• 대한전기학회:학술대회논문집
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• 대한전기학회 2003년도 하계학술대회 논문집 D
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• pp.2543-2545
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• 2003

This paper presents a web-based virtual classroom which can be creating efficiencies in the learning process of PHP language. The proposed flash animations which explain the important principles of several topics for PHP language are designed for the learners to easily understand by executing them through simple mouse clicks. The proposed flash animations enables the learners to achieve efficient and interesting self-learning since the learning process is designed to enhance the multimedia capabilities on the basis of various educational technologies. Also, internet-based on-line voice presentation and its related texts together with moving images are synchronized for efficient, language learning process. Through the proposed virtual classroom, the learners will be capable of learning the concepts related to PHP language and its coding. The results of this paper are to allow the implementation of an efficient virtual classroom, and are also expected to make a contributions to the activation of internet-based educational systems.

• 정보학연구
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• 제5권2호
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• pp.1-9
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• 2002

본 논문은 지역과 사회 탐구 단원을 학생들이 살고 있는 지역 사회인 영월군으로 재구성하고, 이를 웹 기반으로 하는 코스웨어로 구현하여 영월 지역 사회를 조사하고 평가하는 내용을 제시한다. 본 논문에서는 학교 인터넷을 통하여 개별적으로 자기 주도적으로 학습을 하게 하고, 또 전자 메일을 통하여 보고서나 평가 답안을 교사에게 전송하게 하는 상호 작용적인 학습 활동과 피드백을 할 수 있도록 한다. 따라서 학생들이 자신이 살고 있는 영월 지역사회를 이해하는데 큰 효과를 가져왔으며, 나아가 학생들이 지역사회의 발전과 보존을 위해 노력하는 자세를 갖도록 하였다. 이러한 웹 기반 코스웨어 방법이 모든 학교에서 이루어진다면 중학교 학생들에게 자기가 살고 있는 지역 사회와 문화 학습과 인터넷을 이용한 자기 주도적 학습과 정보통신기술 활용 능력 향상에 큰 도움이 될 것이다.

• 인터넷정보학회논문지
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• 제10권3호
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• pp.151-157
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• 2009

많은 이러닝 시스템들은 기존의 설정된 문항난이도를 기반으로 학습자에게 학습정보를 제공하고 있으며, 학습자는 정해진 학습과정에 따라 학습을 수행하고 있다. 이는 학습과정 중에서 학습자마다 학습을 이해하는 정도가 다름에도 불구하고 학습자는 정해진 난이도와 학습과정을 따라야 함으로 효율적인 학습효과를 나타내기 어렵다. 본 연구에서는 학습자가 학습과정중에 이해하는 정도를 시스템이 파악하여 능동적으로 난이도 및 학습과정을 조절하는 학습자 적응형 이러닝 시스템을 제시하고자 한다. 이를 통하여 학습자는 오프라인에서의 학습과 같은 대화형 학습을 통해 온라인 학습이 가져오는 획일적 학습에서 벗어나 보다 높은 학습효과를 높일 수 있다.

• 인터넷정보학회논문지
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• 제2권1호
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• pp.39-48
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• 2001

학습 활동에서 탐구학습은 실험실에서 주로 이루어진다. 이러한 실험실에서 탐구학습과정이 웹을 기반으로 하는 시뮬레이션 코스웨어로 설계하므로써 학생들에게 학습과정을 보다 쉽게 접근해 갈 수 있도록 하며 자기 스스로 사전학습과 탐구실험을 할 수 있는 공간을 제공하고 정보의 공유, 교환 및 상호 작용적인 학습자 중심의 교육 모델을 제공할 수 있다. 웹의 활용은 탐구학습에 적합한 학습도구가 될 뿐만 아니라, 학생들의 흥미를 유발시켜 보다 나은 교수학습 환경을 만들어준다. 이에 본 논문에서는 웹을 이용하여 역학적 에너지를 자기 스스로 학습할 수 있는 환경을 제공하고 탐구실험과정을 가상실험으로 실시할 수 있도록 학습모형을 시뮬레이션 코스웨어로 설계하고 구현하고자 한다.

• 인터넷정보학회논문지
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• 제11권5호
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• pp.95-104
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• 2010

학습을 이해하는 새로운 패러다임으로 구성주의가 등장하면서 협력학습의 필요성이 강조되고 있다. 특히 교수와 학습에 대한 새로운 접근을 지원하는 테크놀로지로 온라인이 부각되면서 온라인 협력학습에 대한 관심이 증대하고 있다. 본 연구는 온라인 협력학습에서 하나의 주요한 요인인 학습과제 유형에 따른 협력학습 모형을 탐색하여 온라인 협력학습 실제에 도움을 주고자 하였다. 이를 위해 학습과제를 문제해결과제와 지식학습과제로 분류한 후, 학습과제 유형별로 적합한 온라인 협력학습 설계와 환경, 그리고 학습과정을 살펴보았다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권9호
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• pp.3138-3150
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• 2021

Multimedia services on the Internet are continuously increasing. Accordingly, the demand for a technology for efficiently delivering multimedia traffic is also constantly increasing. The multicast technique, that delivers the same content to several destinations, is constantly being developed. This technique delivers a content from a source to all destinations through the multicast tree. The multicast tree with low cost increases the utilization of network resources. However, the finding of the optimal multicast tree that has the minimum link costs is very difficult and its calculation complexity is the same as the complexity of the Steiner tree calculation which is NP-complete. Therefore, we need an effective way to obtain a multicast tree with low cost and less calculation time on SDN-based smart network platforms. In this paper, we propose a new multicast tree generation algorithm which produces a multicast tree using an agent trained by model-based meta reinforcement learning. Experiments verified that the proposed algorithm generated multicast trees in less time compared with existing approximation algorithms. It produced multicast trees with low cost in a dynamic network environment compared with the previous DQN-based algorithm.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권3호
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• pp.974-992
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• 2021

Recently, the healthcare field has undergone rapid changes owing to the accumulation of health big data and the development of machine learning. Data mining research in the field of healthcare has different characteristics from those of other data analyses, such as the structural complexity of the medical data, requirement for medical expertise, and security of personal medical information. Various methods have been implemented to address these issues, including the machine learning model and cloud platform. However, the machine learning model presents the problem of opaque result interpretation, and the cloud platform requires more in-depth research on security and efficiency. To address these issues, this paper presents a recent technology for Internet-of-Things-based (IoT-based) health big data processing. We present a cloud-based IoT health platform and health big data processing technology that reduces the medical data management costs and enhances safety. We also present a data mining technology for health-risk prediction, which is the core of healthcare. Finally, we propose a study using explainable artificial intelligence that enhances the reliability and transparency of the decision-making system, which is called the black box model owing to its lack of transparency.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제5권11호
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• pp.2121-2142
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• 2011

In this paper, we propose an e-Learning process control model that aims to graphically describe and automatically generate the manifest of sequencing prerequisites in packaging SCORM's content aggregation models. In specifying the e-Learning activity sequencing, SCORM provides the concept of sequencing prerequisites to be manifested on each e-Learning activity of the corresponding tree-structured content organization model. However, the course developer is required to completely understand the SCORM's complicated sequencing prerequisites and other extensions. So, it is necessary to achieve an efficient way of packaging for the e-Learning content organization models. The e-Learning process control model proposed in this paper ought to be an impeccable solution for this problem. Consequently, this paper aims to realize a new concept of process-driven e-Learning content aggregating approach supporting the e-Learning process control model and to implement its e-Learning process modeling system graphically describing and automatically generating the SCORM's sequencing prerequisites. Eventually, the proposed model becomes a theoretical basis for implementing a SCORM-based e-Learning process management system satisfying the SCORM's sequencing prerequisite specifications. We strongly believe that the e-Learning process control model and its modeling system achieve convenient packaging in SCORM's content organization models and in implementing an e-Learning management system as well.

• International Journal of Internet, Broadcasting and Communication
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• 제16권1호
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• pp.36-46
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• 2024

This study is a descriptive survey to determine the effects of fundamentals of nursing skills education with peer evaluation on self-leadership, self-directed learning ability, and learning attitude. The factors affecting self-directed learning ability were peer evaluation, self-leadership, and learning attitude (F=118.81, p<.001), with an explanatory power of 50.4%. The factors affecting learning attitude were peer evaluation, self-leadership, and self-directed learning ability (F=48.89, p<.001), with an explanatory power of 29.5%. Based on the results of this study, we believe that it is necessary to apply various teaching methods such as peer evaluation and promote self-leadership to improve self-directed learning and learning attitude.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제18권6호
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• pp.1462-1477
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• 2024

With the evolving complexity of connected vehicle features, the volume and diversity of data generated during driving continue to escalate. Enabling data sharing among interconnected vehicles holds promise for improving users' driving experiences and alleviating traffic congestion. Yet, the unintentional disclosure of users' private information through data sharing poses a risk, potentially compromising the interests of vehicle users and, in certain cases, endangering driving safety. Federated learning (FL) is a newly emerged distributed machine learning paradigm, which is expected to play a prominent role for privacy-preserving learning in autonomous vehicles. While FL holds significant potential to enhance the architecture of the Internet of Vehicles (IoV), the dynamic mobility of vehicles poses a considerable challenge to integrating FL with vehicular networks. In this paper, a novel clustered FL framework is proposed which is efficient for reducing communication and protecting data privacy. By assessing the similarity among feature vectors, vehicles are categorized into distinct clusters. An optimal vehicle is elected as the cluster head, which enhances the efficiency of personalized data processing and model training while reducing communication overhead. Simultaneously, the Local Differential Privacy (LDP) mechanism is incorporated during local training to safeguard vehicle privacy. The simulation results obtained from the 20newsgroups dataset and the MNIST dataset validate the effectiveness of the proposed scheme, indicating that the proposed scheme can ensure data privacy effectively while reducing communication overhead.