• International Journal of Internet, Broadcasting and Communication
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• v.4 no.2
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• pp.12-16
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• 2012

This paper presents a learning framework that fits the digital age - an e-Friendly PLE. The learning framework is based on the theory of connectivism which asserts that knowledge and the learning of knowledge is distributive and is not located in any given place but rather consists of the network of connections formed from experiences and interactions with a knowing community, thus, the newly empowered learner is thinking and interacting in new ways. The framework's approach to learning is based on conversation and interaction, on sharing, creation and participation, on learning not as a separate activity, but rather as embedded in meaningful activities such as games or workflows. It sees learning as an active, personal inquiry, interpretation, and construction of meaning from prior knowledge and experience with one's actual environment.

• Journal of Internet Computing and Services
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• v.11 no.1
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• pp.71-76
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• 2010

This research proposed the method for application of SCORM in U-learning system. That is, I proposed the API broker to connect between U-learning and API Instance of RTE that is existing SCORM based learning object interface environment. The API broker operated handling process using request port and response port between SCORM and U-learning server. For efficient operation in each service, this system has learning contents service buffer in API broker.

• The Journal of the Korea Contents Association
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• v.6 no.3
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• pp.102-107
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• 2006

While Internet-based distance learning has proliferated, its educational effectiveness remains controversial. As such, investigation into the problems of Internet-based distance learning is important and fundamental for an assessment of its educational effectiveness. In this study, problems of Internet-based distance learning are grouped into four categories, environmental problems, student problems, course design problems and operational problems. Based on survey data from 606 distance learning participants, perceptional and relative seriousness of problems are analysed. The results indicate that perceptional seriousness of all the problems is not statistically important. However, the findings indicate that the relative seriousness of self willingness among student problems and cheating within operational problems is most remarkable. In contrast, the relative seriousness of traditionally recognized problems such as H/W availability and network speed among environmental problems is least remarkable. An analysis of demographic differences such as sex, employment and age in terms of seriousness of problem is also performed. The results reveal the existence of statistically significant differences according to sex, employment, and age difference with regard to seriousness of problems.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.3
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• pp.755-778
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• 2024

In recent years, the number of devices being connected to the internet has grown enormously, as has the intrusive behavior in the network. Thus, it is important for intrusion detection systems to report all intrusive behavior. Using deep learning and machine learning algorithms, intrusion detection systems are able to perform well in identifying attacks. However, the concern with these deep learning algorithms is their inability to identify a suitable network based on traffic volume, which requires manual changing of hyperparameters, which consumes a lot of time and effort. So, to address this, this paper offers a solution using the extended compact genetic algorithm for the automatic tuning of the hyperparameters. The novelty in this work comes in the form of modeling the problem of identifying attacks as a multi-objective optimization problem and the usage of linkage learning for solving the optimization problem. The solution is obtained using the feature map-based Convolutional Neural Network that gets encoded into genes, and using the extended compact genetic algorithm the model is optimized for the detection accuracy and latency. The CIC-IDS-2017 and 2018 datasets are used to verify the hypothesis, and the most recent analysis yielded a substantial F1 score of 99.23%. Response time, CPU, and memory consumption evaluations are done to demonstrate the suitability of this model in a fog environment.

• Journal of Digital Convergence
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• v.5 no.2
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• pp.123-134
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• 2007

The purpose of this study is to design and implementation of project learning site by using XML. The development of the Internet site for project learning was planned as per preparation, development and test/application stages. The research shows that the elements used for the development of the Internet site for project learning are to give learners motivation, specification of learning goals, reminiscence of preceding knowledge, positive participation in teaching activities, learning-guide feedback, evaluation, reinforcement and correction. It is expected that many teachers apply this model to their classes and show realistic results to motivate their students.

• Journal of Science Education
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• v.41 no.2
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• pp.248-266
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• 2017

The purpose of this study is to present various examples of collaborative learning based on the Instruction of Internet in the 6th grade elementary school mathematics class. So we introduce the design method of classroom environment for classroom Internet and give example of various teaching methods. This study was conducted for nine months from March to November, 2016, one sixth grade of elementary school in D area. During this period, we conducted Instruction of Internet-based collaborative learning to classify typical teaching cases. We classified into 5 type collaborative learning. First, collaborative learning in the classroom. Second, remote collaborative learning between classroom and classroom. Third, Live participation classes. Forth, project collaborative learning. Fifth, using virtual reality in collaborative learning. In addition, we could identify that there is a difference compared to the conventional learning. It became possible to conduct collaborative learning with other students simultaneously or have opening class with both parents and teachers by using Youtube. These examples can be presented as a case to depart from traditional mathematics class in one classroom. In this regard, we will be able to provide several implications about teaching methods utilizing smart device and Internet in future classroom.

• Journal of Internet Computing and Services
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• v.7 no.6
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• pp.131-141
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• 2006

In many papers, item difficulty does apply to the E-Learning system for advance learning effect. But there have to need item revision difficulty for more correct item difficulty. Also, there have to support self-directed learning process which learner can make plan and operate learning progress. In this research, I developed self-directed E-Learning system using item revision difficulty. For efficiency of system development, il is implemented and composited by component based development. In the applied result, it was able to support more correct item revision difficulty to learner. And I displayed efficiency of operation the component based self-directed learning system.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.10
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• pp.2650-2662
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• 2012

Due to the semantic gap issue, the performance of automatic image annotation is still far from satisfactory. Active learning approaches provide a possible solution to cope with this problem by selecting most effective samples to ask users to label for training. One of the key research points in active learning is how to select the most effective samples. In this paper, we propose a novel active learning approach based on sparse graph. Comparing with the existing active learning approaches, the proposed method selects the samples based on two criteria: uncertainty and representativeness. The representativeness indicates the contribution of a sample's label propagating to the other samples, while the existing approaches did not take the representativeness into consideration. Extensive experiments show that bringing the representativeness criterion into the sample selection process can significantly improve the active learning effectiveness.

• International Journal of Internet, Broadcasting and Communication
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• v.16 no.2
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• pp.127-135
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• 2024

Recent studies have shown that inverse design using deep learning has the potential to rapidly generate the optimal design that satisfies the target performance without the need for iterative optimization processes. Unlike traditional methods, deep learning allows the network to rapidly generate a large number of solution candidates for the same objective after a single training, and enables the generation of diverse designs tailored to the objectives of inverse design. These inverse design techniques are expected to significantly enhance the efficiency and innovation of design processes in various fields such as aerospace, biology, medical, and engineering. We analyzes inverse design models that are mainly utilized in the nano and chemical fields, and proposes inverse design models based on supervised and unsupervised learning that can be applied to the engineering system. It is expected to present the possibility of effectively applying inverse design methodologies to the design optimization problem in the field of engineering according to each specific objective.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.3
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• pp.1228-1248
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• 2020

Steganalysis & steganography have witnessed immense progress over the past few years by the advancement of deep convolutional neural networks (DCNN). In this paper, we analyzed current research states from the latest image steganography and steganalysis frameworks based on deep learning. Our objective is to provide for future researchers the work being done on deep learning-based image steganography & steganalysis and highlights the strengths and weakness of existing up-to-date techniques. The result of this study opens new approaches for upcoming research and may serve as source of hypothesis for further significant research on deep learning-based image steganography and steganalysis. Finally, technical challenges of current methods and several promising directions on deep learning steganography and steganalysis are suggested to illustrate how these challenges can be transferred into prolific future research avenues.