• The Journal of the Korea Contents Association
• /
• v.13 no.8
• /
• pp.482-489
• /
• 2013

The purpose of this study was to identify the relationship of instructional design, perceived learning transfer, and satisfaction. The data were collected using questionnaire from the sample of 239 nursing students. The level of learning transfer was explained by introduction with learning context & providing guidance and initial attention. The level of learning transfer was explained by learning object with motivation, learning goal alignment, accessibility and feedback & adaptation. The level of program satisfaction was explained by introduction with learning context & providing guidance and initial attention. The level of program satisfaction was explained by learning object with motivation, presentation design, interaction availability, feedback & adaptation, learning goal alignment and contents quality. The findings serve as basic data to design e-Learning program to improve learning transfer and satisfaction.

• Journal of Korean Physical Therapy Science
• /
• v.13 no.3
• /
• pp.77-84
• /
• 2006

The purpose of this study is to introduce the effect of attentional focus on performance of task. Previous studies has shown that motor learning can be enhanced by directing performers's attention to the effects of their movements(external focus), rather than to the body movement producing the effects(internal focus). Wulf and colleagues have invoked the 'constrained action hypothesis' to explain the comparative benefits of adopting an external rather than an internal focus of attention. This hypothesis proposed that when performers utilize an internal focus of attention, they may actually constrain or interfere with automatic control processes that would normally regulate the movement, whereas an external focus of attention allows the motor system to more naturally self-organize. Electromyography(EMG) was used to determine neuromuscular correlates of external versus internal focus differences. EMG activity was lower with an external relative to an internal focus. This suggest that an external focus of attention enhances movement economy, and presumably reduces 'noise' in the motor system that hampers fine movement control. Focusing on a more remote effect seems to facilitate the discriminability of the effect from the body movements that produced it and to be more beneficial than focusing on a very close effects. There might be an optimal distance of the effect, at which ti wis easily distinguishable from the body movement but at which it is also still possible for the performer to relate this effect to the movement techniques. Future Studies of motor learning of patient need to accommodate these new finding and account for the role of the learner's attentional focus and its influencing on learning.

• IEMEK Journal of Embedded Systems and Applications
• /
• v.16 no.5
• /
• pp.225-232
• /
• 2021

Deraining is one of the image restoration tasks and should consider a tradeoff between local details and broad contextual information while recovering images. Current studies adopt an attention mechanism which has been actively researched in natural language processing to deal with both global and local features. This paper classifies existing deraining methods and provides comparative analysis and performance comparison by using several datasets in terms of generalization.

• 제어로봇시스템학회:학술대회논문집
• /
• 1996.10b
• /
• pp.944-947
• /
• 1996

Realization of autonomous agents that organize their own internal structure in order to behave adequately with respect to their goals and the world is the ultimate goal of AI and Robotics. Reinforcement learning gas recently been receiving increased attention as a method for robot learning with little or no a priori knowledge and higher capability of reactive and adaptive behaviors. In this paper, we present a method of reinforcement learning by which a multi robots learn to move to goal. The results of computer simulations are given.

• Proceedings of the Korean Operations and Management Science Society Conference
• /
• 2008.10a
• /
• pp.329-341
• /
• 2008

Knowledge management has garnered attention due to its role of maintaining competitive advantage. Creating and sharing knowledge is an essential part of managing knowledge. However, the best knowledge is underutilized because employees tend to seek knowledge through their informal networks, not reach out to other sources for obtaining the best knowledge. Prior studies on informal networks pointed out a negative influence of heavy reliance on learning through informal networks but they paid little attention to a structure of informal networks and its impacts on diffusion of knowledge. The aim of our study is to show impacts of informal network on knowledge management by employing a network structure and investigating diffusion of knowledge within it. Our study found out that performance of learning becomes lower in a highly clustered network. Creating random links such as serendipitous learning can improve performance of knowledge management. When employees rely on a knowledge management system, creating random links is not necessary. Costs of adopting knowledge affect performance of knowledge management.

• The Journal of Information Systems
• /
• v.28 no.4
• /
• pp.271-291
• /
• 2019

Purpose The purpose of this study is to propose a methodology to derive positive keywords and negative keywords through deep learning to classify reviews into positive reviews and negative ones, and then refine the results of topic modeling using these keywords. Design/methodology/approach In this study, we extracted topic keywords by performing LDA-based topic modeling. At the same time, we performed attention-based deep learning to identify positive and negative keywords. Finally, we refined the topic keywords using these keywords as filters. Findings We collected and analyzed about 6,000 English reviews of Gyeongbokgung, a representative tourist attraction in Korea, from Tripadvisor, a representative travel site. Experimental results show that the proposed methodology properly identifies positive and negative keywords describing major topics.

• Journal of the Korean Academy of Child and Adolescent Psychiatry
• /
• v.31 no.3
• /
• pp.97-104
• /
• 2020

Deep learning (DL) is a kind of machine learning technique that uses artificial intelligence to identify the characteristics of given data and efficiently analyze large amounts of information to perform tasks such as classification and prediction. In the field of neuroimaging of neurodevelopmental disorders, various biomarkers for diagnosis, classification, prognosis prediction, and treatment response prediction have been examined; however, they have not been efficiently combined to produce meaningful results. DL can be applied to overcome these limitations and produce clinically helpful results. Here, we review studies that combine neurodevelopmental disorder neuroimaging and DL techniques to explore the strengths, limitations, and future directions of this research area.

• Journal of Digital Contents Society
• /
• v.19 no.4
• /
• pp.731-737
• /
• 2018

The recommendation system is actively researched for the purpose of suggesting information that users may be interested in in many fields such as contents, online commerce, social network, advertisement system, and the like. However, there are many recommendation systems that propose based on past preference data, and it is difficult to provide users with little or no data in the past. Therefore, interest in higher-order data analysis is increasing and Matrix Factorization is attracting attention. In this paper, we study and propose a comparison and replay of the Factorization Machines Leaning(FM) model which is attracting attention in the recommendation system and High-Order Factorization Machines Learning(HOFM) which is a high - dimensional data analysis.

• Journal of Korean Physical Therapy Science
• /
• v.28 no.1
• /
• pp.62-75
• /
• 2021

Background: The world has entered the age of biotechnology and artificial intelligence, and encouraging students to test the value of information and knowledge ie to become information fluent, is becoming more important. The education system is also changing in order to adapt to the times. As a part of this, the cultivation of creative talent is a core goal of many nation states, and Israel's Jewish education methods are attracting attention; havruta (or chavrusa) is one such method. This study aims to effects of havruta class on learning attitudes and class satisfaction in a class of college physical therapy students. Design: Pretest-posttest design. Methods: The subjects were 95 students in College A. The learning attitudes questionnaire were used by the Korea Educational Development Institute, and the class satisfaction questionnaire before and after intervention. Results: The results showed significant differences in learning habits about physical therapy of learning attitudes (p<.05) and class methods and contents attention and understanding (p<.05), class interest of class satisfaction (p<.05). Conclusion: These results suggest that havruta class improves learning attitudes and class satisfaction. Therefore, follow-up study is needed to apply the havruta class in various students and teaching methods.

• Computers and Concrete
• /
• v.32 no.3
• /
• pp.263-286
• /
• 2023

Experimenting with concrete to determine its compressive and tensile strengths is a laborious and time-consuming operation that requires a lot of attention to detail. Researchers from all around the world have spent the better part of the last several decades attempting to use machine learning algorithms to make accurate predictions about the technical qualities of various kinds of concrete. The research that is currently available on estimating the strength of concrete draws attention to the applicability and precision of the various machine learning techniques. This article provides a summary of the research that has previously been conducted on estimating the strength of concrete by making use of a variety of different machine learning methods. In this work, a classification of the existing body of research literature is presented, with the classification being based on the machine learning technique used by the researchers. The present review work will open the horizon for the researchers working on the machine learning based prediction of the compressive strength of concrete by providing the recommendations and benefits and drawbacks associated with each model as determining the compressive strength of concrete practically is a laborious and time-consuming task.