• 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 Data and Information Science Society
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• v.20 no.4
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• pp.685-694
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• 2009

The idea of this paper is to get the time series data from the number of student on the elementary, meddle and high-school for the forecasting of the numbers of student. Tow models, model A and model B, of time series data are obtained. The Holt-Winters additive methods are used for the forecasting of the numbers of student with the model A and model B until 2019 year. As the result, the abilities of forecasting on model A and B are better than those of the Korean education statistical system 2007.

• Journal of the Korean Data and Information Science Society
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• v.24 no.2
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• pp.311-320
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• 2013

The purpose of this paper is to suggest the methods of forecasting the numbers of students. The generalized weighted proportion estimation models are suggested and used for forecasting the numbers of student until 2029. The results of the Monte Carlo simulation show that the suggested method is powerful for the forecasting. In conclusion, the numbers of the third grade high-school students will be less than the numbers of college admission quota from 2019.

• The Journal of Korean Association of Computer Education
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• v.17 no.1
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• pp.65-73
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• 2014

The purpose of this study is to examine how time management, usefulness, and interest predict m-learning outcomes. For this study, 144 high school students participated in m-learning activities during science classes. After 5 week of classes, they responded the following surveys: time management, usefulness, interest, satisfaction, perceived achievement and learning persistence. Multiple regression analyses with correlation applied to this study as a data analysis method. The results showed that time management, usefulness, interest significantly predicted learning satisfaction and persistence. In addition, time management and usefulness significantly predicted perceived achievement, Therefore, these findings imply that time management, usefulness should be considered for designing m-learning activities in high school science class.

• 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 The Korean Association of Information Education
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• v.17 no.3
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• pp.305-316
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• 2013

Despite the potential implications of Facebook use, there is a distinct lack of empirically derived theory for designing learning environment. This may be because Facebook is a social tool and there has been limited opportunity for exploratory research regarding Facebook based learning. Therefore, the purpose of this study is to investigate learning presence factors affecting learning outcomes in Facebook-based collaborative learning. Forty two college students participated in the Facebook-based collaborative learning activity, and the data from thirty nine were used for step-wise multiple regression analysis. In addition focus group interview was conducted to examine learning presence of Facebook-based collaborative learning. The results reported that cognitive presence predicted significantly learning outcomes, however, social and emotional presence did not predict learning outcomes. The implication of this study and future research were discussed in this research.

• Proceedings of the Korea Information Processing Society Conference
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• 2018.05a
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• pp.244-246
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• 2018

학습관리시스템의 도입으로 학습자들은 다양한 형태로 학습하게 되고 데이터를 남기게 된다. 교육데이터마이닝은 다양한 형태로 기록되는 교육 데이터를 분석해서 유의미한 정보를 찾아 내는 방법이다. 교육데이터마이님을 활용하면 학생 개인의 학습성과 향상에 도움을 주거나 학습성과 예측 결과를 참고하여 부족한 부분을 지원해 줄 수도 있다. 기존 연구에서는 학습자의 행동 영역 특징이 학습성과에 영향을 끼친다는 것을 검증하기 위하여 나이브 베이즈, 의사결정트리, 신경망 기계학습알고리즘으로 데이터를 분석했다. 따라서 본 연구에서는 기존 연구를 확장하여 학습자의 행동 영역 특징이 학부모 학교 만족도에 영향을 끼치는지 여부를 확인하는 실험을 수행했으며 kNN, 의사결정트리, SVM 기계학습 알고리즘으로 데이터를 분석하였다. 분석결과 학습자의 행동 영역 특정이 학부모 학교 만족도에 영향을 미치는 것을 확인했다.

• The Journal of Bigdata
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• v.4 no.1
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• pp.73-81
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• 2019

With the increase of education data, there have been many studies on the application of various analytics to improve students' performance and educational environments over the past decade. This paper first introduces the cases of universities that successfully utilize the analysis results and, more specifically, examines which data and analytical techniques are used for each analysis purpose. Based on the findings, the limitations of the current analytics and the direction of future analysis are discussed.

• The Journal of the Korea Contents Association
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• v.15 no.11
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• pp.616-625
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• 2015

The purpose of the study is to structure the friendship network by the social network analysis and investigate the effects of social network centrality and learners' performances in college students. Both the in-degree centrality of 1st grade class study-network(t=2.722, P<.005) and the in-degree centrality of and $2^{nd}$ grade class study-network(t=2.708, P<.005)are predicted the individual student's learning performances. But there is no correlation between the in-degree centrality of $1^{st}$ and $2^{nd}$ grade class entertainment-network and the individual student's learning performances. Results of the study suggested the significant effect of social network analysis measures on learners' performance in the friendship networks. Based on the results, implication to the teaching strategy and future research direction were discussed.

• Journal of The Korean Association of Information Education
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• v.20 no.1
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• pp.57-68
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• 2016

The purpose of this study is to examine factors affecting flipped learning outcomes. For this study, 44 middle school students participated in flipped learning activities during science classes. After 5 week of classes, they responded the following surveys: intrinsic motivation, self-regulation, interest in class, interest in science, and learning achievement. Multiple regression analyses with correlation applied to this study as data analysis methods. The results showed that intrinsic motivation significantly predicted interest in class and interest in science. In addition, self-regulation significantly predicted learning achievement. Therefore, these findings imply that intrinsic motivation and self-regulation should be considered for designing flipped learning activities in middle school science classes.