Journal of information and communication convergence engineering
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v.22
no.1
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pp.23-32
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2024
This study aims to improve the quality of distance learning by developing webcast-based e-learning media and integrating it into an e-learning platform for functional job training purposes at the National Research and Innovation Agency, Indonesia. This study uses a Technology Acceptance Model (TAM) to assess and predict user perceptions of information systems using webcast platforms as an alternative to conventional applications. The research method was an online survey using Google Forms. Data collected from 136 respondents involved in practical job training were analyzed using structural equation modeling to test the technology acceptance model. The results showed that the proposed model effectively explained the variables associated with the adoption of web-based e-learning during the COVID-19 pandemic in Indonesia for participants engaged in functional job training. These findings suggest that users' perceptions of ease of use, usefulness, benefits, attitudes, intentions, and webcast usage significantly contribute to the acceptance and use of a more effective and efficient webcast-based e-learning platform.
In the new web based learning environment which has recently emerged, a variety of new learning objectives and teaching methods suited to this learning environment have been adopted. Recently, web based project-based learning methods have received a great deal of attention from those wishing to improve learning performance. The objective of this study is to identify the impact of characteristics of communication media and instruction behavior on collaborative interaction and project performance through web based group projects. The characteristics of communication media were divided into richness, flexibility, and ease of use, and the characteristics of instruction behavior were divided into support and expression, which are independent variables. Collaborative interaction as a mediate variable, was divided into information sharing and negotiation. Project performance was the dependent variable. To verify the proposed research model empirically, an experiment was conducted in which learners participated in on-line and off-line courses with group projects. The group project was conducted virtual product development(VPD), and designed a web-site about the VPD. At the end of the project, a survey was conducted. Of the 270 students, 239 responded. The students were assigned to groups of 3 or 4 members, and represented different genders and levels of computer competence. The reliability, validity, and correlation of research variables were analyzed using SPSS 14.0, and the measurement model and the structural goodness-of-fit of the research model were verified through SEM analysis using Lisrel 8.54. We found important results as follows; First, richness and ease of use has positive impacts on each of sharing information and negotiation. This suggests that richness and ease of use are useful in sharing information which is related to the task and agreeing in opinions among group members. However, flexibility has not positive impacts on sharing information and negotiation. This implies that there is no great difference in performance of PC and information literacy of user. Second, support and expression of instructor have positive impacts on sharing information and negotiation. This indicates that instructors play an important role in encouraging learners to participate in the project and communicating with them, sharing information related to the project, making a resonable decision and finally leading them to improve a project performance. Third, collaborative interaction has a positive impact on project performance. This result shows that if the ability to share information and negotiate among students was improved then a project performance would be improved as well. Recently, in the state of revitalized web based learning, it is opportune that web-based group project is practically conducted, and the impact of characteristics of communication media and characteristics of instruction behavior on sharing information, negotiating among group members and improving a project performance is verified. On the basis of these results, we propose that forms of learning, such as web based project, could be one of solution which is to enforce interaction among learners, and ultimately improve learning performance. Moreover web-based group project is able to make up for a weakness which makes it difficult to make interpersonal relations or friendship among learners in computer mediated communication or web based learning.
The Journal of Korean Association of Computer Education
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v.14
no.6
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pp.31-39
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2011
Information and communication environment includes a lot of dangerous factors to elementary school children who do not establish their own value sense as well as its related knowledge yet. This paper proposes a case based multiplex teaching and learning model which leads students to participate and be interested in, then to improve the educational level of information and communication ethics in elementary school. This model progresses in centric of the conflict situations in information and communication ethics that can be commonly occurred in actual life. To adapt case based approach, three types of teaching and learning models, value conflict, value clarification, and role playing, are applied in a combined form. Along with applying the models, their educational effects have been compared and analysed in time sequence, in the educational level improvement point of view. The verification has been conducted by using surveys and questionnaires, in the four areas; cognitive, affective, behavioral, and then overall. The verification results show that the proposed method is effective to improve the educational level of information and communication ethics in elementary schooll.
International Journal of Internet, Broadcasting and Communication
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v.13
no.2
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pp.231-243
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2021
In Recent years the way we analyze the breast cancer has changed dramatically. Breast cancer is the most common and complex disease diagnosed among women. There are several subtypes of breast cancer and many options are there for the treatment. The most important is to educate the patients. As the research continues to expand, the understanding of the disease and its current treatments types, the researchers are constantly being updated with new researching techniques. Breast cancer survival rates have been increased with the use of new advanced treatments, largely due to the factors such as earlier detection, a new personalized approach to treatment and a better understanding of the disease. Many machine learning classification models have been adopted and modified to diagnose the breast cancer disease. In order to enhance the performance of classification model, our research proposes a model using A Hybrid Modified K-Means Clustering with Modified SVM (Support Vector Machine) Machine learning algorithm to create a new method which can highly improve the performance and prediction. The proposed Machine Learning model is to improve the performance of machine learning classifier. The Proposed Model rectifies the irregularity in the dataset and they can create a new high quality dataset with high accuracy performance and prediction. The recognized datasets Wisconsin Diagnostic Breast Cancer (WDBC) Dataset have been used to perform our research. Using the Wisconsin Diagnostic Breast Cancer (WDBC) Dataset, We have created our Model that can help to diagnose the patients and predict the probability of the breast cancer. A few machine learning classifiers will be explored in this research and compared with our Proposed Model "A Hybrid Modified K-Means with Modified SVM Machine Learning Algorithm to Enhance the Cancer Prediction" to implement and evaluated. Our research results show that our Proposed Model has a significant performance compared to other previous research and with high accuracy level of 99% which will enhance the Cancer Prediction.
The objective of this study is to identify the impacts of communication reinforcement on performance of learning in Web-PBL. Communication reinforcement is defined as the combination of information sharing and co-construction. As factors facilitating communication reinforcement, we propose learner's characteristics, task characteristics, and group characteristics. Learner's characteristics are collaboration-orientation, openness, holistic approach, and online community-orientation which reflects e-learning environment. Collaboration-oriented tasks as group projects were developed and given to groups with 5-6 members. The group characteristics are categorized into 'horizontal' and 'vertical', according to the patterns of communication between a group leader and members. To verify empirically the proposed research model, an experimental design was performed to learners who took on-line and off-line courses with group projects. We found important results as follows; First, field dependence has positive impacts on information sharing, and online community-orientation has positive impacts on co-construction. These results correspond with prior studies on relationship between field dependence and collaborative learning. Second, collaboration-oriented task directly impacts on information sharing, and indirectly affects co-construction, This result implicates that information sharing is pre-requisite of co-construction. Third, 'horizontal' was identified as a factor giving positive effects on information sharing and co-construction. This result implies that horizontal communication is very important to facilitate communication reinforcement.
Journal of the Korea Society of Computer and Information
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v.28
no.10
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pp.197-205
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2023
This study is to apply the Havruta teaching-learning method to college students' major classes and analyze the relationship between the effectiveness evaluation of communication skills and sub-factors using a structural equation model. As a result of the study, the communication ability score was different before and after Havruta teaching-learning, and it was found that after Havruta teaching-learning was higher than before Havruta teaching-learning. The path effect was found to be significant in all of the total, direct, and indirect effects among latent variables, except for the relationship between interpretation ability, role-playing ability, and goal-setting ability in the direct effect. In this study, it was found that the Havruta teaching-learning method not only improves creativity and thinking ability, but also improves self-directed learning ability. In addition, it was reconfirmed that it is a teaching-learning method that can develop social skills and communication skills as well as problem-solving skills while experiencing opinions different from one's own. As a result, research on a thorough student-centered teaching-learning method suitable for the Homo Machina era must be continued and its application in the educational field must be implemented.
The Journal of Korean Association of Computer Education
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v.9
no.3
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pp.97-107
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2006
The deviant behaviors on cyberspace in Korea are increased recently because of the unbalanced development between the social ethics and the technological improvement on information and communication technology. To overcome this, educations on information and communication ethics are executed in school. However most of the educations is to deliver the knowledge on the disfunction of informationization and rules of netiquette without the analysis of the psychological characteristics on the cyberspace. Therefore, educations on information and communication ethics, in which the changes of social behaviors and value attitude are essential, is ineffective by making the students as passive and enervated users. In this research, we developed a teaching-learning model for cultivating consciousness of information and communication ethics by the establishment of ego identity on cyberspace and then proved the effectiveness by the experiment in school. Students can raise their abilities on active dealing with the given situation and context according to this teaching-learning model.
For the mobile edge computing (MEC) system supporting dense network, a joint allocation algorithm of computing and communication resources based on reinforcement learning is proposed. The energy consumption of task execution is defined as the maximum energy consumption of each user's task execution in the system. Considering the constraints of task unloading, power allocation, transmission rate and calculation resource allocation, the problem of joint task unloading and resource allocation is modeled as a problem of maximum task execution energy consumption minimization. As a mixed integer nonlinear programming problem, it is difficult to be directly solve by traditional optimization methods. This paper uses reinforcement learning algorithm to solve this problem. Then, the Markov decision-making process and the theoretical basis of reinforcement learning are introduced to provide a theoretical basis for the algorithm simulation experiment. Based on the algorithm of reinforcement learning and joint allocation of communication resources, the joint optimization of data task unloading and power control strategy is carried out for each terminal device, and the local computing model and task unloading model are built. The simulation results show that the total task computation cost of the proposed algorithm is 5%-10% less than that of the two comparison algorithms under the same task input. At the same time, the total task computation cost of the proposed algorithm is more than 5% less than that of the two new comparison algorithms.
KSII Transactions on Internet and Information Systems (TIIS)
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v.13
no.8
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pp.4285-4299
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2019
Adversarial attack is a technique that causes a malfunction of classification models by adding noise that cannot be distinguished by humans, which poses a threat to a deep learning model. In this paper, we propose an efficient method to detect adversarial images using Gaussian process regression. Existing deep learning-based adversarial detection methods require numerous adversarial images for their training. The proposed method overcomes this problem by performing classification based on the statistical features of adversarial images and clean images that are extracted by Gaussian process regression with a small number of images. This technique can determine whether the input image is an adversarial image by applying Gaussian process regression based on the intermediate output value of the classification model. Experimental results show that the proposed method achieves higher detection performance than the other deep learning-based adversarial detection methods for powerful attacks. In particular, the Gaussian process regression-based detector shows better detection performance than the baseline models for most attacks in the case with fewer adversarial examples.
KSII Transactions on Internet and Information Systems (TIIS)
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v.9
no.10
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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.
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