• Journal of Korea Society of Digital Industry and Information Management
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• v.14 no.3
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• pp.103-113
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• 2018

The purpose of this study supports the establishment of national e-learning policy by analyzing e-learning status and current status of higher education. Enhance the competitiveness of higher education through sharing information between universities. And to improve e-learning quality management. We surveyed the current status of e-learning in 341 universities and questionnaires about e-learning content, e-learning application form, e-learning platform status was surveyed through each school's learning management system. As a result, the infrastructure of e-learning, the rate of platforms secured, and the contents are increasing gradually each year; however, still, not all students can receive the services equally. Dedicated servers and learning management systems were secured by more than 70% of general universities. In the current development status of e-learning content, multimedia, animation, and text forms are gradually decreasing, but video contents are increasing every year. Most of the online contents were used in the e-learning contents by application type, and blended learning, flipped learning, and mooc is not yet actively used since they are still in the beginning stage. Learning analysis techniques should be supported in order to easily use online learning contents such as flipped learning and mooc. We suggest that the effectiveness of e-learning should be measured and the current state of learning analysis for customized learning should be done. This study aims to contribute to the improvement of competitiveness of higher education by sharing information about e-learning among universities as a basis for improvement of e-learning policy. Future tasks are to improve the customized learning environment by adding whether the system environment for learning analysis is provided at the time of the survey.

• Journal of the Semiconductor & Display Technology
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• v.21 no.2
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• pp.90-94
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• 2022

In order to minimize the picking time when the products are released from the warehouse, they should be located close to the exit when the products are released. Currently, the warehouse determines the loading location based on the order of the requirement of products, that is, the frequency of arrival and departure. Items with lower requirement ranks are loaded away from the exit, and items with higher requirement ranks are loaded closer from the exit. This is a case in which the delivery time is faster than the products located near the exit, even if the products are loaded far from the exit due to the low requirement ranking. In this case, there is a problem in that the transit time increases when the product is released. In order to solve the problem, we use the idle time of the stocker in the warehouse to rearrange the products according to the order of delivery time. Temporal difference learning method using Q_learning control, which is one of reinforcement learning types, was used when relocating items. The results of rearranging the products using the reinforcement learning method were compared and analyzed with the results of the existing method.

• Journal of Korea Multimedia Society
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• v.10 no.12
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• pp.1698-1703
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• 2007

The paper focuses on a new 'learning ecology' that is evolving and the challenges that educators must confront. It looks at e-learning as not just another add-on, but a technology that is transforming our educational institutions. How teaching and learning is conceptualized and experienced to generate a determined community of inquiry that integrates social, cognitive, and teaching presence in a manner that will take full advantage of the distinctive assets of e-learning is discussed. Likewise, the possibility of mobile learning is put forward.

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

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

• Journal of Korean Clinical Health Science
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• v.11 no.1
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• pp.1607-1615
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• 2023

purpose: In addition to evaluating the quality of sleep of college students, the effect on non-face-to-face online learning satisfaction is identified and used as basic data for improving the quality of remote lectures. Methods: From June 1 to June 24, 2022, a self-entry survey was conducted on students enrolled in the dental hygiene department of D University in Daegu. To evaluate the non-face-to-face online learning satisfaction and sleep quality of the study subjects using the lBM SPSS Statistics 21 program, ANOVA analysis was conducted on the difference between individual stress levels and non-face-to-face online learning satisfaction. The correlation between sleep quality, stress, and non-face-to-face online learning satisfaction was analyzed using Pearson's correlation coefficient. Results: The lower the quality of sleep, the higher the stress, resulting in statistically significant results (p<0.001). The higher the quality of sleep, the higher the learning satisfaction, resulting in statistically significant results (p<0.001). There was a statistically significant positive correlation between learning satisfaction and stress (r=0.591, p<0.01). Conciussions: Through the above results, in order to improve the satisfaction of non-face-to-face online learning, it is necessary to manage the individual's learning environment and health to relieve stress. Instructors also need to communicate with learners and apply teaching methods considering learners' academic abilities.

• Journal of Engineering Education Research
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• v.25 no.2
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• pp.22-31
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• 2022

This study was conducted to investigate the effect of learning style and lecture attitude on academic achievement in non-face-to-face lectures. Among students attending H University in the first semester of 2020. 1,880 students voluntarily participated in the survey. The questionnaire consists of 33 questions, and actual GPA was used to determine academic achievement. In this study, ANOVA and regression were utilized to find out the factors affecting academic achievement. The results are as follows. First, in the correlation between learning style types, the higher the avoidant learning style, the lower the independent, cooperative, and competitive types. Second, the higher the independent learning type, the higher the positive lecture attitude and GPA, and the higher the type of cooperative learning, the higher the negative lecture attitude. Third, the positive attitudes of 4th graders were higher than that of 1st graders. Fourth, there was no difference in learning style between colleges. Fifth, independent/competitive learning styles and non-face-to-face lecture attitudes had a significant effect on academic achievement in non-face-to-face lectures. In conclusion, in order to narrow the academic gap, instructors must identify the type of learning style. And it is necessary to consider students' preferences for non-face-to-face lectures.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.21 no.1
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• pp.251-268
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• 1996

In this paper, a higher-order feedforward neural network called ridge polynomial network (RPN) which shows good approximation capability for nonlnear continuous functions defined on compact subsets in multi-dimensional Euclidean spaces, is presented. This network provides more efficient and regular structure as compared to ordinary higher-order feedforward networks based on Gabor-Kolmogrov polynomial expansions, while maintating their fast learning property. the ridge polynomial network is a generalization of the pi-sigma network (PSN) and uses a specialform of ridge polynomials. It is shown that any multivariate polynomial can be exactly represented in this form, and thus realized by a RPN. The approximation capability of the RPNs for arbitrary continuous functions is shown by this representation theorem and the classical weierstrass polynomial approximation theorem. The RPN provides a natural mechanism for incremental function approximation based on learning algorithm of the PSN. Simulation results on several applications such as multivariate function approximation and pattern classification assert nonlinear approximation capability of the RPN.

• Journal of the Korea Society of Computer and Information
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• v.22 no.4
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• pp.107-113
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• 2017

The results showed that the male teachers' have higher expectations of the free learning semester compared to the female teachers'. Furthermore, the teachers who had experienced the free learning semester felt less job burdens than the teachers who did not. In addition, the subject teachers showed higher expectations of the free learning semester than the general teachers or the homeroom teachers. Therefore, in order to establish a stable system of free learning semester, it should be preceded by development of differentiated program of special education, free learning semester, strengthening of advance training, establishment of the system for participating in the whole school and the simplification of administration.

• Journal of Korean Public Health Nursing
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• v.15 no.2
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• pp.275-284
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• 2001

In order to assess the knowledge and learning needs. 72 patients with the first onset myocardial infarction were interviewed by the structured questionnaires during the period of September, 1999 to July, 2000. The results were as follows 1. As a whole. $57.9\%$ of patient had correct knowledge in relation to myocardial infarction. As for the knowledge score in terms of general characteristics, the patients who were in higher education and living with spouse were significantly higher point than those who were in lower education and living alone. 2. The mean scores of learning need of the subjects was 4.13 measured by Likert 5 point scale, No significant differences were shown in the score of learning need in terms of the general characteristics. As for the learning need according to domain, the subjects had higher leaning needs in the domain of diet, risk factors and activity and exercise. 3. There was no significant correlation between the learning need and the knowledge of myocardial infarction. The results of finding show that education program should be consider the general characteristics. the level of knowledge and learning needs of the myocardial infarction patients.