• 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.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.6
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• pp.1818-1832
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• 2022

Violence in the Internet era poses a new challenge to the current counter-riot work, and according to research and analysis, most of the violent incidents occurring are related to the dissemination of violence images. The use of the popular deep learning neural network to automatically analyze the massive amount of images on the Internet has become one of the important tools in the current counter-violence work. This paper focuses on the use of transfer learning techniques and the introduction of an attention mechanism to the residual network (ResNet) model for the classification and identification of violence images. Firstly, the feature elements of the violence images are identified and a targeted dataset is constructed; secondly, due to the small number of positive samples of violence images, pre-training and attention mechanisms are introduced to suggest improvements to the traditional residual network; finally, the improved model is trained and tested on the constructed dedicated dataset. The research results show that the improved network model can quickly and accurately identify violence images with an average accuracy rate of 92.20%, thus effectively reducing the cost of manual identification and providing decision support for combating rebel organization activities.

• 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.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.16 no.1
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• pp.21-31
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• 2016

The changes to Ubiquitous Network Environment leads existing learning environment to Smart Learning Environment. Expecially, Smart Learning Environment is in changing paradigm existing teacher centered environment and learner centered environment, recently the demand of Smart Learning Environment for learners are growing up. This study analyzed Learning Environments for Smart Learning Environment focused on the learners through analyzing Ubiquitous Network Environment that is concentrated on the physical aspects and the non-physical aspects. Also, we suggested learning several ways that can be effectively applied based on the environmental characteristics of Smart Learning.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.11 no.5
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• pp.193-201
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• 2011

This paper presents the holistic notion and model of an open social u-learning community, anchored with open content, providing an interactive online study group experience akin to sitting with study buddies on a world-wide campus quad. The interactive social u-learning community design helps conceptualize and maximize advantages of ubiquitous environment in learning. The model is enabled by state-of-the-art web technologies; real-time collaboration technologies for a highly interactive experience; intelligent recommender systems to help learners connect with relevant content and other learners; and mining and analytics to assess learner outcomes. Hence, u-learning design is highly scalable yet interactive and engaging.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.9
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• pp.3169-3185
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• 2021

The majority of students in higher education institutions are among generation Z. They have always depended on e-learning to support their learning activities. Therefore, higher education institutions should provide an attractive e-learning platform. E-learning interface design should be reviewed frequently to smoothen the interaction between students and the e-learning system. It is because interface design that fulfils generation Z students' preferences and expectations may upsurge their participation in e-learning. However, interface design has continually been condemned and turn out to be part of the problem that contributes to the failure of e-learning. Lack of consideration about generation Z students' preferences towards the interface design of e-learning is the factor that leads to these causes. Therefore, this study focused on identifying design characteristics of colour and graphic elements of e-learning from generation Z students' perception. This research involved a purposive sampling method for questionnaire among students of generation Z. The findings from this study could help e-learning developers to design the interface of e-learning that is suitable for generation Z students that will consider color and graphic as important characteristics.

• 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.

• 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.

• The Journal of Information Systems
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• v.15 no.4
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• pp.171-188
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• 2006

Information technology and the Internet have had a dramatic effect on education method and individual life. Universities and companies we making large investments in e-Learning applications but are hard to pressed to evaluate the success of their e-Learning systems. e-Learning can be seen as not only one of Internet based information systems which can provide education services but also one of teaching-teaming methods which can implement self-directed teaming. This paper tests the updated model of information system success proposed by Delone and McLean using a field study of a e-Learning. The five dimensions - information quality, system quality, service quality, user satisfaction, net benefit - of the updated model are parsimonious framework for organizing the e-learning success metrics identified in the literature. Questionaires are collected from 107 students who are enrolling a e-learning class using online survey. The model is tested using SPSS and LISREL. The results show that information quality and service quality are significant predictors of user satisfaction with the e-Learning system but system quality is not. Also user satisfaction is found to be a strong predictor of the learning performance. This strong association between user satisfaction and teaming performance suggests that user satisfaction may serve as a valid surrogate for teaming performance. Empirical testing of the updated DeLone & McLean model should therefore be extended to cover a wider variety of systems.