• International Journal of Contents
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• 제12권4호
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• pp.8-16
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• 2016

This research proposes the composition logic of an Active Learning Environment (ALE), to enable discovery by learning through experience, whilst increasing knowledge about modern architectural heritage. Linking information to the historical heritage using Information and Communication Technology (ICT) helps to overcome the limits of previous learning methods, by providing rich learning resources on site. Existing field trips of cultural heritages are created to impart limited experience content from web resources, or receive content at a specific place through humanities Geographic Information System (GIS). Therefore, on the basis of the blended space theory, an augmented space experience method for overcoming these shortages was composed. An ALE space framework is proposed to enable discovery through learning in an expanded space. The operation of ALE space is needed to create full coordination, such as a Content Management System (CMS). It involves a relation network to provide knowledge to the rule engine of the CMS. The application is represented with the Deoksugung Palace Seokjojeon hall example, by describing a user experience scenario.

• 경영과학
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• 제18권2호
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• pp.25-38
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• 2001

The well-known back-propagation algorithm for multi-layered neural network has successfully been applied to pattern c1assification problems with remarkable flexibility. Recently. the multi-layered neural network is used as a powerful data mining tool. Nevertheless, in many cases with complex boundary of classification, the successful learning is not guaranteed and the problems of long learning time and local minimum attraction restrict the field application. In this paper, an Improved learning procedure of multi-layered neural network is proposed. The procedure is based on the generalized delta rule but it is particular in the point that the architecture of network is not fixed but enlarged during learning. That is, the number of hidden nodes or hidden layers are increased to help finding the classification boundary and such procedure is controlled by entropy evaluation. The learning speed and the pattern classification performance are analyzed and compared with the back-propagation algorithm.

• 디지털산업정보학회논문지
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• 제9권4호
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• pp.177-188
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• 2013

Since the world entered the age called "Smart Revolution", there has also been a lot of changes in the field of education. In educational environment, there is a growing interest in smart learning based on mobile contents, with the development of Smart Devices and ubiquitous technology. This paper is about a research on what effects smart learning has on leaner side when learners make active use of mobile contents in this age of Smart Revolution. First, we embodied teaching plans for some practical classes in forms of mobile contents using M-bizmaker. After the learning process based on the embodied contents for students, we analyzed the survey results on 4 sections-their use of apps, screen composition, technical support, interactions. We also studied the results of a questionnaire on 4 sections-contents, information offering, feedback systems, learner assessment-to evaluate their satisfaction. The research suggests that learner satisfaction can be improved with smart learning based on mobile contents embodied for leaners.

• 디지털산업정보학회논문지
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• 제19권3호
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• pp.149-163
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• 2023

In this study we analyze research trends on machine learning healthcare based on papers from the United States, the United Kingdom, and Korea. In Elsevier's Scopus, we collected 3425 papers related to machine learning healthcare published from 2018 to 2022. Keyword frequency and centrality analysis were conducted using the abstracts of the collected papers. We identified keywords with high frequency of appearance by calculating keyword frequency and found central research keywords through the centrality analysis by country. Through the analysis results, research related to machine learning, deep learning, healthcare, and the covid virus was conducted as the most central and highly mediating research in each country. As the implication, studies related to electronic health information-based treatment, natural language processing, and privacy in Korea have lower degree centrality and betweenness centrality than those of the United States and the United Kingdom. Thus, various convergence research applied with machine learning is needed for these fields.

• International Journal of Internet, Broadcasting and Communication
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• 제16권2호
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• pp.161-168
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• 2024

As the size of big data models grows, distributed training is emerging as an essential element for large-scale machine learning tasks. In this paper, we propose ParamHub for distributed data training. During the training process, this agent utilizes the provided data to adjust various conditions of the model's parameters, such as the model structure, learning algorithm, hyperparameters, and bias, aiming to minimize the error between the model's predictions and the actual values. Furthermore, it operates autonomously, collecting and updating data in a distributed environment, thereby reducing the burden of load balancing that occurs in a centralized system. And Through communication between agents, resource management and learning processes can be coordinated, enabling efficient management of distributed data and resources. This approach enhances the scalability and stability of distributed machine learning systems while providing flexibility to be applied in various learning environments.

• 품질경영학회지
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• 제48권3호
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• pp.511-520
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• 2020

Purpose: The purpose of this study was to devise an accurate machine learning model for predicting silica concentrations following the addition of impurities, through time series analysis of mining data. Methods: The mining data were preprocessed and subjected to time series analysis using the machine learning model. Through correlation analysis, valid variables were selected and meaningless variables were excluded. To reflect changes over time, dependent variables at baseline were treated as independent variables at later time points. The relationship between independent variables and the dependent variable after n point was subjected to Pearson correlation analysis. Results: The correlation (R2) was strongest after 3 hours, which was adopted as a dependent variable. According to root mean square error (RMSE) data, the proposed method was superior to the other machine learning methods. The XGboost algorithm showed the best predictive performance. Conclusion: This study is important given the current lack of machine learning studies pertaining to the domestic mining industry. In addition, using time series analysis in mining data will show further improvement. Before establishing a predictive model for the proposed method, predictions should be made using data with time series characteristics. After doing this work, it should also improve prediction accuracy in other domains.

• 품질경영학회지
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• 제43권2호
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• pp.201-218
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• 2015

Purpose: This study surveyed the effects of instructors' verbal and nonverbal communication quality on students' learning flow and satisfaction. We divided the two types of communication into sub-factors - verbal communication into language, and nonverbal communication into kinesics, proxemics, paralanguage and physical appearance - to examine the causal relationship between learning flow and learning satisfaction. Methods: This study was conducted on the students (parents) of a paid early childhood education program run by "I" company located in Seoul, from November 12, 2014 to November 18, 2014. A total of 317 (90.5 %, effective) questionnaires were collected and analyzed using SPSS 18.0 and AMOS 18.0. Results: First, the verbal communication of the lecturers was found to have significantly positive (+) effects on learning satisfaction. Second, among the nonverbal communications, proxemics and physical appearance were found to have positive (+) effects on learning flow. Third, among the nonverbal communications, proxemics was found to have positive (+) effects on learning satisfaction. Fourth, the learning flow of students was found to have positive (+) effects on learning satisfaction. Conclusion: This study's findings can contribute to realizing desirable communication between instructors and students.

• 지식경영연구
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• 제13권3호
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• pp.105-125
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• 2012

Previous studies have focused on individual and organizational learning. Amid an increasingly complex business environment, a team system designed to improve flexibility and adaptability constitutes the most basic part of an organization. Still, team learning has rarely been discussed. In addition, team learning behavior, despite being an important part of a team process, is often mentioned as a team-level outcome variable. Given that team learning behavior involves constant changes in thinking and behavior, a shared belief among team members is needed in order to positively influence innovative performance of a team. In spite of that, there has been only limited discussion of it. Besides, few domestic studies have dealt with R&D teams that can clearly demonstrate team learning behavior and team innovative performance. This study is an empirical analysis of the impact of team efficacy on team innovative performance and the mediating role of team learning behavior based on materials collected from team leaders and their immediate subordinates in 268 R&D teams. The analysis showed that team learning behavior actually has a positive effect on team innovative performance. Team efficacy also turned out to have a positive influence on team learning behavior. Lastly, the study found that team learning behavior played a mediating role in the relationship between team efficacy and team innovative performance. Based on those results, the study has identified implications and suggested directions for future research.

• 경영정보학연구
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• 제6권2호
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• pp.161-179
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• 2004

본 연구는 중소형 e-Learning시스템의 개발을 위한 실질적 경험을 제시하는 데 목적이 있다. 제안된 시스템은 교수자로 하여금 전문적인 컴퓨팅 관련 기술의 도움없이 인터페이스를 정의하고, 컨텐츠를 조직화하며, 문제의 출제 및 자동 채점이 가능하도록 설계되었다. 본 연구는 e-Learning의 도입을 어렵게 느끼거나, 학교당국의 정책적 결정에 의해서 수용하겠다는 교육현장의 교수자가 타인의 도움없이 본인 스스로 구현할 수 있는 방안을 제시한다. 또한, 교수-학습자 간의 상호작용이 가능하고 개인적으로 운영할 수 있는 저비용, 고효율의 e-Learning기반 사이트를 실질적으로 구현하고 운영할 수 있는 방안을 제안한다.

• 공학교육연구
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• 제24권3호
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• pp.21-31
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• 2021

Flipped learning, which involves listening to lectures at home and performing dynamic group-based problem-solving activities in the classroom, is recently evaluated as a learner-centered teaching method, and interest and applications in engineering education are increasing. Therefore, this study aims to provide practical guidelines for successful application through empirical research analysis on the use of flipped learning in domestic engineering education. Through the selection criteria and keyword search, a systematic review of 36 articles was conducted. As a result of the analysis, flipped learning research in engineering education has increased sharply since 2016, focusing on academic journals and reporting its application cases and effects. Most of the research supported that flipped learning was effective not only for learners' learning activities(e.g., academic achievement, satisfaction, engagement, learning-flow, interaction), but also for individualized learning and securing sufficient practice time. It was often used in major classes with 15 to less than 50 students, especially in computer-related major courses. Most of them consisted of watching lecture videos, active learning activities, and lectures by instructors, and showed differences in management strategies for each class type. Based on the analysis results, suggestions for effective flipped learning management in future engineering education were presented.