• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.8
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• pp.3466-3471
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• 2011

This study is to examine how media education is recognized and used in the field of early childhood education. For this, it analyzed awareness and problems of media education by kindergarten teachers and parents and set the following research questions to find out a new alternative of media education for children. First, what are differences in concerns and educational experiences on media education for children by teacher-parents? Second, what are differences in teaching-learning methods on media education for children recognized by teachers-parents? Third, the present study examined problems and effective improvement methods of media education for children with 250 teachers in the field of early childhood education and 250 parents and obtained the following conclusions. The teaching-learning method preferred most by teachers and parents was talking activity and as a result of asking the preferred type of group, it was found that teachers-parents answered small group activity was most ideal and what are to be improved in the media education for children included the extension of teachers' opportunities to have research training and non-establishment of the genral theory of media education in our whole society.

• Journal of Internet Computing and Services
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• v.19 no.2
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• pp.69-75
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• 2018

Over the years, the types of media and media usage patterns have rapidly changed and communication channels in society have diversified. The courses in the universities on "media" have accordingly been altered to adapt to these changes. To investigate the ways in which the higher learning institutions in Korea have adapted to the changes in the media environment, this study analyzed the curriculum provided by the communication/media departments in the areas of Seoul and Gyeonggi-do. For the curricula analysis,names of the course soffered at the selected universitie swere analyzed; then keywords were extracted from morphological analysis of thes enames. Also, to investigate the changes over time, the courses offered in the years 2008 and 2017 were selected. The network analysis was done by using Netminer; the shape, main components, and major nodes of the network were compared. The results showed that firstly, overall shape of network from 2008 and 2017 looked similar. Due to the existence of concentrations within the major, the overall shape of the network showed several independent components, rather than one network. However, the analysis revealed differences in major nodes in the 2008 case from 2017. In the 2008 case, 'programming,' 'media,' 'introduction' among others were the major nodes; in 2017, 'editing' was the most important node. This signifies that in 2017, the curricula in the selected universities emphasized more practical and technical media education. In other words, the universities have adapted to the changing environments by including new topics, paying more attention to video media, and providing students with more direct field experiences.

• International Journal of Internet, Broadcasting and Communication
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• v.5 no.1
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• pp.9-13
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• 2013

This paper presents the concepts of ubiquitous computing technology to construct a ubiquitous learning environment that enables learning to take place anywhere at any time. This ubiquitous learning environment is described as an environment that supports students' learning using digital media in geographically distributed environments. The u-learning model is a web-based e-learning system that could enable learners to acquire knowledge and skills through interaction between them and the ubiquitous learning environment. Students are allowed to be in an environment of their interest. The communication between devices and the embedded computers in the environment allows learner to learn while they are moving, hence, attaching them to their learning environment.

• Journal of the Korea Computer Industry Society
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• v.3 no.8
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• pp.1117-1128
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• 2002

This study suggests the most efficient teaching method by using multi-media. Based on the outcome of the engineering study, the multi-media and their contents will be applied to teaching methods, at first needing to provide concept and educational expecting effect of them. For multi-media using teaching methods, the study suggests the following 4; (1) teaching method for instructional learning, (2) teaching method for detective learning by guild, (3) teaching method for receptive loaming, (4) teaching method for exploration. Challenges still remained is to examine principles of teaching planning and relevant theories in order to apply the multi-media for the existing education, which should ask teachers in field to select more efficient teaching methods.

• Journal of Korea Multimedia Society
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• v.21 no.12
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• pp.1526-1533
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• 2018

This study aims to provide implications for mobile foreign language vocabulary learning app by analyzing the effects of mobile vocabulary learning app based on character animation. For this purpose, we applied the learning application designed with character animation and text, and the application designed with text only to two groups of learners, and analyzed the effect. As a result, we found that application designed with character animation and text was useful in recognition frequency and duration concerning learning. Regarding learning outcomes, we found that it is useful not only in memory but also in learning interest and motivation. This study provides implications for learning method and design development of mobile-based foreign language vocabulary learning application which actively using recently.

• Proceedings of the Korean Information Science Society Conference
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• 2007.10a
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• pp.211-212
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• 2007

• Journal of Digital Convergence
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• v.15 no.3
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• pp.55-64
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• 2017

This study identified the important categories and items about evaluation criteria of e-learning quality assurance by comparing evaluation criteria between Korea and France case. For deriving the conclusion, this research analyzed the Korea quality assurance case which is consist of success or failure for evaluation of quality assurance, and built the generic quality model of e-learning evaluation criteria. A generic model about evaluation criteria, categories, and item of e-learning quality assurance, which should be reflected on French quality criteria, were developed based on statistical approach. This research suggests a evaluation criteria which can be applied to African and Asian countries, that are related to AUF, as well as Korea. The result of this study can be applied to all organizations around the world which prepare for e-learning quality assurance, and at the same time it will be a valuable resource for companies or institutions which want to be evaluated e-learning quality assurance.

• Smart Media Journal
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• v.10 no.1
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• pp.55-62
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• 2021

We applied machine learning of semi-supervised learning, transfer learning, and federated learning as examples of AI use cases that can be applied to the three major industries(Automobile industry, Energy industry, and AI/Healthcare industry) of Gwangju Metro-city, and established an ML strategy for AI services for the major industries. Based on the ML strategy of AI service, practical approaches are suggested, the semi-supervised learning approach is used for automobile image recognition technology, and the transfer learning approach is used for diabetic retinopathy detection in the healthcare field. Finally, the case of the federated learning approach is to be used to predict electricity demand. These approaches were tested based on hardware such as single board computer Raspberry Pi, Jaetson Nano, and Intel i-7, and the validity of practical approaches was verified.

• Smart Media Journal
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• v.11 no.4
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• pp.30-37
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• 2022

Disease threatens plant growth and recognizing the type of disease is essential to making a remedy. In recent years, deep learning has witnessed a significant improvement for this task, however, a large volume of labeled images is one of the requirements to get decent performance. But annotated images are difficult and expensive to obtain in the agricultural field. Therefore, designing an efficient and effective strategy is one of the challenges in this area with few labeled data. Transfer learning, assuming taking knowledge from a source domain to a target domain, is borrowed to address this issue and observed comparable results. However, current transfer learning strategies can be regarded as a supervised method as it hypothesizes that there are many labeled images in a source domain. In contrast, unsupervised transfer learning, using only images in a source domain, gives more convenience as collecting images is much easier than annotating. In this paper, we leverage unsupervised transfer learning to perform plant disease recognition, by which we achieve a better performance than supervised transfer learning in many cases. Besides, a vision transformer with a bigger model capacity than convolution is utilized to have a better-pretrained feature space. With the vision transformer-based unsupervised transfer learning, we achieve better results than current works in two datasets. Especially, we obtain 97.3% accuracy with only 30 training images for each class in the Plant Village dataset. We hope that our work can encourage the community to pay attention to vision transformer-based unsupervised transfer learning in the agricultural field when with few labeled images.

• Smart Media Journal
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• v.12 no.11
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• pp.134-144
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• 2023

Nowadays, Deep Learning (DL) technology is being used in several government departments. South Korea imports a lot of seafood. If the demand for fishery products is not accurately predicted, then there will be a shortage of fishery products and the price of the fishery product may rise sharply. So, South Korea's Ministry of Ocean and Fisheries is attempting to accurately predict seafood imports using deep learning. This paper introduces the solution for the fish import prediction in South Korea using the Long Short-Term Memory (LSTM) method. It was found that there was a huge gap between the sum of consumption and export against the sum of production especially in the case of two species that are Hairtail and Pollock. An import prediction is suggested in this research to fill the gap with some advanced Deep Learning methods. This research focuses on import prediction using Machine Learning (ML) and Deep Learning methods to predict the import amount more precisely. For the prediction, two Deep Learning methods were chosen which are Artificial Neural Network (ANN) and Long Short-Term Memory (LSTM). Moreover, the Machine Learning method was also selected for the comparison between the DL and ML. Root Mean Square Error (RMSE) was selected for the error measurement which shows the difference between the predicted and actual values. The results obtained were compared with the average RMSE scores and in terms of percentage. It was found that the LSTM has the lowest RMSE score which showed the prediction with higher accuracy. Meanwhile, ML's RMSE score was higher which shows lower accuracy in prediction. Moreover, Google Trend Search data was used as a new feature to find its impact on prediction outcomes. It was found that it had a positive impact on results as the RMSE values were lowered, increasing the accuracy of the prediction.