• 한국정보시스템학회지:정보시스템연구
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• 제31권1호
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• pp.219-238
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• 2022

Purpose The purpose of this study is to investigate the structural relationships between interest, interaction level, presence, which are the characteristics of metaverse, learning immersion, and learning satisfaction, which are learning factors. Design/methodology/approach A questionnaire survey technique was used to achieve the purpose of the study. A questionnaire survey was conducted from November 22 to December 5, 2021, with students with experience in non-face-to-face classes using Gather Town and a total of 114 copies of the questionnaire excluding those with insincere answers were used for empirical analysis. SPSS Win ver.23.0 was used for basic statistical analysis, and AMOS 22.0 was used for the establishment and analysis of a structural equation model. Findings According to the study findings, interest and interaction levels had effects on learning immersion and learning presence, self-efficacy on learning presence, and learning immersion and learning presence on learning satisfaction. This study is meaningful in that it conducted an empirical study to find variables for improving learning immersion by conducting classes based on metaverse. Based on the findings of this study, it was found that interest and interaction, which are the biggest characteristics of metaverse, sustain learning participation and immersion and increase presence thereby enhancing learning satisfaction so that the possibilities of metaverse as a next generation education platform passing the limit of existing real time video platforms can be peeped.

• 한국IT서비스학회지
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• 제19권1호
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• pp.159-172
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• 2020

The purpose of this study is to examine the structural relationship among factors affecting learning engagement, satisfaction, continuance intention in China-MOOC. For this study, data were collected from 334 students who were taking MOOC classes. and structural equation model ling analyses were employed to examine the causal relationships among variables. From the results of this study, First, self-regulated learning and interaction significantly affected learning engagement. Second, interaction had direct effects on satisfaction. Third, satisfaction significantly affect continuance intention. Furthermore, satisfaction mediated relationships between interaction and continuance intention. These results imply that self-regulated learning, interaction, learning engagement should be considered for designing and implementing China-MOOC learning. Further implication are discussed in the study.

• 지식경영연구
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• 제10권1호
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• pp.17-32
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• 2009

Cyber University has been continuously increased since it is of great necessity of education through lifelong study. Recently, the management of cyber universities does not ensure education success, because some problems are coming out. Now we are to take an interest in qualitative level of e-learning. The purpose of this study is to classify and investigate interaction factors of e-learning, which were one of the restrictions to develop e-learning, influence learning flow and satisfaction. The authors discuss the implications of the findings for interaction and learning flow theory and practice.

• International Journal of Human Ecology
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• 제10권1호
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• pp.1-11
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• 2009

The effect of learning orientation on company innovation and innovation capability are explored based on survey data collected from 154 small and medium-sized manufacturing firms. The theoretical links between learning orientation and company innovation as well as innovation capability are investigated in four research models that compare textile and non-textile manufacturing firms. Learning orientation has a significant effect on company innovation and innovation capability in the model test. However, some of the three segmented factors (commitment to learning, shared vision, and open-mindedness) of learning orientation had no significant effect on company innovation and innovation capability. Company innovation and innovation capability of textile manufacturing firms are predicted by the commitment to learning and shared vision, whereas those of non-textile firms were determined by shared vision and open-mindedness. Differences show that firms may need to put weight on some distinctive aspects of learning orientation according to the business categories in order to enhance company innovation.

• 대한영상의학회지
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• 제81권6호
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• pp.1274-1289
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• 2020

최근, 의료 영상 분야에서 딥러닝은 가장 활발하게 연구되고 있는 기술 중 하나이다. 충분한 데이터와 최신의 딥러닝 알고리즘은 딥러닝 모델의 개발에 중요한 요소이다. 하지만 일반화된 최적의 딥러닝 모델을 개발하기 위해서는 데이터의 양과 최신의 딥러닝 알고리즘 외에도 많은 것을 고려해야 한다. 데이터 수집부터 가공, 전처리, 모델의 학습 및 검증, 경량화까지 모든 과정이 딥러닝 모델의 성능에 영향을 미칠 수 있기 때문이다. 본 종설에서는 의료 영상에 최적화된 딥러닝 모델을 위해 개발 과정 각각에서 고려해야 할 중요한 요소들을 살펴보고자 한다.

• 한국병원경영학회지
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• 제18권4호
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• pp.54-77
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• 2013

This paper is a comparison study about leadership training transfer factors between physicians working in large scale hospitals and managers working in firms. To fulfill this purpose, this study conducted a regression analysis on 101 managers and 59 physicians who had attended similar leadership training programs more than 16 hours recently in order to identify the differences on the learning transfer factors. 6 factors such as Learner readiness, Performance self-efficacy, (so far as Trainee Characteristics group), Organization Culture, Supervisor's tangible incentives and Supervisor's intangible support, (so far as Work environment group), Content Validity & Transfer Design (so far Training Design group) were used as independent variables while the personal Managerial Capability Increase and Leadership Capability Increase were used as dependent variables. And also we used 5 factors as control variables ; Job style (Manager or Physician), Age, Gender, Working years and Organization size. Here are the summary of major findings ; first, there were statistically significant differences between the learning transfer factors in leadership training programs for managers and those of physicians. Second, there were also statistically significant differences among trainees' working years and their organization size factors while age and gender do not affect the learning transfer factors. Third, for the physician's leadership training the practitioners should focus on two factors ; Organization Culture and Learner readiness.

• 한국정보시스템학회지:정보시스템연구
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• 제30권2호
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• pp.29-56
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• 2021

Purpose Cyber deviance of adolescents has become a serious social problem. With a widespread use of smartphones, incidents of cyber deviance have increased in Korea and both quantitative and qualitative damages such as suicide and depression are increasing. Research has been conducted to understand diverse factors that explain adolescents' delinquency in cyber space. However, most previous studies have focused on a single theory or perspective. Therefore, this study aims to comprehensively analyze motivations of juvenile cyber deviance and to develop a predictive model for delinquent adolescents by integrating four different theories on cyber deviance. Design/methodology/approach By using data from Korean Children & Youth Panel Survey 2010, this study extracts 27 potential factors for cyber deivance based on four background theories including general strain, social learning, social bonding, and routine activity theories. Then this study employs econometric analysis to empirically assess the impact of potential factors and utilizes a machine learning approach to predict the likelihood of cyber deviance by adolescents. Findings This study found that general strain factors as well as social learning factors have positive effects on cyber deviance. Routine activity-related factors such as real-life delinquent behaviors and online activities also positively influence the likelihood of cyber diviance. On the other hand, social bonding factors such as community commitment and attachment to community lessen the likelihood of cyber deviance while social factors related to school activities are found to have positive impacts on cyber deviance. This study also found a predictive model using a deep learning algorithm indicates the highest prediction performance. This study contributes to the prevention of cyber deviance of teenagers in practice by understanding motivations for adolescents' delinquency and predicting potential cyber deviants.

• 한국컴퓨터정보학회논문지
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• 제23권11호
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• pp.151-158
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• 2018

As the competition of 21 cyber universities in Korea has been on a continual increase, they are focusing on improving the quality of the e-learning education in cyber universities. In this study, we intended to derive the failure factors of the previous chatting system in the 2010s and the success factors from previous studies. Next, we identified priorities among five factors(Reliability, UI Convenience, Usability, Network effect, Operational policy) using AHP and the practical ways to implement the chatting service. We applied the chatting system to all the curriculums of S cyber university. Our study finds that the chat service affects the satisfaction of education. Finally, we propose the utilization plan to improve the e-learning education of cyber university through the findings of this research.

• International Journal of Internet, Broadcasting and Communication
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• 제15권4호
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• pp.90-99
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• 2023

This paper deals with studying strategy about impact of deep learning (DL) on the factor of Korean economic growth. To study classification of impact factors of Korean economic growth, we suggest dynamic equation of microeconomy and study methods on economic growth impact of deep learning. Next step is to suggest DL model to dynamic equation with Korean economy data with growth related factors to classify what factor is import and dominant factors to build policy and education. DL gives an influence in many areas because it can be implemented with ease as just normal editing works and speak including code development by using huge data. Currently, young generations will take a big impact on their job selection because generative AI can do well as much as humans can do it everywhere. Therefore, policy and education methods should be rearranged as new paradigm. However, government and officers do not understand well how it is serious in policy and education. This paper provides method of policy and education for AI education including generative AI through analysing many papers and reports, and experience.

• 한국항해항만학회:학술대회논문집
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• 한국항해항만학회 2022년도 추계학술대회
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• pp.383-384
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• 2022

Gas Combustion Unit (GCU) onboard liquefied natural gas carriers handles boil-off to stabilize tank pressure. There are many factors for LNG cargo operators to take into consideration to determine whether to use GCU or not. Gas consumption of main engine and re-liquefied gas through the Partial Re-Liquefaction System (PRS) are good examples of these factors. Human gas operators have decided the operation so far. In this paper, some deep learning neural network models were developed to provide human gas operators with a decision support system. The models consider various factors specially into GCU operation. A deep learning model with Sigmoid activation functions in input layer and hidden layers made the best performance among eight different deep learning models.