• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권7호
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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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• 제15권1호
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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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• 제15권1호
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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.

• 한국콘텐츠학회논문지
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• 제19권11호
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• pp.383-391
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• 2019

본 연구는 초등학교 고학년 학생 부모의 과잉간섭이 인터넷 중독에 미치는 영향에 있어서 학습무동기의 매개효과를 확인하는 것이다. 이를 위해 초등학생 5, 6학년 329명을 대상으로 과잉간섭, 학습무동기, 인터넷 중독에 대한 자료를 수집하여 분석을 실시하였다. 분석 결과는 다음과 같다. 첫째, 과잉간섭, 학습무동기, 인터넷 중독 간에는 유의한 정적상관이 나타났다. 둘째, 과잉간섭은 인터넷 중독에 유의한 영향을 주었다. 셋째, 과잉 간섭은 학습무동기에 유의한 영향을 주었으며, 이러한 영향을 받은 학습무동기는 인터넷 중독에 유의한 영향을 주었다. 이를 통해 학습무동기가 과잉간섭이 인터넷 중독에 영향을 미치는 것에 있어서 간접 매개효과가 있음을 확인하였다. 본 연구 결과를 토대로 초등학생의 인터넷 중독 예방에 대한 교육적 시사점과 후속 연구에 대한 제언을 논의하였다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2003년도 ICCAS
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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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• 제4권4호
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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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• 제9권10호
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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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• 제10권2호
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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.

• 한국콘텐츠학회:학술대회논문집
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• 한국콘텐츠학회 2005년도 추계 종합학술대회 논문집
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• pp.284-288
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• 2005

인터넷 원격교육이 보편화되어 가고 있는 현시점에서 인터넷 원격교육의 교육적 효과에 대한 검증은 매우 필요한 연구 대상이라고 할 수 있다. 면대면 교육이 아닌 인터넷원격교육의 문제점에 대한 분석은 교육적 효과에 대한 검증에서 반드시 거쳐야하는 중요성을 내포하고 있다. 본 연구는 인터넷 원격교육에 참가한 학생들을 대상으로 한 설문을 바탕으로 문제점에 대한 분석을 실시하였다. 문제점을 환경적문제, 학생자신의 문제, 교수설계문제, 운영상의문제의 4가지 카타고리로 구분하고 카타고리별 문제점들을 도출하여 설문을 통해 문제의 심각도를 분석하였다. 또한 각 문제들의 성별, 직업유무, 나이에 따른 차이 유무를 통계적 방법론을 통하여 검증하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권11호
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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.