• Journal of the Korean BIBLIA Society for library and Information Science
• /
• v.13 no.2
• /
• pp.265-282
• /
• 2002

The purpose of this study is to investigate the principles of school library media center program and to finds out present level and future outlook of the program implementation in primary and middle school. The fundamental objective of school is learning and school library functions as a link to support this objective. Therefore quality of education must always be linked to the library media programs. A questionaire which consists of 13 questions covering school library media center operation was designed to final out how learning-teaching media program is being practiced in Korea. Based on this study, it is concluded that there is significant difference between present practice level and desired future-oriented practice. It is hoped that this study will help planners in formulating school library policy to achieve educational goal of the school.

• Journal of Broadcast Engineering
• /
• v.21 no.5
• /
• pp.739-748
• /
• 2016

This study quantitatively measured the impact of blended learning with smart devices for learning satisfaction. It is targeted in specialized domestic company with IT Service which build smart learning systems and utilize for employee training. Specifically, it empirically analyzed that learning attitude(Self-efficacy, Self-innovativeness, Perceived usefulness, Perceived ease of use) with smart devices affect acceptance of smart learning and offline face-to-face learning satisfaction. As a result, the learning attitude of the smart learning gave a positive effect on the acceptance of the smart learning and then acceptance of the smart learning gave a positive effect on offline face-to-face learning satisfaction. Additionally learning the attitude of the smart learning even gave a positive impact, as well as the acceptance of smart learning experience in offline training. It imply that this variables of smart-learning attitude affect the self-directed learning and positive learning experience.

• Journal of Information Technology Services
• /
• v.22 no.4
• /
• pp.141-157
• /
• 2023

The present work aimed to present the development of 'Funt', the augmented reality-based digital app as an educational tool for foreign language learning. Our work further evaluated the learning efficacy of the tool by the assessment of the three dependent measures including learning motivation, learning satisfaction, and learning involvement. With a learning app of 'Funt', students can use AR app to access recognition-based or location-based experiences such that any objects, artifacts, or media appear to be in the app. Students are then able to interact with the digital content by manipulating it to learn more about it. Students's engagement should also increase when they create their own experience in AR to demonstrate their understanding of a particular concept or words. Learning effects were evaluated on survey data collected from a hundred respondents aging six to nine years. One-group design for pre-test and post-test was utilized to examine the differences of learning efficacy by comparing the non-'Funt' group and the Funt group scores. A pairwise t-Test was performed for pairwise comparisons between two learning groups. The results indicate that the 'Funt' group scored significantly higher than the non-'Funt' group in the measures of learning motivation, learning satisfaction, and learning involvement. Overall, our results suggest that 'Funt' attracted the students' attention, provided them with a fun context to learn English vocabulary, and develop positive motivation and satisfaction towards vocabulary learning through AR technology.

• Journal of the Korean Association of Oral and Maxillofacial Surgeons
• /
• v.48 no.4
• /
• pp.189-191
• /
• 2022

• Journal of The Korean Association of Information Education
• /
• v.19 no.3
• /
• pp.373-384
• /
• 2015

Computer education is gradually focusing on a wide variety of areas of expertise such as production of visual media and software development, instead of on the use of ICT like before. Particularly, computer courses related to production of image media require a complex practice procedure and repeated practice, making the future hands-on work difficult. Therefore, this study tried to verify the effect of the change in running methods of the "computer graphic" course from offline to blended learning. This analyzed the students' lecture satisfaction survey results of before and after the change. As a result, blended learning was well received and led to the small variations in the scores of the satisfaction surveys between the lecturers. Additionally, many students responded that the blended learning was more effective in their satisfaction surveys.

• Journal of Internet Computing and Services
• /
• v.20 no.3
• /
• pp.33-41
• /
• 2019

In this paper, we propose a method to learn the redesigned class with background and object for semantic segmentation of indoor scene image. Semantic image segmentation is a technique that divides meaningful parts of an image, such as walls and beds, into pixels. Previous work of semantic image segmentation has proposed methods of learning various object classes of images through neural networks, and it has been pointed out that there is insufficient accuracy compared to long learning time. However, in the problem of separating objects and backgrounds, there is no need to learn various object classes. So we concentrate on separating objects and backgrounds, and propose method to learn after class simplification. The accuracy of the proposed learning method is about 5 ~ 12% higher than the existing methods. In addition, the learning time is reduced by about 14 ~ 60 minutes when the class is configured differently In the same environment, and it shows that it is possible to efficiently learn about the problem of separating the object and the background.

• Smart Media Journal
• /
• v.11 no.11
• /
• pp.92-98
• /
• 2022

Identifying defects in textiles is a key procedure for quality control. This study attempted to create a model that detects defects by analyzing the images of the fabrics. The models used in the study were deep learning-based VGGNet and ResNet, and the defect detection performance of the two models was compared and evaluated. The accuracy of the VGGNet and the ResNet model was 0.859 and 0.893, respectively, which showed the higher accuracy of the ResNet. In addition, the region of attention of the model was derived by using the Grad-CAM algorithm, an eXplainable Artificial Intelligence (XAI) technique, to find out the location of the region that the deep learning model recognized as a defect in the fabric image. As a result, it was confirmed that the region recognized by the deep learning model as a defect in the fabric was actually defective even with the naked eyes. The results of this study are expected to reduce the time and cost incurred in the fabric production process by utilizing deep learning-based artificial intelligence in the defect detection of the textile industry.

• Smart Media Journal
• /
• v.12 no.1
• /
• pp.47-53
• /
• 2023

Recently, research on a smart farm that creates new values by combining information and communication technology(ICT) with agriculture has been actively done. In order for domestic smart farm technology to have productivity at the same level of advanced agricultural countries, automated decision-making using machine learning is necessary. However, current smart greenhouse data collection technologies in our country are not enough to perform big data analysis or machine learning. In this paper, we design and implement a smart greenhouse data preprocessing system for autonomous machine learning. The proposed system applies target data to various preprocessing techniques. And the proposed system evaluate the performance of each preprocessing technique and store optimal preprocessing technique for each data. Stored optimal preprocessing techniques are used to perform preprocessing on newly collected data

• Journal of Smart Tourism
• /
• v.4 no.2
• /
• pp.5-14
• /
• 2024

This study explores the transformative potential of machine learning (ML) and ML-driven data analytics in the hospitality industry. It provides a comprehensive overview of this emerging method, from explaining ML's origins to introducing the evolution of ML-driven data analytics in the hospitality industry. The present study emphasizes the shift embodied in ML, moving from explicit programming towards a self-learning, adaptive approach refined over time through big data. Meanwhile, social media analytics has progressed from simplistic metrics deriving nuanced qualitative insights into consumer behavior as an industry-specific example. Additionally, this study explores innovative applications of these innovative technologies in the hospitality sector, whether in demand forecasting, personalized marketing, predictive maintenance, etc. The study also emphasizes the integration of ML and social media analytics, discussing the implications like enhanced customer personalization, real-time decision-making capabilities, optimized marketing campaigns, and improved fraud detection. In conclusion, ML-driven hospitality data analytics have become indispensable in the strategic and operation machinery of contemporary hospitality businesses. It projects these technologies' continued significance in propelling data-centric advancements across the industry.

• Journal of Service Research and Studies
• /
• v.11 no.4
• /
• pp.67-76
• /
• 2021

The importance of smart learning is increasing as the speed of development of non-face-to-face services increases due to the influence of COVID-19. This study is the user satisfaction evaluation model that utilizes the causal relationship between variables used for evaluation, focusing on the usability evaluation results of the learning disability intervention service (www.basic-edu.net) according to the need to evaluate the use satisfaction of the smart learning system. To this end, theoretical studies were conducted on smart learning and learning disability intervention services, www.basic-edu.net, usability evaluation of learning disability intervention systems, and use satisfaction evaluation models. And based on the results, a hypothesis was presented on the user satisfaction evaluation model of the smart learning system. The experimental method allowed 40 students and parents across the country to use the www.basic-edu.net service and was evaluated for its usability. In addition, using this data, the hypothesis was verified using regression analysis based on four variables: ease of use, interest, self-learning, and satisfaction with use. As a result of the hypothesis verification, it was found that the causal relationship of all hypotheses from H1 to H4 was significant.