• Nutrition Research and Practice
• /
• v.8 no.1
• /
• pp.94-102
• /
• 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.

• International Journal of Advanced Culture Technology
• /
• v.5 no.2
• /
• pp.74-81
• /
• 2017

Recently, there are many companies that use systems based on artificial intelligence. The accuracy of artificial intelligence depends on the amount of learning data and the appropriate algorithm. However, it is not easy to obtain learning data with a large number of entity. Less data set have large generalization errors due to overfitting. In order to minimize this generalization error, this study proposed DGA(Dropout Genetic Algorithm) which can expect relatively high accuracy even though data with a less data set is applied to machine learning based genetic algorithm to deep learning based dropout. The idea of this paper is to determine the active state of the nodes. Using Gradient about loss function, A new fitness function is defined. Proposed Algorithm DGA is supplementing stochastic inconsistency about Dropout. Also DGA solved problem by the complexity of the fitness function and expression range of the model about Genetic Algorithm As a result of experiments using MNIST data proposed algorithm accuracy is 75.3%. Using only Dropout algorithm accuracy is 41.4%. It is shown that DGA is better than using only dropout.

• Journal of the Korea Convergence Society
• /
• v.11 no.7
• /
• pp.125-136
• /
• 2020

The purpose of this study is to understand school dropout experiences of multiculrual adolescents and to suggest policy of promoting their school adjustment. In-depth interviews are conducted with four multicultural adolescents who have dropped out from school and the interviews were analyzed using the phenomenological analysis method. The current study finds that school dropout experiences of multicultual adolescents could be classified in seven themes and those themes are divided again into 19 sub-themes. The seven main themes are as follows: 'the family in crisis', 'maladjustment in school life', 'discrimination and conflict', 'school-violence victimization', 'school dropout crisis', 'nonoperating the mandatory delay before school dropout program', and 'school dropout'. Based on these results, we would like to provide basic information to prevent multicultural youth's academic suspension.

• Journal of Korea Society of Digital Industry and Information Management
• /
• v.17 no.1
• /
• pp.109-115
• /
• 2021

The number of freshmen at universities is decreasing due to the recent decline in the school-age population, and the survival of many universities is threatened. To overcome this situation, universities are seeking ways to use big data within the school to improve the quality of education. A study on the prediction of dropout students is a representative case of using big data in universities. The dropout prediction can prepare a systematic management plan by identifying students who will drop out of school due to reasons such as dropout or expulsion. In the case of actual on-campus data, a large number of missing values are included because it is collected and managed by various departments. For this reason, it is necessary to construct a model by effectively reflecting the missing values. In this study, we propose a university student dropout prediction model based on eXtreme Gradient Boost that can be applied to data with many missing values and shows high performance. In order to examine the practical applicability of the proposed model, an experiment was performed using data from C University in Chungbuk. As a result of the experiment, the prediction performance of the proposed model was found to be excellent. The management strategy of dropout students can be established through the prediction results of the model proposed in this paper.

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

• Korean Journal of Social Welfare
• /
• v.53
• /
• pp.75-104
• /
• 2003

This study was performed to analyze the process and develop the pattern of school dropout in the context of the Korean educational systems. Participants were 30 adolescents experiencing dropout one more times and their age ranged from 16 to 23. A major category derived and analysed from a grounded theory method of Strauss and Corbin(1990). The qualitative analysis indicated that dropout is contradictory experiences in Korea. Because it is emancipation from various distress of school systems as well as loss of various benefit as students, Main causes of dropout were identity problems, school frustrations(failure in school achievements), environmental disadvantaged and delinquencies. Sometimes these various causes are emerged at the same time. Most important condition of context which was decided was whether dropout was voluntary and considered. After dropout from school, it was important if there were resources such as emotional and informative supports. Especially informative support was critical to adjust after dropout. Dropout experience was divided largely into 4 patterns such as type of 'searching the identity', 'being disorganized', 'escaping' and 'frustration'. In chronological analysis resulted in 4 stages including 'stage I. loss of motivation', 'stage II. escaping from school', 'stage III. trial and error', 'stage IV changing the meaning of dropout in the life.

• The Journal of the Korea Contents Association
• /
• v.10 no.3
• /
• pp.404-412
• /
• 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 Information Processing Systems
• /
• v.17 no.3
• /
• pp.630-644
• /
• 2021

Educational data analysis is attracting increasing attention with the rise of the big data industry. The amounts and types of learning data available are increasing steadily, and the information technology required to analyze these data continues to develop. The early identification of potential dropout students is very important; education is important in terms of social movement and social achievement. Here, we analyze educational data and generate predictive models for student dropout using logistic regression, a decision tree, a naïve Bayes method, and a multilayer perceptron. The multilayer perceptron model using independent variables selected via the variance analysis showed better performance than the other models. In addition, we experimentally found that not only grades but also extracurricular activities were important in terms of preventing student dropout.

• Journal of Technologic Dentistry
• /
• v.42 no.4
• /
• pp.362-371
• /
• 2020

Purpose: The following study seeks to ascertain the motivations behind students' academic major selection and to identify the obstacles they encounter in the transition to university life, with the objective of providing information necessary to adapt well to the university and the course. Thereby, we aim to supply basic resources needed in the development of a university adaptation program to prevent academic dropout. Methods: Between October 1, 2019 and November 29, 2019, a self-administered questionnaire was distributed to a study sample consisting of students currently attending dental technology courses in Gangwondo and Gyeonggido. A total of 474 (94.8%) responses to the questionnaire were received and used for the final analysis. Results: Factors including major selection motivation, intrinsic motivation (p<0.001), academic adjustment (p<0.001), social adjustment (p<0.01), and institutional adjustment (p<0.05) all had negative relationships with academic dropout intention. Personal-emotional adjustment (p<0.001), however, showed a positive relationship with dropout intention. The explanatory power of the model was found to be 50.0%. Conclusion: This research shows that intrinsic motivation and personal-emotional adjustment diminish academic dropout intention. Therefore, it is recommended that diverse postenrolment course-adjustment programs should be developed to improve students' confidence in their choice of study, their adjustment to the course, and their level of satisfaction.

• Nuclear Engineering and Technology
• /
• v.53 no.12
• /
• pp.4014-4021
• /
• 2021

If safety injection systems (SISs) do not work in the event of a loss-of-coolant accident (LOCA), the accident can progress to a severe accident in which the reactor core is exposed and the reactor vessel fails. Therefore, it is considered that a technology that provides recoverable maximum time for SIS actuation is necessary to prevent this progression. In this study, the corresponding time was defined as the golden time. To achieve the objective of accurately predicting the golden time, the prediction was performed using the deep fuzzy neural network (DFNN) with rule-dropout. The DFNN with rule-dropout has an architecture in which many of the fuzzy neural networks (FNNs) are connected and is a method in which the fuzzy rule numbers, which are directly related to the number of nodes in the FNN that affect inference performance, are properly adjusted by a genetic algorithm. The golden time prediction performance of the DFNN model with rule-dropout was better than that of the support vector regression model. By using the prediction result through the proposed DFNN with rule-dropout, it is expected to prevent the aggravation of the accidents by providing the maximum remaining time for SIS recovery, which failed in the LOCA situation.