and residence, in the order of whether or not they matched the school location. Conclusion : Machine learning-based prediction of dropout risk focuses on the early identification of students at risk. The types and causes of dropout crises vary significantly among students. It is important to identify the types and causes of dropout crises so that appropriate actions and support can be taken to remove risk factors and increase protective factors. The relative importance of the factors affecting dropout risk found in this study will help guide educational prescriptions for preventing college student dropout.

• 공학교육연구
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• 제15권5호
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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.

• 수산해양교육연구
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• 제28권2호
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• pp.550-563
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• 2016

This study examined well-being as a mediator between self-discrepancy and dropout intention of junior college Student. 270 students in Busan answered the questionnaire about self-discrepancy, well-being, dropout intention. Structural equation modeling indicated that there were the complete mediating effect of well-being in the relationship between self-discrepancy and dropout intention. It indicated discrepancy between individuals' representation of their actual self(their actual self-state) and their representation of individual's hopes and aspirations(their ideal self-guide) affects junior college students' dropout intention, and well-being mediate process of self-discrepancy leading to dropout intention. This conclusion provide the significant implications that help preparing psychosocial intervention strategy for junior college students to decrease dropout intention and intervention strategy to enhance their well-being.

• 한국치위생학회지
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• 제22권2호
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• pp.143-151
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• 2022

Objectives: This study investigates dropout intention and the structural relationship between the dental hygienist role and satisfaction with the study major perceived by dental hygiene students. Methods: A survey was conducted on 269 dental hygiene students. The survey items covered general characteristics, department choice motivation, the desirability of dental hygienist career, practice clinical experience, perception of dental hygienist role, satisfaction with study major, and dropout intention. Independent sample t-test, one-way ANOVA, Mann-Whitney U test, multiple linear regression analysis, and structural equation modeling were used for statistical analysis. Results: The dropout intention level of dental hygiene students was 2.4 out of 5.0. Satisfaction with study major partially mediates perception of dental hygienist role and dropout intention (direct effect=0.182, p=0.024, indirect effect=-0.437, p=0.010). Perception of dental hygienist role (β=-0.255, p=0.010) and satisfaction with study major (β=-0.661, p=0.010) showed a negative relationship with dropout intention. The factor most affecting dropout intention was satisfaction with study major. Dropout intention was high when selecting a major based on external motivations (β=-0.448, p<0.001). Conclusions: Perception of dental hygienist role and satisfaction with study major directly or indirectly affect dropout intention. Therefore, improving satisfaction with study major and improving the perception of dental hygienists will help reduce dropout intention.

• 대한한의학회지
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• 제32권4호
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• pp.128-138
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• 2011

Objective: This study aimed at providing preliminary data useful in reducing participant dropout and improving the quality of clinical trials, by analyzing the factors related to dropout. Methods: The data came from 15 acupuncture and/or moxibustion clinical trials (n=638; August 2005 to December 2009). Logistic regression analysis was used to reveal factors influencing participant dropout. Results: Gender, age, treatment method (intervention), treatment frequency, availability of follow-up, and presence of compensation treatment for the control group were factors influencing participant dropout. Conclusion: Subsequent studies of large-scale acupuncture and moxibustion clinical trials should address dropout factors that consider the character of each clinical trial, or general characters like participants' gender, age, occupation, and diverse diseases.

• 한국컴퓨터정보학회논문지
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• 제23권5호
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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.

• 한국빅데이터학회지
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• 제4권2호
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• pp.175-183
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• 2019

학습자 중도탈락은 사이버대학이 해결해야 할 과제 중 하나이다. 2019년도 기준으로 사이버대학의 전체 학생 수는 13만여 명에 달하고 있으나, 중도탈락 비율도 매우 높은 편이다. 중도탈락율을 낮추기 위해 사이버대학은 학습 분석에 많은 투자를 하고 있다. 특히 일부 사이버대학에서는 중도탈락 가능성을 정량적으로 분석하여 중도탈락이 우려되는 학생에 대한 지원을 강화하고 있다. 본 논문의 목적은 중도탈락 예측지수에 영향을 미치는 학습데이터를 규명하는데 있다. 분석 결과, 수강 차시(진도율), 이수학점, 평점, 휴학 횟수가 중도탈락에 유의미한 영향을 미치는 것으로 확인되었다. 사이버대학은 학생 중도탈락 예측지수에 관한 사후검증을 통해 예측 모델의 정확도를 높여나가야 할 것이다.

• 대한한의학회지
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• 제37권3호
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• pp.62-73
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• 2016

Objectives: Dropout and weight regain are common problems in most obesity treatments. The purpose of this study was to review previously published study results of the predictive factors associated with dropout during weight loss treatment and weight loss maintenance after successful weight loss. Methods: Authors searched for the articles related to dropout and weight loss maintenance, published from 2007 to 2016 found on Pubmed, Scopus, RISS, and KISS. A total of 19 articles were finally selected. From the study results, unchangeable and changeable predictors were extracted, and these predictors were examined according to dropout and weight loss maintenance categories. Results: The unchangeable predictors of dropout were younger age, lower education level and female, whereas the changeable predictors of dropout were lower initial weight loss, symptoms of depression and body dissatisfaction. The strongest factor for predicting the dropout was initial weight loss. The unchangeable predictors of weight loss maintenance were old age, male and family history of obesity, whereas the changeable predictors of weight loss maintenance were regular exercise, dietary restraint, self-weighing and low depressive symptoms. Initial weight loss, depressive symptoms, body image, dietary restraint, physical activity, weight loss expectation and social support were considered to be dominant factors for weight loss treatments. Conclusions: Our review results suggest that unchangeable and changeable predictors of dropout and weight loss maintenance should be carefully examined during treatments of obesity.