• International Journal of Internet, Broadcasting and Communication
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• v.15 no.1
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• pp.56-62
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• 2023

In the educational environment of the post-COVID-19 era, metaverse learning, which can improve the disadvantages of online learning and improve learning outcomes, is attracting attention. Metaverse is expected to play an important role as a learning experience platform (LXP) that can provide immersion and experience for learners. In order to successfully introduce and utilize metaverse learning that utilizes the metaverse platform, teachers' knowledge of metaverse-related technologies and pedagogical convergence is important. So far, teacher knowledge for educational use of the metaverse has not been explored. In this regard, this study explored the TPACK (Technological, Pedagogical And Content Knowledge) framework as a teacher's knowledge system for metaverse learning. Based on this, this study designed the class contents of metaverse learning. The results of this study are expected to diffuse the importance of TPACK required for metaverse learning and contribute to the development of teachers' competence.

• International Journal of Computer Science & Network Security
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• v.23 no.6
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• pp.13-16
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• 2023

This study aims to explore the status of e-learning in the public sector institutes of the Sukkur region in Pakistan. A survey was conducted to collect data from students and teachers regarding their awareness, access, and use of e-learning resources. The results showed that although there is a widespread use of the internet and mobile devices for accessing information, there is a lack of awareness and access to e-learning resources. Barriers to accessing e-learning content and a lack of familiarity with e-learning content development technologies were also identified. The study concludes that there is a need for improved e-learning facilities and curriculum in the public sector institutes of the Sukkur region in Pakistan. Recommendations are provided for developing a correctness-centered e-learning based curriculum that is tailored to the specific needs of the students in the region. It is hoped that the findings of this study will inform efforts to improve the teaching and learning process in the region and provide students with greater flexibility and access to study materials.

• Cartoon and Animation Studies
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• s.43
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• pp.363-386
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• 2016

This research is to design basic framework for developing teaching and learning method in the field of art and design major in university. Especially taking off from oneway e-learning teaching method, it is focused on increasing reality of student learning situation with applying AR contents process to augment virtual condition on reality condition. The processing of framework design and developing teaching and learning method are about practice education based on AR for model experiment research which was published "The E-Learning for Practice Training Using Augmented Reality in the College Education". This thesis is supposed to be a precedent study of the pre-published, and the purpose of those two studies were for experiment test in earnest in studying effect research. The classification of learning method divided basically as Face-to-Face Learning(FFL), Blended Learning(BL), fully E-Learning(EL), and Augmented Reality based E-Learning(AREL). This research compares and analyzes each frame of FFL, BL, and EL. And then, designed framework lead to the over-all conclusion with the type of AREL. Additionally, AREL for PBE suggests the ways of advanced teaching learning.

• The Journal of Korean Association of Computer Education
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• v.15 no.4
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• pp.25-39
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• 2012

Smart education is defined as creating new values through connecting educational elements based on smart devices and infrastructure. In the study, we propose a theoretical and procedural framework for developing smart learning activities, which is considered characteristic of smart education, as well as environments in smart schools of Sejong Special Autonomous city. In addition, we discuss an instructional model developed from the framework. A smart learning activity as a basic unit in instruction is represented as a block when design and instruction focuses on smart learning activities. The block consists of components from learning activities, motivation, information activities, and tools when a teacher has smart learning ideas. Based on the theoretical and procedural framework, the thought-sharing model (i.e., that learners share ideas and opinions with classmates, review classmates' work, and enhance their own work) is an instructional model that leads to smart education. We discuss considerations for developing instructional models using the framework.

• Journal of The Korean Association of Information Education
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• v.23 no.6
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• pp.639-653
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• 2019

The purpose of this study is to design an instructional framework and cognitive learning environment for AI education based on computational thinking in order to ground the theoretical rationale for AI education. Based on the literature review, the learning model is proposed to select the algorithms and problem-solving models through the abstraction process at the stage of data collection and discovery. Meanwhile, the instructional model of AI education through computational thinking is suggested to enhance the problem-solving ability using the AI by performing the processes of problem-solving and prediction based on the stages of automating and evaluating the selected algorithms. By analyzing the research related to the cognitive learning environment for AI education, the instructional framework was composed mainly of abstraction which is the core thinking process of computational thinking through the transition from the stage of the agency to modeling. The instructional framework of AI education and the process of constructing the cognitive learning environment presented in this study are characterized in that they are based on computational thinking, and those are expected to be the basis of further research for the instructional design of AI education.

• Korean Journal of Artificial Intelligence
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• v.11 no.2
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• pp.19-27
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• 2023

In recent times, an exponential increase in Internet traffic has been observed as a result of advancing development of the Internet of Things, mobile networks with sensors, and communication functions within various devices. Further, the COVID-19 pandemic has inevitably led to an explosion of social network traffic. Within this context, considerable attention has been drawn to research on network traffic analysis based on machine learning. In this paper, we design and develop a new machine learning framework for network traffic analysis whereby normal and abnormal traffic is distinguished from one another. To achieve this, we combine together well-known machine learning algorithms and network traffic analysis techniques. Using one of the most widely used datasets KDD CUP'99 in the Weka and Apache Spark environments, we compare and investigate results obtained from time series type analysis of various aspects including malicious codes, feature extraction, data formalization, network traffic measurement tool implementation. Experimental analysis showed that while both the logistic regression and the support vector machine algorithm were excellent for performance evaluation, among these, the logistic regression algorithm performs better. The quantitative analysis results of our proposed machine learning framework show that this approach is reliable and practical, and the performance of the proposed system and another paper is compared and analyzed. In addition, we determined that the framework developed in the Apache Spark environment exhibits a much faster processing speed in the Spark environment than in Weka as there are more datasets used to create and classify machine learning models.

• Convergence Security Journal
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• v.22 no.4
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• pp.129-133
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• 2022

Deep learning frameworks are still evolving, and there are various frameworks. Typical deep learning frameworks include TensorFlow, PyTorch, and Keras. The Deepram framework utilizes optimization models in image classification through image learning. In this paper, we use the TensorFlow and PyTorch frameworks, which are most widely used in the deep learning image recognition field, to proceed with image learning, and compare and analyze the results derived in this process to know the optimized framework. was made.

• Electronics and Telecommunications Trends
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• v.38 no.4
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• pp.95-103
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• 2023

The importance of the decentralized training with decentralized execution (DTDE) framework is well-known in the study of multiagent reinforcement learning. In many real-world environments, agents cannot share information. Hence, they must be trained in a decentralized manner. However, the DTDE framework has been less studied than the centralized training with decentralized execution framework. One of the main reasons is that many problems arise when training agents in a decentralized manner. For example, DTDE algorithms are often computationally demanding or can encounter problems with non-stationarity. Another reason is the lack of simulation environments that can properly handle the DTDE framework. We discuss current research trends in the DTDE framework.

• Education of Primary School Mathematics
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• v.26 no.4
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• pp.277-298
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• 2023

Despite the significance of teacher guidebooks as a support for teacher learning, there are few studies that address the educative features of teacher guidebooks. The purpose of this study is to develop a framework for analyzing the educative features of teacher guidebooks for elementary school mathematics. The framework developed by Fuentes and Ma(2018) for analyzing teacher guidebooks, "Teacher Learning Opportunities in Mathematics Curriculum Materials", was used as an initial framework by adding the unit development flow that reflects on the organizational features of teacher guidebooks in Korea for elementary mathematics. Then, the framework was modified and supplemented by testing 10 types of teacher guidebooks for Grades 3 and 4 per six units reflecting on different mathematical strands. As a result, the final framework expanded the initial framework and added elements related to each dimension of the framework according to the unit development flow. The analytical framework developed in this study can be used to closely analyze the educative features of teacher guidebooks of Korean elementary school mathematics in the future and to develop teacher guidebooks to promote teacher learning.

• Journal of Multimedia Information System
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• v.6 no.2
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• pp.81-86
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• 2019

Deep learning is emerging as one of the best tool in processing data related to medical imaging. In our research work, we have proposed a deep learning based framework CNN (Convolutional Neural Network) for the classification of dysplastic tissue images. The CNN has classified the given images into 4 different classes namely normal tissue, mild dysplastic tissue, moderate dysplastic tissue and severe dysplastic tissue. The dataset under taken for the study consists of 672 tissue images of epithelial squamous layer of oral cavity captured out of the biopsy samples of 52 patients. After applying the data pre-processing and augmentation on the given dataset, 2688 images were created. Further, these 2688 images were classified into 4 categories with the help of expert Oral Pathologist. The classified data was supplied to the convolutional neural network for training and testing of the proposed framework. It has been observed that training data shows 91.65% accuracy whereas the testing data achieves 89.3% accuracy. The results produced by our proposed framework are also tested and validated by comparing the manual results produced by the medical experts working in this area.