• Journal of the Korean Data and Information Science Society
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• v.17 no.2
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• pp.345-353
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• 2006

Over the past few years, training professionals have become more pragmatic in their approach to technology-based media by using it to augment traditional forms of training delivery, such as classroom instruction and text-based materials. This trend has led to the rise of the term blended learning. Blended learning, an environment of e-learning, is a powerful learning solution created through a mixture of face-to-face and online learning delivered through a mix of media and superior learning experiences. In this article we design and implement an e-learning environment for blended learning. The environment focused on following factors: learning activity and participation of learners, and real time feedback of instructor.

• The KIPS Transactions:PartC
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• v.10C no.6
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• pp.705-710
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• 2003

Recently, most interchanges of information have been performed in the internet environments. So, the technuque, which is used as intrusion deleting tool for system protecting against attack, is very important. But, the skills of intrusion detection are newer and more delicate, we need preparations for defending from these attacks. Currently, lots of intrusion detection systemsmake the midel of intrusion detection rule using experienced data, based on this model they have the strategy of defence against attacks. This is not efficient for defense from new attack. In this paper, a new model of intrusion detection is proposed. This is hybrid statistical learning model using likelihood ratio test and statistical learning theory, then this model can detect a new attack as well as experienced attacks. This strategy performs intrusion detection according to make a model by finding abnomal attacks. Using KDD Cup-99 task data, we can know that the proposed model has a good result of intrusion detection.

• Communications for Statistical Applications and Methods
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• v.16 no.6
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• pp.979-987
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• 2009

In this study, we compare the assessments made by self, peer and instructor in a statistics course. The goal is to investigate the following two questions: (1) Is it reasonable or fair to expect students to be responsible for assessing the work of their colleagues and themselves? (2) What are students' opinions about the learning effect after they participate in the assessment process? As part of the study investigating these questions, we designed a prototype for a Web-based assessment tool and a procedure to apply the assessment techniques in a statistics course. In addition, we collected and analyzed the data produced in the assessment processes from students and the instructor. The analysis results are summarized as follows: First, self assessment was not accord with instructor assessment, but peer assessment was similar to the assessment by instructor. This result reflected that it is reasonable or fair to expect students to be responsible for assessing the work of their colleagues. Second, peer assessment of their colleagues successfully helped students increase their understanding of the course, and the students increased their skills in the actual assessment process by assessing the work of their colleagues. Finally, many students indicated a high interest level on the assessments.

• Journal of Fisheries and Marine Sciences Education
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• v.24 no.6
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• pp.935-949
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• 2012

The purpose of this study was to investigate the structural relationship among the self-efficacy, self-directed learning ability, school adjustment and learning flow in middle school students by the structural equation modeling analysis. The subjects of this study consisted of 553 middle school students. The data were analyzed with descriptive statistics, Pearson correlations and structural equation modeling analysis by using the SPSS 12.0 and AMOS 5.0 statistical program. The results of this study were as followed: First, there were significant correlations among the self-efficacy, self-directed learning ability, school adjustment and learning flow. Second, the self-directed learning ability and school adjustment directly affected the learning flow. Third, self-efficacy and school adjustment variables indirectly affected learning flow. The indices of the best fit model on these variable were adequate. This study shows that the self-efficacy, self-directed learning ability, school adjustment are the significant predictor for the learning flow during adolescent.

• International Journal of Contents
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• v.17 no.2
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• pp.41-47
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• 2021

The coronavirus pandemic has brought about dramatic changes in education, evidenced most clearly by the increase in e-learning. Thus, to identify how learners' attitudes toward e-learning are affected by diverse factors, this study examined the effects of perceived system quality and usefulness, the self-management of learning, and self-efficacy. A total of 236 college students participated in the survey. Multiple regression analysis was performed to test the study's proposed hypotheses. The study findings suggested that learners' attitudes toward e-learning are positively influenced by perceived e-learning usefulness, self-management of learning, and self-efficacy. However, the perceived system quality had no influence and no statistical significance.

• Journal of Aerospace System Engineering
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• v.14 no.2
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• pp.50-56
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• 2020

In this study, we investigated whether long short-term memory (LSTM) can be used in the future to predict F10.7 index data; the F10.7 index is a space environment factor affecting atomic oxygen erosion. Based on this, we compared the prediction performances of LSTM, the Autoregressive integrated moving average (ARIMA) model (which is a traditional statistical prediction model), and the similar pattern searching method used for long-term prediction. The LSTM model yielded superior results compared to the other techniques in the prediction period starting from the max/min points, but presented inferior results in the prediction period including the inflection points. It was found that efficient learning was not achieved, owing to the lack of currently available learning data in the prediction period including the maximum points. To overcome this, we proposed a method to increase the size of the learning samples using the sunspot data and to upgrade the LSTM model.

• Korean Journal of Radiology
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• v.22 no.10
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• pp.1697-1707
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• 2021

The recent introduction of various high-dimensional modeling methods, such as radiomics and deep learning, has created a much greater diversity in modeling approaches for survival prediction (or, more generally, time-to-event prediction). The newness of the recent modeling approaches and unfamiliarity with the model outputs may confuse some researchers and practitioners about the evaluation of the performance of such models. Methodological literacy to critically appraise the performance evaluation of the models and, ideally, the ability to conduct such an evaluation would be needed for those who want to develop models or apply them in practice. This article intends to provide intuitive, conceptual, and practical explanations of the statistical methods for evaluating the performance of survival prediction models with minimal usage of mathematical descriptions. It covers from conventional to deep learning methods, and emphasis has been placed on recent modeling approaches. This review article includes straightforward explanations of C indices (Harrell's C index, etc.), time-dependent receiver operating characteristic curve analysis, calibration plot, other methods for evaluating the calibration performance, and Brier score.

• Proceedings of the Korean Society for Quality Management Conference
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• 1998.11a
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• pp.696-704
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• 1998

Artificial neural networks (ANN) have successfully applied into various areas. But, How to effectively established network is the one of the critical problem. This study will focus on this problem and try to extensively study. Firstly, four different learning algorithms ANNs were constructed. The learning algorithms include backpropagation, simulated annealing, genetic algorithm, and tabu search. The experimental results of the above four different learning algorithms were tested by statistical analysis. The training RMS, training time, and testing RMS were used as the comparison criteria.

• Journal of the Korean Data and Information Science Society
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• v.24 no.5
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• pp.1077-1088
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• 2013

High-dimensional data analysis arises from almost all scientific areas, evolving with development of computing skills, and has encouraged penalized estimations that play important roles in statistical learning. For the past years, various penalized estimations have been developed, and the least absolute shrinkage and selection operator (LASSO) proposed by Tibshirani (1996) has shown outstanding ability, earning the first place on the development of penalized estimation. In this paper, we first introduce a number of recent advances in high-dimensional data analysis using the LASSO. The topics include various statistical problems such as variable selection and grouped or structured variable selection under sparse high-dimensional linear regression models. Several unsupervised learning methods including inverse covariance matrix estimation are presented. In addition, we address further studies on new applications which may establish a guideline on how to use the LASSO for statistical challenges of high-dimensional data analysis.

• Journal of the Korean Data and Information Science Society
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• v.19 no.2
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• pp.443-450
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• 2008

Peer assessment is the process of assessment of students by other students and one form of innovative assessment. It actively involves students in the assessment process and is generally agreed that such involvement enhances the quality and effectiveness of the learning process, since assessing something and benchmarking process is a powerful aid to mastering it themselves. It is more effective on the hard courses for them to understand. In this article we present a peer assessment technique which was applied to students enrolled in a mathematical statistics course and a historical course. In order to measure the effectiveness of the technique, students had to evaluate their colleagues based on predefined criteria and a comparison is presented between the instructor assessments and the peer assessment.