• School Mathematics
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• v.19 no.3
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• pp.593-615
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• 2017

The purpose of this study is to develop a mobile-based math learning application and explore its application. In order to develop a learning application, the present study included literature review on math education involving mobile learning, investigation of literature related to mathematics education conducted in a digital environment, and method of use and implementation environment of existing math learning applications by type. Based on these preliminary investigation and analysis, an android version application, 'Mathematics Classroom for Middle School 3rd Graders' was developed. This application can be used for learning units such as Quadratic Functions and Graphs, Representative Value, and Variance and Standard Deviation. For the unit on Quadratic Functions and Graphs, the application was constructed so that students can draw various graphs by using the graphic mode and discuss their work with other students in the chatting room. For the unit on Representative Value, the application was constructed with the mathematical concept of representative value explained through animation along with activities of grouping data acquired after playing archery games by points or arranging them according to size so that students can study when and how to use median value, mode, and average. The application for Variance and Standard Deviation unit was also constructed in a way that allowed students to study the concept of variance and standard deviation and solve the problems on their own. The results of this study can be used as teaching & learning materials customized for individual student in math classes and will provide anyone the opportunity to engage in an interesting self-directed learning of math at anytime. Developed in the format of real life study, the application will contribute to helping students develop a positive attitude about math.

• Journal of Korean Academy of Nursing Administration
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• v.9 no.1
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• pp.75-88
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• 2003

Purpose : The Purposes of this study was to develop a learning organization measurement for nurses, and to test the validity and reliability of the measurement. Method : This study was conducted through 3 phases -theoretical framework choice, measurement items selection, and the testing of validity and reliability. In order to test reliability and validity of the measurement, data were collected from the 261 nurses, working in the 1 hospital with more 800 beds. The data obtained were analyzed by SPSS for Window program using percentages, Factor Analysis, Cronbach's alpha coefficients. Result : As a result of the study, 2 scales -Learning Organization Building Scale, and Knowledge Management Process Scale- were developed. Learning Organization Building Scale was consisted of 23 items, 5 factors. 5 factors explained 60.26% of the total variance, and the Cronbach's alpha of this scale was .8807. Knowledge Management Process Scale was consisted of 17 items, 4 factors. 4 factors explained 66.14% of the total variance, and the Cronbach's alpha of this scale was .9147. Conclusion : The Study supports the validity and reliability of the scales. Therefore, these scales can be effectively utilized for many researches about Learning organization of Nurse, and Nursing organization in the Hospital Setting.

• Nuclear Engineering and Technology
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• v.52 no.2
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• pp.287-295
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• 2020

Group method of data handling (GMDH) is considered one of the earliest deep learning methods. Deep learning gained additional interest in today's applications due to its capability to handle complex and high dimensional problems. In this study, multi-layer GMDH networks are used to perform uncertainty quantification (UQ) and sensitivity analysis (SA) of nuclear reactor simulations. GMDH is utilized as a surrogate/metamodel to replace high fidelity computer models with cheap-to-evaluate surrogate models, which facilitate UQ and SA tasks (e.g. variance decomposition, uncertainty propagation, etc.). GMDH performance is validated through two UQ applications in reactor simulations: (1) low dimensional input space (two-phase flow in a reactor channel), and (2) high dimensional space (8-group homogenized cross-sections). In both applications, GMDH networks show very good performance with small mean absolute and squared errors as well as high accuracy in capturing the target variance. GMDH is utilized afterward to perform UQ tasks such as variance decomposition through Sobol indices, and GMDH-based uncertainty propagation with large number of samples. GMDH performance is also compared to other surrogates including Gaussian processes and polynomial chaos expansions. The comparison shows that GMDH has competitive performance with the other methods for the low dimensional problem, and reliable performance for the high dimensional problem.

• Educational Technology International
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• v.6 no.2
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• pp.113-130
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• 2005

The purpose of this study was to determine the teaching styles of professors who teach adult students in selected higher institutions. It also identified whether professors' teaching styles were teacher-centered or learner-centered and examined the relationship between instructors' teaching styles and such instructor demographic variables as gender, years of teaching experience, and taught level of courses. This study used The Principles of Adult Learning Scale(PALS) (Conti,1983) to measure instructional preferences. Demographic characteristics were collected through a personal data inventory. The analysis of variance (ANOVA) and multivariate analysis of variance (MANOVA) tests were used to analyze the data. The data were examined for significance at the .05 level of confidence by means of analysis of variance. The dependent variables in this study were teaching styles of full-time professor, as represented by the seven subscores from the standardized instrument on the PALS. The seven subscores were: (1) learner-centered activities, (2) personalizing instruction, (3) relating to experience, (4) assessing student needs, (5) climate building, (6) participation in the learning process, and (7) flexibility for personal development. The study established that there was a significant difference in mean scores on the PALS between participants when examined by the number of years of teaching experiences.

• Korean Security Journal
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• no.39
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• pp.217-239
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• 2014

The purpose of this study is to compare with three juvenile delinquency theories on adolescents' status offenses including Hirschi's social bonding theory, Agnew's general strain theory, and Akers' social learning theory. The data derived from a sample of 2,337 middle school students taken from National Youth Policy Institute in 2011-2012. Multiple OLS regression analysis revealed that variables from social learning theory were strongly supported as an explanation for adolescents' status offenses, while variables from general strain theory were not supported. The social learning model explained 12.0% of the variance in adolescents' status offenses. However, general strain variables explained 2.6% of the variance in the dependant variable and 6.2% of the variance in adolescents' status offenses were explained by the social bonding variables. The present research made important contributions the further utilization of social learning in investigating many of the damaging forms of social deviance which exist in our society.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.3
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• pp.449-458
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• 2023

Many people think that sign language is only for people who are deaf and cannot speak, but of course it is necessary for people who want to talk with them. One of the biggest challenges in ASL(American Sign Language) alphabet recognition is the high inter-class similarities and high intra-class variance. In this paper, we proposed an architecture that can overcome these two problems, which performs similarity learning to reduces inter-class similarities and intra-class variance between images. The proposed architecture consists of the same convolutional neural network with a double configuration that shares parameters (weights and biases) and also applies the Keras API to reduce similarity learning and variance through this pathway. The similarity learning results the use of the dual CNN shows that the accuracy is improved by reducing the similarity and variability between classes by not including the poor results of the two classes.

• Journal of The Korean Association For Science Education
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• v.18 no.3
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• pp.327-336
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• 1998

The purposes of this study were to investigate 6th-graders' use of cognitive strategies and metacognitive strategies in usual learning and testing situations, and to compare the difference in the use of the strategies by students' science achievement, learning motivation, and gender. The relationship among these strategies, science achievement, and learning motivation were also examined, and the portion of variance of explanation for achievement score was studied by a multiple regression analysis. The results showed that high-achieving students used more cognitive strategies and metacognitive strategies in usual learning and more cognitive strategies in testing situations than low-achieving students. Highly motivated students used more cognitive and metacognitive strategies than poorly motivated students in all situations. Elementary female students used more learning strategies than male students in usual learning. On the other hand, no gender differences was found to be significant in the use of strategies in testing situations. These learning strategies were significantly correlated with the science achievement and motivation scores. The cognitive strategies in usual learning accounted for the significant portion of the variance of the achievement score. Educational implications are discussed.

• ETRI Journal
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• v.28 no.1
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• pp.59-66
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• 2006

Learning behaviors of a radial basis function network (RBFN) using a singular value decomposition (SVD) and stochastic gradient (SG) algorithm, together named RBF-SVD-SG, for odor sensing systems are analyzed, and a fast training method is proposed. RBF input data is from a conducting polymer sensor array. It is revealed in this paper that the SG algorithm for the fine-tuning of centers and widths still shows ill-behaving learning results when a sufficiently small convergence coefficient is not used. Since the tuning of centers in RBFN plays a dominant role in the performance of RBFN odor sensing systems, our analysis is focused on the center-gradient variance of the RBFN-SVD-SG algorithm. We found analytically that the steadystate weight fluctuation and large values of a convergence coefficient can lead to an increase in variance of the center-gradient estimate. Based on this analysis, we propose to use the least mean square algorithm instead of SVD in adjusting the weight for stable steady-state weight behavior. Experimental results of the proposed algorithm have shown faster learning speed and better classification performance.

• The Journal of the Convergence on Culture Technology
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• v.10 no.4
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• pp.575-580
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• 2024

Detecting the status of websites, normal or phishing, is necessary to defend against intelligent phishing attacks. We propose a machine learning-based classification to predict the status of websites. First, we collect information about 'URL', convert it into numerical data, and remove outliers. Second, we apply VIF(Variance Inflation Factors) to understand the correlation and independence between variables. Finally, we develop a phishing website detection model with machine learning-based classifications, which predicts website status. In the test datasets, Random Forest showed the best performance, with precision of 93.74%, recall of 92.26%, and accuracy of 93.14%. In the future, we expect to apply our model to detect various phishing crimes.

• Journal of Korean Academy of Fundamentals of Nursing
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• v.20 no.3
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• pp.239-247
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• 2013

Purpose: This study was done to investigate the relationship between metacognition, learning flow, and problem solving ability in simulation learning of nursing students and to identify the factors influencing problem solving ability. Methods: The study sample was 136 nursing students. Data were collected from September to November, 2012 using a structured questionnaire on metacognition, learning flow and problem solving ability. Descriptive statistics, Pearson correlation and stepwise multiple regression analysis were used with the SPSS win 20.0 program to analyze the data. Results: There were significant positive correlations between metacognition, learning flow and problem solving ability. Learning flow was a significant factor affecting problem solving ability. These variables accounted for 33% of variance. Conclusion: These results suggest that simulation learning has a positive effect on nursing students' learning outcomes.