• Knowledge Management Research
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• v.12 no.2
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• pp.17-34
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• 2011

As organization's success depends on how much employees are active in recognizing new knowledge and transform into organization's value, this study is to examine the influence of employees' self-directed learning capacity on organizational absorptive capacity, organizational commitment and job involvement. The study has shown that managerial level employees have high level of self-directed learning capacity, and employees who have such high level capacity also shown to have high level of absorptive capacity and organizational affective commitment and job involvement. There were static correlations between all the variables, and absorptive capacity has a complete meditated effect on each variables. Hence, in order to enhance absorptive capacity, organizations should put their effort to develop employees' self-directed learning capacity, which will not only improve individual competency but also lead to organization's success in this competitive global market.

• International Journal of Computer Science & Network Security
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• v.24 no.3
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• pp.160-172
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• 2024

The characteristic aspects of learning a foreign language require special resources and tools for online learning. Criteria for choosing educational platforms depend on key elements of an academic subject area. Microsoft Teams (hereafter, MT) educational platform is competitive one because it meets most of the needs that arise during the formation of a secondary linguistic persona. Due to the large number of corporate programs, there are a successful acquisition of language skills and the implementation of all types of oral activities of students. A significant MT advantage is the constant analysis and monitoring of the platform of participants' needs in the educational process by developers. The article highlights MT advantages and disadvantages. The attention is drawn to individual programs, which, in the authors' opinion, are the most successful to learn writing, reading, speaking, listening, as well as organize classes that meet needs of modern foreign students.

• 제어로봇시스템학회:학술대회논문집
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• 1991.10a
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• pp.768-772
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• 1991

The Self Creating and organizing (SCO) is a new architecture and one of the unsupervized learning algorithm for the artificial neural network. SCO begins with only one output node which has a sufficiently wide response range, and the response ranges of all the nodes decrease with time. Self Creating and Organizing Neural Network (SCONN) decides automatically whether adapting the weights of existing node or creating a new node. It is compared to the Kohonen's Self Organizing Feature Map (SOFM). The results show that SCONN has lots of advantages over other competitive learning architecture.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.9 no.1
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• pp.36-41
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• 2009

Self organizing map(SOM) is one of the unsupervised methods based on the competitive learning. Many clustering works have been performed using SOM. It has offered the data visualization according to its result. The visualized result has been used for decision process of descriptive data mining as exploratory data analysis. In this paper we propose improvement of SOM using stratified sampling of statistics. The stratification leads to improve the performance of SOM. To verify improvement of our study, we make comparative experiments using the data sets form UCI machine learning repository and simulation data.

• Proceedings of the Korea Contents Association Conference
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• 2006.11a
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• pp.776-779
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• 2006

Today, It has developed actively e-learning according to web without time and place which increase of PC user with super-highway information network and various up-to-date information rapid growth. It has to provide the chance to student which will be able to practice and to think free and originative under liberal learning environment that learner can compose knowledge through various thinking by themselves. From the present paper, I would design e-learning system using TGT model of cooperative e-learning system that more effective studying through advantage of e-learning studying. I look forward to helping improvement of student's real ability as embody competitive games method by TGT model's basic theory of web-based.

• Journal of Korea Society of Digital Industry and Information Management
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• v.14 no.1
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• pp.103-120
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• 2018

Global conglomerates in the late 2010s are seeking changes from the existing method of financial growth into that of sustainable growth, as they are facing the age of 4th industrial revolution. In preparation for this change of the times and to stay competitive, small businesses are required to produce creative knowledge, and systematically share and store it by means of implementing voluntary and specific changes in their convictions and actions. This study aims to empirically examine how the ICT small businesses' organizational learning participants' self-leadership affect their job satisfaction through organization orientation and Leader-Member Exchange(LMX). The research sample consists of the first to third-year employees that have previously participated in the small businesses' organizational learning support project, and of learning leaders, group leaders, and group members from companies that have previously concluded the project. The results are as follows: First, the self-leadership at the ICT small businesses' organizational learning project has been shown to have a positive effect on job satisfaction. The same positive results are shown in learning orientation and LMX. Second, the learning orientation has been shown to have a positive effect on job satisfaction. Third, the LMX has been shown to have a positive effect on job satisfaction.

• Proceedings of the Korea Association of Information Systems Conference
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• 2004.11a
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• pp.431-443
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• 2004

Many e-Learning companies are incorporated for the last five years, but most of them are failed or merged by the other company. The main reasons are the absence of competitive strategies and recognition of critical success factors. There are many researches on the critical success factors of Information System (IS) and Electronic Commerce (EC) . We derived e-Learning success factors from the previous IS and EC researches. We classified the success factors into five dimensions, (1) contents management, (2) learner management, (3) business strategy, (4) organizational support and ability, (5) learning management system (LMS), and each dimension has 9 or more success factors measurement items. We surveyed the perceived importance of the success factors from the manager of South Korea e-Learning firms. The paper categorized the items into two or more factors for each dimension by the exploratory factor analysis. Finally, we conducted one-way ANOVA for each success factors by the business model. As a result, there is different importance level for each success factors by the business model. We concluded that each e-Learning company needs different strategies to their business model.

• International Journal of Internet, Broadcasting and Communication
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• v.13 no.1
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• pp.219-228
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• 2021

The aim of this study was to investigate the learning styles with personalities of medical students which may affect the efficiency of teaching-learning system of clinical education to determine the association with the clinical performance examination achievement of the students. The learning styles and personality traits of 147 students of medical college were investigated. The obtained data were analyzed by statistical analysis including independent t-test and correlation analysis. The results of the analyses are as follows: there was significant difference in the participation model in the different genders; of the personality traits, there was significant difference in self-transcendence in the different genders, whereas there was significant difference in the persistence for past failure experiences; and there was significant association between the 6 sub-learning models(Independent vs. Dependent, Collaborative vs. Competitive, and Participant vs. Avoidant learning styles) and the personality traits(Novelty Seeking, Harm Avoidance, Reward Dependence, Persistence, Self-directedness, Cooperativeness and Self-transcendence). In addition, the participant type of students had higher scholastic achievements in clinical performance, and the students who scored high in self-transcendence and persistence also had higher clinical performance. In conclusion, the student's learning style and personalities affected the clinical scholastic performance. We believe that considering this current study, it would be possible to improve the quality of clinical education of medical teaching as well as helping medical students to choose career paths that are suitable for their personalities.

• KEPCO Journal on Electric Power and Energy
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• v.7 no.2
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• pp.323-328
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• 2021

Diagnostic models are required. Data augmentation is one of the best ways to improve deep learning performance. Traditional augmentation techniques that modify image brightness or spatial information are difficult to achieve great results. To overcome this, a generative adversarial network (GAN) technology that generates virtual data to increase deep learning performance has emerged. GAN can create realistic-looking fake images by competitive learning two networks, a generator that creates fakes and a discriminator that determines whether images are real or fake made by the generator. GAN is being used in computer vision, IT solutions, and medical imaging fields. It is essential to secure additional learning data to advance deep learning-based fault diagnosis solutions in the power industry where facilities are strictly maintained more than other industries. In this paper, we propose a method for generating power facility images using GAN and a strategy for improving performance when only used a small amount of data. Finally, we analyze the performance of the augmented image to see if it could be utilized for the deep learning-based diagnosis system or not.

• International Journal of Computer Science & Network Security
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• v.23 no.8
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• pp.190-198
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• 2023

To enhance customer satisfaction for higher profits, an e-commerce sector can establish a continuous relationship and acquire new customers. Utilize machine-learning models to analyse their customer's behavioural evidence to produce their competitive advantage to the e-commerce platform by helping to improve overall satisfaction. These models will forecast customers who will churn and churn causes. Forecasts are used to build unique business strategies and services offers. This work is intended to develop a machine-learning model that can accurately forecast retainable customers of the entire e-commerce customer data. Developing predictive models classifying different imbalanced data effectively is a major challenge in collected data and machine learning algorithms. Build a machine learning model for solving class imbalance and forecast customers. The satisfaction accuracy is used for this research as evaluation metrics. This paper aims to enable to evaluate the use of different machine learning models utilized to forecast satisfaction. For this research paper are selected three analytical methods come from various classifications of learning. Classifier Selection, the efficiency of various classifiers like Random Forest, Logistic Regression, SVM, and Gradient Boosting Algorithm. Models have been used for a dataset of 8000 records of e-commerce websites and apps. Results indicate the best accuracy in determining satisfaction class with both gradient-boosting algorithm classifications. The results showed maximum accuracy compared to other algorithms, including Gradient Boosting Algorithm, Support Vector Machine Algorithm, Random Forest Algorithm, and logistic regression Algorithm. The best model developed for this paper to forecast satisfaction customers and accuracy achieve 88 %.