• Nuclear Engineering and Technology
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• v.55 no.6
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• pp.2026-2033
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• 2023

Due to Thailand's nuclear energy public acceptance problem, the understanding of nuclear energy public perception was the key factor affecting to re-consideration of the nuclear energy program. Thailand Institute of Nuclear Technology and its alliances together developed the classification model for the nuclear energy public perception from the big data comments on social media using Facebook using deep transfer learning. The objective was to insight into the Thailand nuclear energy public perception on Facebook social media platform using sentiment analysis. The supervised learning was used to generate up-to-date classification model with more than 80% accuracy to classify the public perception on nuclear power plant news on Facebook from 2009 to 2022. The majority of neutral sentiments (80%) represented the opportunity for Thailand to convince people to receive a better nuclear perception. Negative sentiments (14%) showed support for other alternative energies due to nuclear accident concerns while positive sentiments (6%) expressed support for innovative nuclear technologies.

• International Journal of Computer Science & Network Security
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• v.24 no.10
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• pp.25-31
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• 2024

Currently, on social media, malicious comments have emerged as a serious issue. Existing artificial intelligence-based comment classification systems have limitations due to data bias and overfitting. To address this, this study proposed a novel comment classification system that combines crowdsourcing and federated learning. This system collects data from various users and utilizes a large language model like KoBERT through federated learning to classify comments accurately while protecting user privacy. It is expected to provide higher accuracy than existing methods and improve significantly the efficiency of detecting malicious comments. The proposed system can be applied to social media platforms and online communities.

• Journal of the Korea Society of Computer and Information
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• v.16 no.12
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• pp.297-304
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• 2011

This study was to analyze the learning effectiveness of multi-media based class by comparing with traditional classroom method. The "Distribution Working Subject" course that is one of the required courses of Vocational high school was selected and its contents were digitalized on MS Powerpoint for multi-media based class. The thirty students were sampled for each experimental and control groups. The homogeneity and learning achievement of sample groups were tested for experiment. Same teacher took the classes of two groups and delivered same contents of course. Only difference between two groups was the delivery method, one is traditional classroom teaching method and the other was the multi-media based class. The learning achievements and satisfaction of sample were post-tested in order to analyze the learning effectiveness by comparing two teaching methods. The results showed that there was a significant difference between experimental and control group in learning achievement after ANCOVA controlled pre-test as covariance(F=5.08, p<.05). It means that the learning achievement of multi-media based class was higher than that of traditional classroom group. The results also showed that a significant difference in students' satisfaction between two groups (t=5.57, p<.001). This study concluded that using multi-media in class could produce more learning achievements and satisfaction of students than traditional classroom method.

• Journal of Korean Home Economics Education Association
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• v.32 no.4
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• pp.1-18
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• 2020

The purpose of this study is to improve media literacy through home economics education. To this end, in this study, developed were 10 teaching-learning plans, learning activity sheets, and teaching materials for the 'relationship' area, the core concept of the 2015 revised home economics curriculum, using the ADDIE method. Pre- and post-survey results after implementing the developed program showed statistically significant improvements in enhancing participants' ability to access media and critical understanding of media. Through this, also found was that the ability to express and produce one's own thoughts and feelings has improved. As a result of a qualitative analysis, it was found that students who participated in the class experienced an overall change in the performance goals of media literacy, especially in 'critical understanding and evaluation', and improved in media use ethics and social participation consciousness as well. It is significant that this study has developed a program that can foster media literacy in home economics education. It is expected to help improve the acceptability of media literacy education in home economics education, enhancement of the expertise of home economics teachers' media literacy education, and the improvement of teaching and learning activities in the field.

• Journal of Digital Contents Society
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• v.16 no.4
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• pp.523-534
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• 2015

Social learning is a form to support learners' active engagement and participation in learning with other learners and instructors by using social media. The concept of social learning should be considered beyond the simple use of social media for learning or education. This study aims to apply the understanding of social learning based on the theoretical background of social theories of learning in designing and developing a program for workforce education. As a pilot test, the newly developed social learning program was implemented to 302 employees with the title of 'Innovative Display Strategy for POP". 138 employees successfully completed the social learning course that focuses on delivering contents in time-line based platform, supporting interactions among students, and working effectively through small smart devices in their workplace. The results were derived from three kinds of data-source: learner's log data, their final evaluation score, and the survey to measure the satisfaction about social learning. Finally the implications for social learning were discussed in terms of the program revision and directions for future application.

• Journal of Fisheries and Marine Sciences Education
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• v.15 no.2
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• pp.184-192
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• 2003

When the present Learning System for Computer-Related Subjects Using WBI is implemented on the Web with the above characteristics to help students to study computer subjects without any limitations of time or space, they can easily attain the goals of learning, have computer-utilizing abilities or information capacity, and enhance their capabilities for self-initiative learning. This system enables the learners to carry out 'plan-do-see' for the contents of learning initiatively. The learners can study the practice part of the curriculum using multi-media, such as motion pictures, voices, images, and sound effects, vividly with a sense of actual presence. It helps the students to have an active attitude toward leaning afterward. without meeting the teacher or without any storage media, the leaners can submit their assignments or materials for performance evaluation via the Internet.

• Smart Media Journal
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• v.11 no.2
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• pp.39-52
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• 2022

In the context of the evolution of automation and intelligence, deep learning and machine learning algorithms have been widely applied in aquaculture in recent years, providing new opportunities for the digital realization of aquaculture. Especially, water quality management deserves attention thanks to its importance to food organisms. In this study, we proposed an end-to-end deep learning-based TabNet model for water quality prediction. From major indexes of water quality assessment, we applied novel deep learning techniques and machine learning algorithms in innovative fish aquaculture to predict the number of water cells counting. Furthermore, the application of deep learning in aquaculture is outlined, and the obtained results are analyzed. The experiment on in-house data showed an optimistic impact on the application of artificial intelligence in aquaculture, helping to reduce costs and time and increase efficiency in the farming process.

• International Journal of Computer Science & Network Security
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• v.21 no.8
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• pp.238-246
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• 2021

The rapid rise of the Internet and social media has resulted in a large number of text-based reviews being placed on sites such as social media. In the age of social media, utilizing machine learning technologies to analyze the emotional context of comments aids in the understanding of QoS for any product or service. The classification and analysis of user reviews aids in the improvement of QoS. (Quality of Services). Machine Learning algorithms have evolved into a powerful tool for analyzing user sentiment. Unlike traditional categorization models, which are based on a set of rules. In sentiment categorization, Bidirectional Long Short-Term Memory (BiLSTM) has shown significant results, and Convolution Neural Network (CNN) has shown promising results. Using convolutions and pooling layers, CNN can successfully extract local information. BiLSTM uses dual LSTM orientations to increase the amount of background knowledge available to deep learning models. The suggested hybrid model combines the benefits of these two deep learning-based algorithms. The data source for analysis and classification was user reviews of Indian Railway Services on Twitter. The suggested hybrid model uses the Keras Embedding technique as an input source. The suggested model takes in data and generates lower-dimensional characteristics that result in a categorization result. The suggested hybrid model's performance was compared using Keras and Word2Vec, and the proposed model showed a significant improvement in response with an accuracy of 95.19 percent.

• Educational Technology International
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• v.23 no.2
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• pp.183-205
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• 2022

Mental status examination is an important stage in the assessment process because it serves as the foundation for establishing nursing diagnosis and intervention. Until now many students still feel difficult to understand the terms in the assessment of mental status. Interactive Mobile Learning in one of the media that is currently being developed. The use of this media will provide more in-depth learning opportunities, and students can practice their skills in carrying out practicals because of the mobility principle possessed by smartphones. The purpose of this study was to develop a smartphone-based app and evaluate the app's effectiveness by measuring nursing students' knowledge of mental status examination. Design: A randomized trial with a pre-and post-test design was conducted at a university in Indonesia. A total of seventy junior nursing students participated in this study. The intervention group received a smartphone-based app, and the control group received one-time lecture-based learning. We offered the experimental group the app and information about how to use it, and we encouraged them to use it. The control group received classroom instruction. Results: The intervention group scored significantly higher than the control group on knowledge score (t = 19.40, p = 0.000) and satisfaction with the learning method (t = 0.640, p = 0.021) Conclusion: These findings suggest that smartphonebased education could be an effective method in nursing education for teaching mental status examinations.

• The Journal of the Convergence on Culture Technology
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• v.9 no.5
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• pp.685-695
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• 2023

This study aimed to investigate the current utilization and implications of digital learning support media in the field of French studies, and to explore future research directions. To achieve this, we conducted a comprehensive review of the use of digital media in various learning processes within French studies. Additionally, we examined the direct application of ChatGPT, an emerging technology, to learning by extending its use to foreign language and education fields. Our findings indicate that the application of digital learning support media in French studies is somewhat limited, with selective use in processes such as online class support media, pre-class learning, efficient learning and interaction, and self-directed learning. In the case of ChatGPT, our research found that no studies have been conducted within French studies, and very few studies have been conducted on its practical application in other educational fields. While ChatGPT has a wide range of applications and has shown positive effects on learners, ethical concerns have been raised regarding the quality, source, and reliability of information. Therefore, future research in French studies should focus on educational application and effectiveness verification in university teaching and learning situations, as well as interdisciplinary convergence with digital learning support media.