• Information Systems Review
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• v.10 no.3
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• pp.39-64
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• 2008

As digital convergence evolves, it is expected that the market of IP-based services like VoIP and IPTV will be expanded. In particular, IPTV market is expected to attract consumers' attention through various interactive services offering a variety of experiences to consumers. Interactivity sets apart old media from new one in terms of how to mediate effects of user satisfaction. The object of this study is to investigate (1) multi-dimensional Interactivities in an interactive medium based on IP and relationship among them, and (2) significant factors affecting cognitive absorption of interactive media users. This study aims to provide implications on how to develop strategies for IP-based media including e-learning system.

• International Journal of Computer Science & Network Security
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• v.22 no.9
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• pp.334-342
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• 2022

Nowadays, as we can notice on social media, most users choose to use more than one language in their online postings. Thus, social media analytics needs reviewing as code-switching analytics instead of traditional analytics. This paper aims to present evidence comparable to the accuracy of code-switching analytics techniques in analysing the mood state of social media users. We conducted a systematic literature review (SLR) to study the social media analytics that examined the effectiveness of code-switching analytics techniques. One primary question and three sub-questions have been raised for this purpose. The study investigates the computational models used to detect and measures emotional well-being. The study primarily focuses on online postings text, including the extended text analysis, analysing and predicting using past experiences, and classifying the mood upon analysis. We used thirty-two (32) papers for our evidence synthesis and identified four main task classifications that can be used potentially in code-switching analytics. The tasks include determining analytics algorithms, classification techniques, mood classes, and analytics flow. Results showed that CNN-BiLSTM was the machine learning algorithm that affected code-switching analytics accuracy the most with 83.21%. In addition, the analytics accuracy when using the code-mixing emotion corpus could enhance by about 20% compared to when performing with one language. Our meta-analyses showed that code-mixing emotion corpus was effective in improving the mood analytics accuracy level. This SLR result has pointed to two apparent gaps in the research field: i) lack of studies that focus on Malay-English code-mixing analytics and ii) lack of studies investigating various mood classes via the code-mixing approach.

• Korean Journal of Child Studies
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• v.25 no.6
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• pp.69-87
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• 2004

The purpose of this study was to develop a media literacy program for young children and explore its applicability and effects on young children's media literacy learning. Media literacy, as a concept combined literacy, or the ability to read and write, with media, is about more than just consuming information or understanding technological aspects of media, but is defined as expanded information and communication skills that are responsive to the changing nature of information in human environment. In order to develop media literacy program for young children, the goal and objectives, content areas, teaching methods and materials, and evaluation of media literacy program were searched and established. The subjects of this study consisted of a total of 51 children at age 5-6. The research had been implemented for 8 weeks integrated into daily activities of kindergarten children. Data were collected by interviewing with children using animations, and children-made-cartoons during the pre- and post-tests, and were analyzed quantitatively using rating criteria. The results of this study showed that there were significant differences found in children's abilities to reception, critical thinking, and creativity. This research made a major contribution to provision of a ground for developing an effective media literacy program for young children.

• Journal of Engineering Education Research
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• v.15 no.4
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• pp.85-93
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• 2012

This paper is to explore the effects of Robot Based Instruction(RBI) on the learning attitude of elementary school students. According to this research, researcher found out that there is significant improvement in learning attitude score after RBI was applied. The result of verification on the learning attitude is difference by sex showed that male students' learning attitude score is more high better than another group. In particular, it showed that there is more significant improvement in science art discretionary activities subjects. The above-mentioned results are based on as follows two reasons. First, RBI is efficient to improve students' internal motivation and ownership about tasks, and that is related to environment of learning and instruction focused on authentic task and practice. Second, educational advantages of robot media was reflected appropriately in RBI, also appropriate instructional environment for RBI was supported.

• The Journal of the Korea Contents Association
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• v.7 no.4
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• pp.192-198
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• 2007

Due to the development of information telecommunication technology, e-learning industry is rapidly expanding its scope along with its production technology. The recent trend of e-learning program is likely converted from Wmv(Window Media Video) of Microsoft to Flv(Flash video), which has less capacity but better quality than other image file. It has successfully drawn the users attention since Flv can operate at most OS environments and browsers let alone with window and Lenux without extra players and codec setup. However, there is no accurate data on comparative analysis between Wmv and Flv regarding capacity, quality and production time. Therefore, the study shows the comparative data analysis on Wmv and Flv so as to set out production platform up to its idiosyncrasy.

• Smart Media Journal
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• v.5 no.3
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• pp.35-41
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• 2016

In this paper, we propose hand-gesture signal recognition based on EPS(Electronic Potential Sensor) using Deep learning model. Extracted signals which from Electronic field based sensor, EPS have much of the noise, so it must remove in pre-processing. After the noise are removed with filter using frequency feature, the signals are reconstructed with dimensional transformation to overcome limit which have just one-dimension feature with voltage value for using convolution operation. Then, the reconstructed signal data is finally classified and recognized using multiple learning layers model based on deep learning. Since the statistical model based on probability is sensitive to initial parameters, the result can change after training in modeling phase. Deep learning model can overcome this problem because of several layers in training phase. In experiment, we used two different deep learning structures, Convolutional neural networks and Recurrent Neural Network and compared with statistical model algorithm with four kinds of gestures. The recognition result of method using convolutional neural network is better than other algorithms in EPS gesture signal recognition.

• Smart Media Journal
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• v.8 no.1
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• pp.67-73
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• 2019

This study investigates Active Learning and Facilitation Convergence Education Program which can improve core competency to cope with vocational education in the fourth industrial revolution era. I applied the integrated advantages of Active Learning which enhances 'problem solving skill' and those of Facilitation for creative thinking idea to application design process coursework and verified the effectiveness of such education method through student satisfaction survey. I also designed application contents for the students who are familiar with the mobile environments and UI contents for data visualization which can help those students to improve their skills in software. Every coursework was conducted as a team project. As a result, Active Learning and Facilitation Convergence Education Program is found to be helpful in improving the basic skills and competencies required in college education. I hope this work helps to reduce the educational gap between industry and professional colleges.

• Smart Media Journal
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• v.11 no.2
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• pp.31-38
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• 2022

Promptly predicting changes in the salinity in rivers is an important task to predict the damage to agriculture and ecosystems caused by salinity infiltration and to establish disaster prevention measures. Because machine learning(ML) methods show much less computation cost than physics-based hydraulic models, they can predict the river salinity in a relatively short time. Due to shorter training time, ML methods have been studied as a complementary technique to physics-based hydraulic model. Many studies on salinity prediction based on machine learning have been studied actively around the world, but there are few studies in South Korea. With a massive number of datasets available publicly, we evaluated the performance of various kinds of machine learning techniques that predict the salinity of the Nakdong River Estuary Basin. As a result, LightGBM algorithm shows average 0.37 in RMSE as prediction performance and 2-20 times faster learning speed than other algorithms. This indicates that machine learning techniques can be applied to predict the salinity of rivers in Korea.

• Smart Media Journal
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• v.11 no.7
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• pp.61-69
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• 2022

In the learned machine learning, the performance of machine learning degrades at the same time as drift occurs in terms of learning models and learning data over time. As a solution to this problem, I would like to propose the concept and evaluation method of ML drift to determine the re-learning period of machine learning. An XAI test and an XAI test of an apple image were performed according to strawberry and clarity. In the case of strawberries, the change in the XAI analysis of ML models according to the clarity value was insignificant, and in the case of XAI of apple image, apples normally classified objects and heat map areas, but in the case of apple flowers and buds, the results were insignificant compared to strawberries and apples. This is expected to be caused by the lack of learning images of apple flowers and buds, and more apple flowers and buds will be studied and tested in the future.

• Smart Media Journal
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• v.13 no.5
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• pp.45-51
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• 2024

Recently, realistic contents such as virtual reality(VR) and augmented reality (AR) are widely used for education to provide beneficial learning environments with thee-dimensional(3D) information and interactive technology. Specially, AR technology will be helpful to intuitively understand by adding virtual objects registered in the real learning environment with effective ways. In this paper, we developed an AR learning system using 3D spatial information in the 2D based textbook for studying math related to geometry. In order to increase spatial learning effect, we applied to solid shapes such as prisms and pyramids in mathematics education process. Also, it allows participants to use various shapes and expression methods (e.g., wireframe mode) with interaction. We conducted the experiment with our AR system, evaluated achievement and interest. Our experimental study showed positive results, our results are expected to provide effective learning methods in various classes through realistic visualization and interaction methods.