• Korean Journal of Child Studies
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• v.33 no.6
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• pp.71-84
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• 2012

The purpose of this study is to analyze the relationship between decision making abilities young children and their self-directed learning abilities. A survey was carried out using 160 young children in the J region. The collected data were analyzed by Pearson correlation and multiple regression techniques using the SPSS statistics program. The conclusions are as follows : First, decision making abilities in young children exhibited a positive correlation with their self-directed learning abilities. Second, decision making abilities in young children were an influential variable in terms of their self-directed learning abilities. As a result, decision making abilities in young children were an important variable in predicting their self-directed learning abilities.

• Communications for Statistical Applications and Methods
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• v.29 no.2
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• pp.251-261
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• 2022

As AI has a wide range of influence on human social life, issues of transparency and ethics of AI are emerging. In particular, it is widely known that due to the existence of historical bias in data against ethics or regulatory frameworks for fairness, trained AI models based on such biased data could also impose bias or unfairness against a certain sensitive group (e.g., non-white, women). Demographic disparities due to AI, which refer to socially unacceptable bias that an AI model favors certain groups (e.g., white, men) over other groups (e.g., black, women), have been observed frequently in many applications of AI and many studies have been done recently to develop AI algorithms which remove or alleviate such demographic disparities in trained AI models. In this paper, we consider a problem of using the information in the sensitive variable for fair prediction when using the sensitive variable as a part of input variables is prohibitive by laws or regulations to avoid unfairness. As a way of reflecting the information in the sensitive variable to prediction, we consider a two-stage procedure. First, the sensitive variable is fully included in the learning phase to have a prediction model depending on the sensitive variable, and then an imputed sensitive variable is used in the prediction phase. The aim of this paper is to evaluate this procedure by analyzing several benchmark datasets. We illustrate that using an imputed sensitive variable is helpful to improve prediction accuracies without hampering the degree of fairness much.

• Journal of Digital Convergence
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• v.7 no.1
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• pp.137-147
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• 2009

The main purpose of cyber home education in public sector is to promote public education and restrain expensive private education expenses. The primary purpose of this study is to explore the effects of motivation, immersion and tutor on the effectiveness of cyber home learning. The variables of motivation, immersion and tutor participation were incorporated in this study as follows: 1) tutor participation was classed as a single independent variable, which has interaction and also effects passion; 2) motivation and 3) immersion were classed as two mediatory variables: motivation which include relevance and confidence; and immersion which includes attention and controllability. 4) learning effect was classed as a single dependent variable of learning influence factor which has learning attitude and learning satisfaction. The results show that a tutor had a direct influence on the effects of the learners' study but motivation and immersion had an indirect influence on the effects of learners' study independently. Based on these findings a tutor should provide motivation and immersion with various learning methods and contents for learners to get maximum benefits from cyber home learning.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.39 no.4
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• pp.462-469
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• 2002

While the conventional K-means algorithms use a fixed weight to design a vector codebook for all learning iterations, the proposed method employs a variable weight for learning iterations. The weight value of two or more beyond a convergent region is applied to obtain new codevectors at the initial learning iteration. The number of learning iteration applying a variable weight must be decreased for higher weight value at the initial learning iteration to design a better codebook. To enhance the splitting method that is used to generate an initial codebook, we propose a new method, which reduces the error between a representative vector and the member of training vectors. The method is that the representative vector with maximum squared error is rejected, but the vector with minimum error is splitting, and then we can obtain the better initial codevectors.

• Journal of the Korea Society of Computer and Information
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• v.15 no.4
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• pp.57-63
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• 2010

The damping parameter of Levenberg-Marquardt algorithm switches between error backpropagation and Gauss-Newton learning and affects learning speed. Fixing the damping parameter induces some oscillation of error and decreases learning speed. Therefore, we propose the way of a variable damping parameter with referring to the alternation of error. The proposed method makes the damping parameter increase if error rate is large and makes it decrease if error rate is small. This method so plays the role of momentum that it can improve learning speed. We tested both iris recognition and wine recognition for this paper. We found out that this method improved learning speed in 67% cases on iris recognition and in 78% cases on wine recognition. It was also showed that the oscillation of error by the proposed way was less than those of other algorithms.

• Communications for Statistical Applications and Methods
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• v.29 no.6
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• pp.629-640
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• 2022

Variable selection is one of the most crucial tasks in supervised learning, such as regression and classification. The best subset selection is straightforward and optimal but not practically applicable unless the number of predictors is small. In this article, we propose directly solving the best subset selection via the genetic algorithm (GA), a popular stochastic optimization algorithm based on the principle of Darwinian evolution. To further improve the variable selection performance, we propose to run multiple GA to solve the best subset selection and then synthesize the results, which we call ensemble GA (EGA). The EGA significantly improves variable selection performance. In addition, the proposed method is essentially the best subset selection and hence applicable to a variety of models with different selection criteria. We compare the proposed EGA to existing variable selection methods under various models, including linear regression, Poisson regression, and Cox regression for survival data. Both simulation and real data analysis demonstrate the promising performance of the proposed method.

• Journal of Educational Research in Mathematics
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• v.14 no.3
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• pp.305-325
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• 2004

This study is on the teaching-learning of parameter concept in secondary school mathematics. In our school mathematics curriculum, parameter concept is explicitly presented at high school mathematics textbook. But student have difficulty in understanding parameter concept because this concept is implicitly used in the textbook from 7-grade mathematics. Moreover, it is true that mathematics teacher give a little attention to student's understanding of parameter con- cept. In this study, we analyzed concept definition of parameter and the extension of parameter on the basis of preceding research, our mathematical curriculum, mathematical dictionaries. After that, we concluded that parameter is explicitly called in t where x= f(t), y= g(t) and parameter is implicitly treated in the learning of relation between quantities in our mathematical curriculum. We pointed to the importance of parameter concept in the successful learning of school algebra. Specially, when the level of algebra is in the learning of relation between quantities, parameter is the key concept for understanding and representing of families of equations or functions. In mathematics class, students have opportunity to reflect that what the role of each variable(parameter, dependent variable, independent variable etc.) is, and where the information which determines it comes from. It is for mathematical communications as well as learning school algebra. Therefore, mathematics teacher's didactical attention is more needed to student have a good concept image of parameter before they learn explicitly its concept definition.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.50 no.3
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• pp.107-113
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• 2001

This paper deals with speed control of DC servo motor using a PID controller with a gain tuning based on a Back-Propagation(BP) Learning Algorithm. Conventionally a PID controller has been used in the industrial control. But a PID controller should produce suitable parameters for each system. Also, variables of the PID controller should be changed according to environments, disturbances and loads. In this paper described by a experiment that contained a method using a PID controller with a gain tuning based on a Back-Propagation(BP) Learning Algorithm, we developed speed characteristics of a DC servo motor on variable loads. The parameters of the controller are determined by neural network performed on on-line system after training the neural network on off-line system.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.32B no.3
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• pp.503-511
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• 1995

A batch-mode algorithm is proposed to increase the speed of learning in the error backpropagation algorithm with variable learning rate and variable momentum parameters in classification problems. The objective function is normalized with respect to the number of patterns and output nodes. Also the gradient of the objective function is normalized in updating the connection weights to increase the effect of its backpropagated error. The learning rate and momentum parameters are determined from a function of the gradient norm and the number of weights. The learning rate depends on the square rott of the gradient norm while the momentum parameters depend on the gradient norm. In the two typical classification problems, simulation results demonstrate the effectiveness of the proposed algorithm.

• Educational Technology International
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• v.6 no.2
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• pp.43-58
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• 2005

The purpose of this study was to investigate the impact of organizational learning culture on job satisfaction and organizational commitment. Two streams of scholarly work have provided the theoretical foundations for this study. The first stream comes from the literature on learning organization. The second stream of the theoretical foundation comes from an extensive literature on attitude-intention-behavior relationships. In addition, this study was tested three alternative models. Alternative model 1 employed job satisfaction as the mediating commitments variable between learning culture and organizational commitment. Alternative model 2 used organizational commitment as the mediating variable between learning culture and job satisfaction. Finally, alternative model 3 specified a direct impact of learning culture on both job satisfaction and organizational commitment, and reciprocal linkages between these two variables. The results of this study support the hypothesized relations among an organization's learning culture, job satisfaction, and organizational commitment. The findings of this study are various congruent with a widely accepted hypothesis that job satisfaction serves as an appraisal function in evaluating various work environments and determining emotional responses such as organizational commitment. Organizational learning culture is one of the important factors that organizations cannot overlook. Therefore, the findings of this study provide a new direction for researchers seeking to explain the complex relations among these central organizational variables.