• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.7
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• pp.3333-3350
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• 2019

The smart jammer launches jamming attacks which degrade the transmission reliability. In this paper, smart jamming attacks based on the communication probability over different channels is considered, and an anti-jamming Q learning algorithm (AQLA) is developed to obtain anti-jamming knowledge for the local region. To accelerate the learning process across multiple regions, a multi-regional intelligent anti-jamming learning algorithm (MIALA) which utilizes transferred knowledge from neighboring regions is proposed. The MIALA algorithm is evaluated through simulations, and the results show that the it is capable of learning the jamming rules and effectively speed up the learning rate of the whole communication region when the jamming rules are similar in the neighboring regions.

• International Journal of Internet, Broadcasting and Communication
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• v.15 no.1
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• pp.8-12
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• 2023

With the recent expansion of online lectures, studies on their effectiveness and their influencing factors have increased. This study examines the factors affecting the satisfaction of online classes, considering the utilization and importance of online lectures, which have greatly increased in recent years. Based on the review of previous studies, this study identified learning presence, self-efficacy, and learning immersion as factors affecting the satisfaction of online classes, and suggested hypotheses that explain the relationship between these factors, and empirically reviewed the hypotheses. As a result of the study, it was found that learning presence and self-efficacy had a positive effect on learning immersion, and learning immersion had a positive effect on learning satisfaction. Based on the research results, some practical implications for improving the satisfaction of online classes were suggested.

• 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.

• The Journal of the Korea Contents Association
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• v.19 no.11
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• pp.383-391
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• 2019

This study confirms the mediating effect of learning amotivation in the effect of an elementary school senior parental excessive interference on Internet addiction. To this end, 329 elementary school students in fifth and sixth grade were analyzed by collecting data on parental excessive interference, learning amotivation and Internet addiction. The results of the analysis are as follows. First, there was a significant static relationship between parental excessive interference, learning amotivation, and Internet addiction. Second, parental excessive interference had a significant effect on Internet addiction. Third, parental excessive interference had a significant effect on learning amotivation, and the learning amotivation affected a significant effect on Internet addiction. Through this process, it was confirmed that the learning amotivation has an indirect mediated effect on the effect of parental excessive interference on Internet addiction. Based on the results of this study, the educational implications of preventing Internet addiction among elementary school students and suggestions for follow-up research were discussed.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.616-621
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• 2003

Until now, many networked robots have been connected to the Internet for the various applications. With these networked robots, very long distance teleoperation can be possible through the Internet. However, the promising area of the Internet-based teleoperation may be distance learning, because of several reasons such as the unpredictable characteristics of the Internet. In robotics class, students learn many theories about robots, but it is hard to perform the actual experiments for all students due to the rack of the real robots and safety problems. Some classes may introduce the virtual robot simulator for students to program the virtual robot and upload their program to operate the real robot through the off-line programming method. However, the students may also visit the laboratory when they want to use the real robot for testing their program. In this paper, we developed an Internet-based robot education system. The developed system was composed of two parts, the robotics class materials and the web-based Java3d robot simulator. That is, this system can provide two services for distance learning to the students through the Internet. The robotics class materials can be provided to the student as the multimedia contents on the web page. As well, the web-based robot simulator as the real experiment tool can help the students get good understanding about certain subject. So, the students can learn the required robotics theories and perform the real experiments from their web browser when they want to study themselves at any time.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.4 no.4
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• pp.595-617
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• 2010

This paper proposes a novel of a personalized Computer Assisted Language Learning (CALL) system based on learner's cognitive abilities related to foreign language proficiency. In this CALL system, a strategy of retrieval learning, a method of learning memory cycle, and a method of repeated learning are applied for effective vocabulary memorization. The system is designed to offer personalized learning based on cognitive abilities related to the human language process. For this, the proposed CALL system has a cognitive diagnosis module which can measure five types of cognitive abilities. The results of this diagnosis are used to create dynamic learning scenarios for personalized learning and to evaluate user performance in the learning. This system is also designed in order to have users be able to create learning word lists and to share them simply with various functions based on open APIs. Additionally, through experiments, it has shown that this system helps students to learn English vocabulary effectively and enhances their foreign language skills.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.10
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• pp.3771-3788
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• 2015

This paper studies the distributed energy efficient resource management in the Internet of Things (IoT). Wireless communication networks support the IoT without limitation of distance and location, which significantly impels its development. We study the communication channel and energy management in the wireless communication network supported IoT to improve the ability of connection, communication, share and collaboration, by using the game theory and distributed learning algorithm. First, we formulate an energy efficient neighbor collaborative game model and prove that the proposed game is an exact potential game. Second, we design a distributed energy efficient channel selection learning algorithm to obtain the global optimum in a distributed manner. We prove that the proposed algorithm will asymptotically converge to the global optimum with geometric speed. Finally, we make the simulations to verify the theoretic analysis and the performance of proposed algorithm.

• International Journal of Internet, Broadcasting and Communication
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• v.10 no.2
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• pp.60-67
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• 2018

The purpose of this study was to examine the effects of internet-use Anatomy class, as one of the Flipped learning method, on critical thinking disposition. The class for this study was conducted from March 1 to April 10, 2018. The study involved a total of 180 people in the first year of a University located in C province. Data collection was carried out before and after the Flipped learning method application. Frequency analysis, Paired t-test, Pearson correlation, and Regression analysis were used for the analysis. According to the analysis, 28.3% of men and 71.1% of women and before applying the program analysis of correlation between Flipped learning perception and critical thinking disposition showed a significant correlation between confidence(sub-component of critical thinking) only (p<.005). Comparing the scores of critical thinking before and after the program, it was found that Truth seeking (p<.001), Open-mindness (p<.005), Confidence (p<.001), Systematicity (p<.005), Analyticity (p<.001), and Inquisitiveness (p<.001) scores had increased significantly except Maturity (p>.005). And the regression analysis of Flipped learning method applying influence on critical thinking disposition were significantly affected (p<.001). Based on the results of this study, it was possible to determine that Flipped learning method had a positive effect on critical thinking disposition.

• Proceedings of the Korea Contents Association Conference
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• 2005.11a
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• pp.284-288
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• 2005

Internet-based distance learning is proliferated, but justifying its educational effectiveness is challenging. Investigation into the problems is important and fundamental for the study on the educational effectiveness. In this study, problems of Internet-based distance learning are structured by four categories as environmental problems, student problems, course design problems and operational problems. Based on a survey of distance education participants, problems are analysed. Also, analysis from the point of demographic differences such as sex, job existence and age is performed.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.11
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• pp.4246-4267
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• 2020

As the network goes deep into all aspects of people's lives, the number and the complexity of network traffic is increasing, and traffic classification becomes more and more important. How to classify them effectively is an important prerequisite for network management and planning, and ensuring network security. With the continuous development of deep learning, more and more traffic classification begins to use it as the main method, which achieves better results than traditional classification methods. In this paper, we provide a comprehensive review of network traffic classification based on deep learning. Firstly, we introduce the research background and progress of network traffic classification. Then, we summarize and compare traffic classification based on deep learning such as stack autoencoder, one-dimensional convolution neural network, two-dimensional convolution neural network, three-dimensional convolution neural network, long short-term memory network and Deep Belief Networks. In addition, we compare traffic classification based on deep learning with other methods such as based on port number, deep packets detection and machine learning. Finally, the future research directions of network traffic classification based on deep learning are prospected.