• International Journal of Internet, Broadcasting and Communication
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• v.16 no.1
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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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• v.18 no.6
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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.

• Journal of the Korea Society of Computer and Information
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• v.16 no.10
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• pp.45-52
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• 2011

Web 2.0 could influence the teaching and learning system significantly due to its characteristics to utilize information using internet in various ways, to create information, and to reorganize it through information sharing. In this new environment of information-oriented classes using the computer, positive education method is required to develop new teaching/learning method based on the internet web 2.0 in order to fulfill the learner's intellectual curiosity and to lead the future-oriented classes. This paper proposed a teaching-learning models in the web 2.0-based internet information education and its effect analysis.

• The Journal of Sustainable Design and Educational Environment Research
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• v.20 no.3
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• pp.25-32
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• 2021

This study analyzed the application articles of the Internet of Things (IoT) in the educational environment. It defined learning environmental data, utilization scenarios, and models that IoT can improve teaching and learning through Focus Group Interviews for academic experts, teachers, and technicians in related fields. In addition, the IoT pilot prototype was developed, verified, and drew implications from the perspective of collection, analysis, and utilization of real-time data based on the actual school settings. This study has significance as a priori case of building and applying a learning environment using the Internet of Things in real school settings considering relevant restrictions.

• Journal of Internet of Things and Convergence
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• v.5 no.2
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• pp.81-87
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• 2019

It is changing to the 4^th Industrial Revolution rapidly as the information age through the Internet is changing, and it is rapidly changing to the era of the IoT using all things. In education, with the change to the Internet of Things, interest in education for the 4th Industrial Revolution is increasing. It is necessary to change from NetPBL method using Internet to T-PBL using Triz. In this paper, we focus on the task-based learning (T-PBL) method using Triz and examine the necessity and importance of its use. We propose a teaching model using Triz as a tool for T-PBL. Triz is being used as a tool to solve problems in creative ways. We will design a model applying Triz to the blockchain system security class related to the IoT.

• Journal of Internet Computing and Services
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• v.12 no.6
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• pp.95-103
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• 2011

This study was executed to give a help in planning and implementing Blended learning through investigating the learners' satisfaction difference according to Blended learning situation and learners' trait. For this purpose this study divided online learning situation into three types to examine the influence on satisfaction. And participants was divided based on the learning style to examine the influence of the trait on satisfaction. The Blended learning satisfaction classified into four; web environment, content, face to face sessions, general view on Blended learning's implementation.

• Proceedings of the KIEE Conference
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• 2004.11c
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• pp.502-504
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• 2004

With rapidly growing of computer and internet technology, Internet-based tote-operation of robotic systems has created new opportunities in resource sharing, long-distance learning, and remote experimentation. In this paper, remote control system of a mobile robot through the internet has been designed. The internet users can access and command a mobile robot in the real time, receiving the robot's sensor data. The overall system has been tested and its usefulness shown through the experimental results.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.9
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• pp.2961-2975
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• 2022

The Location-Based Service (LBS) is one of the most well-known services on the Internet. Positioning is the primary association with LBS services. This study proposes an intelligent LoRa-based positioning system, called AI@LBS, to provide accurate location data. The fingerprint mechanism with the clustering algorithm in unsupervised learning filters out signal noise and improves computing stability and accuracy. In this study, data noise is filtered using the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm, increasing the positioning accuracy from 95.37% to 97.38%. The problem of data imbalance is addressed using the SMOTE (Synthetic Minority Over-sampling Technique) technique, increasing the positioning accuracy from 97.38% to 99.17%. A field test in the NTUST campus (www.ntust.edu.tw) revealed that AI@LBS system can reduce average distance error to 0.48m.

• International Journal of Internet, Broadcasting and Communication
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• v.12 no.1
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• pp.144-150
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• 2020

Reinforcement learning is a technology that can present successful and creative solutions in many areas. This reinforcement learning technology was used to deploy containers from cloud servers to fog servers to help them learn the maximization of rewards due to reduced traffic. Leveraging reinforcement learning is aimed at predicting traffic in the network and optimizing traffic-based fog computing network environment for cloud, fog and clients. The reinforcement learning system collects network traffic data from the fog server and IoT. Reinforcement learning neural networks, which use collected traffic data as input values, can consist of Long Short-Term Memory (LSTM) neural networks in network environments that support fog computing, to learn time series data and to predict optimized traffic. Description of the input and output values of the traffic-based reinforcement learning LSTM neural network, the composition of the node, the activation function and error function of the hidden layer, the overfitting method, and the optimization algorithm.

• Journal of Korea Multimedia Society
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• v.6 no.4
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• pp.676-684
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• 2003

eLearning can be defined as an approach to teaching and teaming that utilises Internet technologies to communicate and collaborate in an educational context. This includes technology that supplements traditional classroom training with web-based components and learning environments where the educational process is experienced online. The use of hypertext as an educational tool has a very rich history. The advent of the internet and one of its major application, the world wide web (WWW), has given a tremendous boost to the theory and practice of hypermedia systems for educational purposes. However, the web suffers from an inability to satisfy the heterogeneous needs of a large number of users. For example, web-based courses present the same static teaming material to students with widely differing knowledge of the subject. Adaptive hypermedia techniques can be used to improve the adaptability of eLearning. In this paper we report an approach to the design a unified implementation framework suitable for web-based eLearning that accommodates the three main dimensions of hypermedia adaptation: content, navigation, and presentation. The framework externalises the adaptation strategies using XML notation. The separation of the adaptation strategies from the source code of the eLearning software enables a system using the framework to quickly implement a variety of adaptation strategies. This work is a part of our more general ongoing work on the design of a framework for adaptive content delivery. parts of the framework discussed in this paper have been imulemented in a commercial eLearning engine.