• Journal of the Korean Society of Food Culture
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• v.24 no.4
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• pp.440-450
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• 2009

The purposes of study were to survey the status of nutrition education in school and investigate the perception of nutrition teacher candidates concerning the direction and ideal method for nutrition education. A questionnaire was distributed to 554 nutrition teacher candidates from August to October, 2006. A total of 468 usable data were collected (84.5% response rate). The statistical data analysis was completed by using SPSS for Windows (ver. 10.0) for descriptive analysis, ANOVA and $X^2$-test. About 52% of respondents had nutrition education teaching experience. Half of the respondents indicated that the necessity for nutrition education stemmed from their own need for such education. The main problem in students' dietary life was 'the increasing intake of processed foods, instant foods and fast foods (4.23 out of Likert 5 point scale)' and the major nutritional problem was 'high calorie intake with low essential nutrients (3.96 out of Likert 5 point scale)'. Over half the respondents (53.4%) recommended that nutrition education be oriented towards behavioral change rather than knowledge delivery. Social learning theory was preferred mostly as an theory apt to nutrition education (60.3%) and the most effective means of education was referred to organizing the regular class for nutrition education (50.5%). The 'playing such as songs or game' was reported as both effective and realizable method in nutrition education.

• Journal of the Korea Society of Computer and Information
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• v.27 no.8
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• pp.253-261
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• 2022

In this study, the educational effect were sought to be identified after developing and applying project learning for the artificial intelligence based liberal arts education for the non-computer majors. A paired-sample t-test was performed within each group to determine the extent of improvement in the learning flow and artificial intelligence efficacy in the experimental and control groups. After class, an independent sample t-test was performed to examine the statistical effects of pre-test and post-test on the learning flow and artificial intelligence efficacy in the experimental and control groups. The experimental group and control group demonstrated significant improvements in the learning flow and artificial intelligence efficacy before and after class, each respectively. There was no statistically significant difference in the learning flow between the experimental group for which the project learning method was applied and the control group for which only theory and practice were conducted in the artificial intelligence class. It was also confirmed that the experimental group for which the project learning method was applied improved the efficacy of artificial intelligence to a significant level compared to the control group which only proceeded with theory and practice.

• 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.

• The Korea Journal of Herbology
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• v.37 no.2
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• pp.13-21
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• 2022

Objectives : The evaluation of academic achievement is pivotal for establishing accurate direction and adequate level of medical education. The purpose of this study was to firstly establish innovative item analysis technique of Item Response Theory (IRT) for analyzing multiple-choice test of herbology in the traditional Korean medicine education which has not been available for the difficulty of test theory and statistical calculation. Methods : The answers of 390 students (2012-2018) to the 14 item herbology test in college of Korean medicine were used for the item analysis. As for the multidimensional analysis of item characteristics, difficulty, discrimination, and guessing parameters along with item-total correlation and percentage of correct answer were calculated using Classical Test Theory (CTT) and IRT. Results : The validity parameters of strong and weak items were illustrated in multiple perspectives. There were 4 items with six acceptable index scores, and 5 items with only one acceptable index score. The item discrimination of IRT was found to have no significant correlation with difficulty and discrimination indices of CTT emphasizing attention of professionals of medical education as for the test credibility. Conclusion : The critical suggestions for the development, utilization and revision of test items in the e-learning and evidence-based Teaching era were made based on the results of item analysis using IRT. The current study would firstly provide foundation for upgrading the quality of Korean medicine education using test theory.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2004.11a
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• pp.361-368
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• 2004

The purpose of the study was to explore the potential of the Social Network Analysis as an analytical tool for scientific investigation of learner-learner, or learner-tutor interaction within an Computer Supported Corporative Learning (CSCL) environment. Theoretical and methodological implication of the Social Network Analysis had been discussed. Following theoretical analysis, an exploratory empirical study was conducted to test statistical correlation between traditional performance measures such as achievement and team contribution index, and the centrality measure, one of the many quantitative measures the Social Network Analysis provides. Results indicate the centrality measure was correlated with the higher order learning performance and the peer-evaluated contribution indices. An interpretation of the results and their implication to instructional design theory and practices were provided along with some suggestions for future research.

• Smart Structures and Systems
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• v.29 no.1
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• pp.17-28
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• 2022

The cable component of cable-stayed bridges is gradually impacted by weather conditions, vehicle loads, and material corrosion. The stayed cable is a critical load-carrying part that closely affects the operational stability of a cable-stayed bridge. Damaged cables might lead to the bridge collapse due to their tension capacity reduction. Thus, it is necessary to develop structural health monitoring (SHM) techniques that accurately identify damaged cables. In this work, a combinational identification method of three efficient techniques, including statistical analysis, clustering, and neural network models, is proposed to detect the damaged cable in a cable-stayed bridge. The measured dataset from the bridge was initially preprocessed to remove the outlier channels. Then, the theory and application of each technique for damage detection were introduced. In general, the statistical approach extracts the parameters representing the damage within time series, and the clustering approach identifies the outliers from the data signals as damaged members, while the deep learning approach uses the nonlinear data dependencies in SHM for the training model. The performance of these approaches in classifying the damaged cable was assessed, and the combinational identification method was obtained using the voting ensemble. Finally, the combination method was compared with an existing outlier detection algorithm, support vector machines (SVM). The results demonstrate that the proposed method is robust and provides higher accuracy for the damaged cable detection in the cable-stayed bridge.

• PNF and Movement
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• v.12 no.2
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• pp.81-88
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• 2014

Purpose: The purpose of this study is to examine the needs of prior field learning and the academic achievement of field experience learning in a college. Methods: This study was performed from May 1 to October 30, and students were given questionnaire. The research questionnaire as follows: (1) to investigate the academic achievement after field experience learning, (2) to verify the needs of field experience learning. A statistical analysis was performed using SPSS 17.0 for window version. Results: The results was as follows : First, satisfaction of field learning had scored good(47.2%) in lesson goal, good(51.8%) in acquisition of knowledge and techniques, good(51.0%) in preparation of study and good(45.9%) in association. Second, curriculum of field learning had scored normal(35.5%) in prior education, good(47.4%) in composition, good(50.8%) in guidance and good(47.2%) in contents. Third, curriculum of field learning had scored good(44.6%) in duration, good(46.1%) in numbers, good(51.3%) in convenience and normal(38.1%) in means of transportation. Forth, needs of field learning had scored good(46.6%) in field learning of practicum, good(48.2%) in field learning of theory subject, 3-4 times(42.0%) in frequency of field learning and 2hours(57.3%) in a field learning hour. Conclusion: These findings suggest that college student's thinking of field experience learning is positive. Field experience learning provided that college students have directly an opportunity of gaining valuable experience to feel the field.

• Proceedings of the Korean Information Science Society Conference
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• 2005.11b
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• pp.820-822
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• 2005

학습자들의 효과적인 학습을 위하여 구성주의 학습이론을 바탕으로 웹 기반 코스웨어를 설계하였다. 기존의 학습과정인 교과서, 멀티미어 타이틀, 웹을 기반으로 한 코스웨어 등이 개발되어 교육현장에서 적용되면서 학습자에게 학습 흥미유발 및 교육성취도 등에서 효과가 있는 것으로 많은 연구자료에서 검증되었다. 하지만 기존의 코스웨어들은 하나의 과정으로 순차적이며 단계적인 학습이 이루어지는 단점이 있어 본 논문에서는 이러한 점을 보완하였다. 학습자들이 각자의 경험과 지식에 바탕을 두어 여러 유형의 학습안중에서 각자에 맞는 학습안을 선택하여 학습하도록 하였다. 즉, 기존의 웹 기반 코스웨어의 장점을 살리면서 좀더 학습자에 맞는 웹 기반 코스웨어를 개발하여 적용하였다. 이러한 결과 구성주의 학습이론을 적용한 코스웨어가 학습자 중심의 능동적인 학습을 통해 학습 성취도를 높여주는 것으로 나타났다.

• Journal of the Korean Data and Information Science Society
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• v.22 no.2
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• pp.197-206
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• 2011

The ability to detect online abnormal events in signals is essential in many real-world signal processing applications. In order to detect abnormal events, previously known algorithms require an explicit signal statistical model, and interpret abnormal events as statistical model abrupt changes. In general, maximum likelihood and Bayesian estimation theory to estimate well as detection methods have been used. However, the above-mentioned methods for robust and tractable model, it is not easy to estimate. More freedom to estimate how the model is needed. In this paper, we investigate a machine learning, descriptor-based approach that does not require a explicit descriptors statistical model, based on support vector machines are known to be robust statistical models and a sequential optimal algorithm online support vector machine is introduced.

• Computers and Concrete
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• v.9 no.3
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• pp.215-226
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• 2012

This article employs Support Vector Machine (SVM) for determination of fracture parameters critical stress intensity factor ($K^s_{Ic}$) and the critical crack tip opening displacement ($CTOD_c$) of concrete. SVM that is firmly based on the theory of statistical learning theory, uses regression technique by introducing ${\varepsilon}$-insensitive loss function has been adopted. The results are compared with a widely used Artificial Neural Network (ANN) model. Equations have been also developed for prediction of $K^s_{Ic}$ and $CTOD_c$. A sensitivity analysis has been also performed to investigate the importance of the input parameters. The results of this study show that the developed SVM is a robust model for determination of $K^s_{Ic}$ and $CTOD_c$ of concrete.