• Journal of Internet Computing and Services
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• v.20 no.5
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• pp.79-86
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• 2019

This paper is an overview of key elements of Korea National Open University's smart mobile learning system, and an attempt to evaluate its main services relative to the FRAME model and the Mobile Learning Development Model for distance learning in higher education. KNOU improved its system architecture to one based on xMOOC e-learning content delivery while also upgrading its PC-based online/mobile learning services to facilitate an easier and more convenient access to lectures and for better interactivity. From the users' viewpoint, the upgraded 'U-KNOU Campus' allows for a more integrated search capability coupled with better course recommendations and a customized notification service. Using the new system, the students can access not only the school- and peer-issued messages via online bulletin boards but also share information and pose questions to others including to the school faculty/officials and system administrators. Additionally, a new mobile payment method has been incorporated into the system so that the students can select and pay for additional courses from anywhere. In spite of these advances, the issue of device usability and content development remain; specifically U-KNOU Campus needs to improve its instructor-learner and learner-to-learner interactivity and mobile evaluation interface.

• Journal of Broadcast Engineering
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• v.26 no.1
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• pp.79-87
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• 2021

In this paper, we analysis the semi-supervised learning (SSL), which is adopted in order to train a deep learning-based classification model using the small number of labeled data. The conventional SSL techniques can be categorized into consistency regularization, entropy-based, and pseudo labeling. First, we describe the algorithm of each SSL technique. In the experimental results, we evaluate the classification accuracy of each SSL technique varying the number of labeled data. Finally, based on the experimental results, we describe the limitations of SSL technique, and suggest the research direction to improve the classification performance of SSL.

• 한국정보교육학회:학술대회논문집
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• 2021.08a
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• pp.175-179
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• 2021

Computer Science Unplugged activities are activities to learn about computer science through learning tools other than programming programs. Existing unplugged activities focus on the procedural thinking process and focus on guiding the thinking process through play. There is a lack of research on unsupervised learning, which plays an important role in machine learning, which has recently attracted attention. In this study, we designed and conducted an unplugged activities for unsupervised learning that analyzes data using video media familiar to elementary school students. The results on the effectiveness of the class were analyzed using the bebras challenge. As a result of analyzing the scores of the pre-test and post-test, it was confirmed that the students' computational thinking and problem-solving ability improved.

• Journal of The Korean Association of Information Education
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• v.18 no.3
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• pp.423-432
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• 2014

With wide spread of smart devices, the use of children's smart devices were increased. We have performed an exploratory study about children's use of smart devices in the viewpoint of lives of children. The children was encouraged on the school centered learning by their parents. In this study, we wanted to explore the state of life of children's using of smart devices in the view of the children's life at the age of information society. The results show that there was a difference in children's using of smart devices between big and small city. This research shows that the students have the lack of the experiences of learning of desired usages and learning with devices. In the play with the non-electronic media, outside play was more than indoor play. Half of the answers at the amount of time to play the game and to watch the TV were "within an 1 hour". We know that the proportion of children's indoor play portion is more than outdoor play of it.

• Korean Journal of Child Studies
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• v.37 no.4
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• pp.17-29
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• 2016

Objective: The purpose of this study was to examine the bidirectional relation between mobile phone dependency (MPD) and self-regulated learning (SRL) of early Korean adolescents in $4^{th}$, $6^{th}$ and $8^{th}$ grade, while taking into account gender differences. Methods: The study made use of panel data from the Korean Children and Youth Panel Study (KCYPS), and three waves of data collected from 2,264 adolescents were analyzed by means of autoregressive cross-lagged modeling. Results: The results can be summarized as follows. Firstly, MPD and SRL were consistently stable for adolescents in $4^{th}$, $6^{th}$ to $8^{th}$ grades. Secondly, a bidirectional relations between MPD and SRL were confirmed. In other words, there was a significant influence of a high level of MPD on a subsequent low level of SRL, and the high level of SRL also had a significant effect on the lower level of MPD across time. According to multi-group analysis, no gender differences were found in the relations between two constructs during the studied period. Conclusion: Findings highlighted not only the necessary media usage education but also parenting intervention strategies may help early adolescents to be prevented from negative effects of media usage and to enhance self-regulated learning ability. Based on the results, more implications were also discussed.

• Journal of Korean Library and Information Science Society
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• v.37 no.3
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• pp.173-191
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• 2006

Examinations and analysis were done to understand the digital reference services of the Teaching & Learning Aid Centers under 16 metropolitan city and provincial offices of education. The data used were collected from July 3. 2006 to July 15. 2006 by the direct contacts on their main homepages and recording the question and answer transcripts. It was proven that 13 of the 16 Teaching & Learning Aid Centers have done the reference services. In this study, several characteristics-whether the digital reference service was done or not, interface access level, its title, its communication tool, reference question number, reference question style, answer rate, etc-were analyzed. Tips for the future development were provided on the basis of the inadequacies and other findings revealed through this study.

• Journal of the Korean Geographical Society
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• v.42 no.5
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• pp.769-787
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• 2007

This study aims to uncover sources of environmental learning from a student perspective using the previously unstudied case of South Korea. literature from other countries credits many sources of learning, including: media, school, personal sources, and non-governmental organizations. This analysis is based on focus group and questionnaire data collected during in-country field work. Results from South Korea are then compared with other studies carried out in the Asia-Pacific and the Western developed world. The results show that, similar to other countries including Australia, China, and India; South Korean students learn about the environment mainly through the media and schools. Television, schools, and domestic internet web pages were found to be some of the most-used sources of environmental information in South Korea, while more personal sources, such as community, family, and friends, were found to play an overall lesser instructive role. When compared internationally, South Korean students often exhibited less trust in the reliability of various sources, especially business, community, and foreign sources of information.

• Smart Media Journal
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• v.9 no.3
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• pp.41-45
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• 2020

Recently, artificial intelligence (AI) and the Internet of things (IoT), which are represented by machine learning and deep learning among IT technologies related to the Fourth Industrial Revolution, are applied to our real life in various fields through various researches. In this paper, IoT and AI using object recognition technology are applied to classify clothing. For this purpose, the image dataset was taken using webcam and raspberry pi, and GoogLeNet, a convolutional neural network artificial intelligence network, was applied to transfer the photographed image data. The clothing image dataset was classified into two categories (shirtwaist, trousers): 900 clean images, 900 loss images, and total 1800 images. The classification measurement results showed that the accuracy of the clean clothing image was about 97.78%. In conclusion, the study confirmed the applicability of other objects using artificial intelligence networks on the Internet of Things based platform through the measurement results and the supplementation of more image data in the future.

• Smart Media Journal
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• v.9 no.1
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• pp.38-44
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• 2020

Due to the high dependence on electric energy, electric fires make up a significant portion of fires in Korea. Electric arcs by short circuits or poor contact cause three of four electrical fires. An electric arc is a discharge phenomenon of electrical current between the insulators, which instantaneously produces high temperature. In order to reduce the fire due to electric arc, this study aims to predict the electric arc risk. We collected arc data from the arc detectors and converted into graphs based on temporal arc data. We used machine learning for training converted graph with different number of temporal arc data. To measure the performance of the learning model, we use the test data. In the results, when the number of temporal arc data was 20, the prediction rate was high as 86%.

• Smart Media Journal
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• v.8 no.2
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• pp.66-73
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• 2019

Recently, object recognition technology using computer vision has attracted attention as a technology to replace sensor-based object recognition technology. It is often difficult to commercialize sensor-based object recognition technology because such approach requires an expensive sensor. On the other hand, object recognition technology using computer vision may replace sensors with inexpensive cameras. Moreover, Real-time recognition is viable due to the growth of CNN, which is actively introduced into other fields such as IoT and autonomous vehicles. Because object recognition model applications demand expert knowledge on deep learning to select and learn the model, such method, however, is challenging for non-experts to use it. Therefore, in this paper, we analyze the structure of deep - learning - based object recognition models, and propose a platform that can automatically select a deep - running object recognition model based on a user 's desired condition. We also present the reason we need to select statistics-based object recognition model through conducted experiments on different models.