• Korean Journal of Childcare and Education
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• v.7 no.2
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• pp.115-135
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• 2011

The purpose of this study was to developing a cooperative learning model utilize storytelling technique to the Preliminary childhood teachers whose learning was in order to improve ability and collaboration of making relationships a teams members by the cooperative learning which was performed on the basis of the confidence after understanding themselves and others. To achieve the purpose, the study was performed by dividing into two stages. First, the draft of storytelling based cooperative learning model was proposed by performing a literature survey and a case study. Second, the draft model was applied to the actual work. And the draft was modified and developed to the final model on the basis of the draft model's strenth and implemented to 39 students who were the sophomore of child care education department and enrolled the profession class of at B college P city for 8 weeks. From the implementation result of the model, it was obtained that there was the positive reaction on applying storytelling technique to the beginning stage of cooperative learning. And adding of 'Re-constitution role sharing team' step in the original steps was suggested. In the end, this model was modified and issued based on the research result. The researchers proposed a cooperative learning model storytelling based for Preliminary childhood teachers which consists 6 phases : (1) understanding cooperative learning (2) Building the team and role sharing team (3) theme setting and theme structuralization (4) Re-constitution role sharing team (5) announcement of the results and evaluating (6) reflection of general.

• Journal of Korea Society of Digital Industry and Information Management
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• v.18 no.1
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• pp.125-138
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• 2022

Companies want organizational members who take e-learning courses to enjoy the advantages of transcending time and space that e-learning has, but also want what they have learned to help the organization, the work they perform, or their future careers. In addition, while enjoying the effect of reducing education costs compared to offline education through e-learning, it is expected that executives and employees will apply the knowledge and skills learned to the field and perform tasks to achieve results. As COVID-19 continues, many education programs that have been conducted offline at corporate sites have been converted to e-learning, with a larger number of e-learning operations than in the past. This study was conducted based on the perception that learners' learning satisfaction is important for the successful operation of e-learning education, and that learners' own self-directed learning ability and self-determination are important as well as corporate efforts. As a result of the study, hypotheses 1-1, 1-2, 1-3-1, and 1-3-2 that the better the self-determination (autonomy, competence, full-time support, and peer support) is, the higher the learning satisfaction will be. Both Hypothesis 2-1 and Hypothesis 2-2 were adopted that the better self-directed learning (subjectivity, execution ability) is, the higher the learning satisfaction will increase. In conclusion, it is necessary to properly introduce the concepts of self-determination and self-directed learning in corporate education while operating with the corporate education system.

• Journal of KIBIM
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• v.11 no.2
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• pp.24-32
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• 2021

Recently, starting with smart construction research, interest in technology that automates construction site management using artificial intelligence technology is increasing. In order to automate construction site management, it is necessary to recognize objects such as construction equipment or workers, and automatically analyze the relationship between them. For example, if the relationship between workers and construction equipment at a construction site can be known, various use cases of site management such as work productivity, equipment operation status monitoring, and safety management can be implemented. This study derives a real-time object detection platform architecture that is required when performing construction site management using deep learning technology, which has recently been increasingly used. To this end, deep learning models that support real-time object detection are investigated and analyzed. Based on this, a deep learning model development process required for real-time construction site object detection is defined. Based on the defined process, a prototype that learns and detects construction site objects is developed, and then platform development considerations and architecture are derived from the results.

• Journal of dental hygiene science
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• v.22 no.1
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• pp.9-19
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• 2022

Background: The purpose of this study was to identify the differences in the importance of oral pathology learning objectives for instructors and clinical dental hygienists and provide basic data that can guide learning objectives for acquiring practically necessary basic knowledge in the clinical field. Methods: Through the first-stage expert meeting, 27 items with less than four points out of 129 learning objectives in 15 detailed areas were deleted, 12 additional opinions were reflected, 114 learning objectives were set, and a survey was conducted with 253 people. Results: There were statistically significant differences in 92 items after examining the difference between professors and clinical dental hygienists. Among the areas of inflammation and repair, "Can explain the five symptoms of inflammation" had the highest with a score at 4.76 in the case of the professors. Among the areas of tooth damage, "Can explain abrasion" had the highest with a score at 4.61 in the case of the clinical dental hygienists. Conclusion: I would like to propose the existing 15 detail areas and 129 learning objectives as 14 detail areas and 98 learning objectives and strengthen the job competency of dental hygienists in the future. First, you need to develop competencies that are highly relevant to your work. Second, it is necessary to develop related textbooks and educational materials based on revised learning objectives and competencies. Third, based on revised learning objectives, the dental hygienist national examination should be improved. Through these changes in education, the education of oral and maxillofacial disease subjects should strengthen job competencies among dental hygienists with learning objectives that can be applied to actual clinical practice based on basic knowledge rather than knowledge orientation. In addition, it is possible to improve the quality of dental hygiene studies.

• 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.

• The Journal of the Korea Contents Association
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• v.20 no.7
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• pp.618-628
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• 2020

The purpose of this paper is to propose a new security orchestration service model by combining various security solutions that have been introduced and operated individually as a basis for cyber security framework. At present, in order to respond to various and intelligent cyber attacks, various single security devices and SIEM and AI solutions that integrate and manage them have been built. In addition, a cyber security framework and a security control center were opened for systematic prevention and response. However, due to the document-oriented cybersecurity framework and limited security personnel, the reality is that it is difficult to escape from the control form of fragmentary infringement response of important detection events of TMS / IPS. To improve these problems, based on the model of this paper, select the targets to be protected through work characteristics and vulnerable asset identification, and then collect logs with SIEM. Based on asset information, we established proactive methods and three detection strategies through threat information. AI and SIEM are used to quickly determine whether an attack has occurred, and an automatic blocking function is linked to the firewall and IPS. In addition, through the automatic learning of TMS / IPS detection events through machine learning supervised learning, we improved the efficiency of control work and established a threat hunting work system centered on big data analysis through machine learning unsupervised learning results.

• Journal of Multimedia Information System
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• v.6 no.4
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• pp.165-172
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• 2019

Machine-learning techniques have been actively employed to information security in recent years. Traditional rule-based security solutions are vulnerable to advanced attacks due to unpredictable behaviors and unknown vulnerabilities. By employing ML techniques, we are able to develop intrusion detection systems (IDS) based on anomaly detection instead of misuse detection. Moreover, threshold issues in anomaly detection can also be resolved through machine-learning. There are very few datasets for network intrusion detection compared to datasets for malicious code. KDD CUP 99 (KDD) is the most widely used dataset for the evaluation of IDS. Numerous studies on ML-based IDS have been using KDD or the upgraded versions of KDD. In this work, we develop an IDS model using CSE-CIC-IDS 2018, a dataset containing the most up-to-date common network attacks. We employ deep-learning techniques and develop a convolutional neural network (CNN) model for CSE-CIC-IDS 2018. We then evaluate its performance comparing with a recurrent neural network (RNN) model. Our experimental results show that the performance of our CNN model is higher than that of the RNN model when applied to CSE-CIC-IDS 2018 dataset. Furthermore, we suggest a way of improving the performance of our model.

• The Journal of Korean Association of Computer Education
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• v.8 no.4
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• pp.57-69
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• 2005

In this study we try to design and develop a mixed model of project-based learning with internet. In order to support the developed model, we separately developed a web-based tool, called Project Learning BBS. The classroom teaching for advertisement-photo production with the model has been carried out during May-July in 2002 and March-July in 2003 respectively with 40 students each. The overall activities of group collaborative work done during the teaching periods have been formatively evaluated with classroom observation, interviews and students' portfolios that were related to the processes of photo planning, video shooting and editing. It has also investigated how the students involved the web-based group discussion activities. In short, data analysis indicated that the participants accepted the proposed project-based learning model for their learning events in a positive way in order to increase the utilization of CMC.

• Journal of Korean Society for Quality Management
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• v.47 no.4
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• pp.687-700
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• 2019

Purpose: The purpose of this study is to investigate the interaction effects between price determinants of artworks. We expand the methodology in art market by applying machine learning techniques to estimate the price of artworks and compare linear regression and machine learning in terms of prediction accuracy. Methods: Moderated regression analysis was performed to verify the interaction effects of artistic characteristics on price. The moderating effects were studied by confirming the significance level of the interaction terms of the derived regression equation. In order to derive price estimation model, we use multiple linear regression analysis, which is a parametric statistical technique, and k-nearest neighbor (kNN) regression, which is a nonparametric statistical technique in machine learning methods. Results: Mostly, the influences of the price determinants of art are different according to the auction types and the artist 's reputation. However, the auction type did not control the influence of the genre of the work on the price. As a result of the analysis, the kNN regression was superior to the linear regression analysis based on the prediction accuracy. Conclusion: It provides a theoretical basis for the complexity that exists between pricing determinant factors of artworks. In addition, the nonparametric models and machine learning techniques as well as existing parameter models are implemented to estimate the artworks' price.

• The Journal of Korean Association of Computer Education
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• v.3 no.1
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• pp.51-63
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• 2000

All learners can selection of their work through hypermedia technology in the area provided by usual WBI. Also, it provides learner with individual teaching-learning environment and estimation. RC2 System has the fundamental client/server model, and provides the learning, evaluation algorithms based on the LCPG(Learning Contents Problem Graph) model, the dynamic re-learning mechanism in according to the property of individual. Moreover, it support learning editor to provide interface, which is convenient for teacher, Courseware writer, on the Web