• 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.

• Journal of Engineering Education Research
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• v.15 no.5
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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.

• Journal of Information Processing Systems
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• v.17 no.3
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• pp.630-644
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• 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 The Korean Society of Integrative Medicine
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• v.12 no.2
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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.

• 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.17 no.1
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• pp.109-115
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• 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.

• International Journal of Computer Science & Network Security
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• v.21 no.12spc
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• pp.539-543
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• 2021

Data mining technique was used to track student's performance during years studding in college and determine the impact of GPA_SEC on the GPA student rates according to the current academic advising method used on student's status. The study utilized a sample of 5436 individuals were drawn from two colleges in Majmaah University, KSA during 2013-2018 period. The results showed that the student's completion status in terms of graduation, dropout, Stumbling or dismissed was classified according to the average grades of admission from secondary school GPA_SEC. The results show the effect of the current academic advising that most of students gain less grades comparing with GPA_SEC in addition that the higher GPA_SEC was the higher graduation, dropout and dismissed decreased when GPA_SEC was high.. Therefore, the study recommends tracking students academically to evaluate their results of each semester to find out the causes of the deficiencies and addressing them within the 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 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 Information Technology Applications and Management
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• v.28 no.4
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• pp.21-32
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• 2021

In this study, factors affecting undergraduate students' dropout intentions consisted of aptitude and career fit of student's major, employement prospects of student's major, and graduation attitude were presented in the structural equation model and the moderating effect of academic year on the path coefficeint presented in the research model was verified. Undergraduate students are grouped into senior year (3rd and 4th grades) and junior year(1st and 2nd year) by the year in the university, As a result of analyzing 454 undergraduate students (105 in the junior group and 349 in the senior group), it was found that graduation attitude had a negative (-) effect on the dropout intention in both junior and senior group. The effect of employment prospects, and major aptitude and career firt on graduation attitude was not found in the junior group. However, in senior group, the effect of employment prospects, and major aptitude and career firt on graduation attitude was found to have a positive (+) effect, indicating that moderating effect of academic year does exist.