• Journal of the Korea Convergence Society
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• v.12 no.10
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• pp.145-150
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• 2021

The trend of learning as an online study platform continues, and motivation of learners for continued use of the platform is also becoming important. In addition to learning, various services use gamification techniques that excite users for continued use. This study analyzes successful service cases and proposes guidelines applicable to learning platforms to present ways to increase learners' interest and efficiency in online study platforms using reward systems and storytelling techniques among gamification elements. Based on the three guidelines drawn through this study, it can contribute to the digitization of educational infrastructure by increasing the immersion of learners within the online study platform that will be more commercialized in the future.

• Korean Journal of Adult Nursing
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• v.12 no.3
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• pp.418-430
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• 2000

Nurses in today's challenging health care settings need to be skilled critical thinkers and clinical experts. The nurse must be able to use a broad knowledge base to mobilize resources, coordinate actions and evaluate outcomes in complex new situations. So the national licensing examination for registered nurses is change to improve the quality of professional competency of nurses in Korea. Prior to this, learning objectives should be developed and improved periodically. The purpose of this study is to describe the nursing competency to provide base line data for developing learning objectives in adult health nursing. This study was conducted by means of a questionnaire which was developed by the researcher after reviewing the literature. The questionnaire was based on learning objectives which were developed by a nation-wide nursing faculty majoring in adult health nursing. The subjects were 45 nurses in a middle level hospital. The collected data were treated using SPSS Win 7.5 Statistical Package so as to obtain such descriptive statistics as mean score, frequency, and to test reliability test, nonpar-Friedman test. To summarize the major findings in this study, it showed the scope of nursing competency and can guide the direction of study and methodological criteria to develop learning objectives. Recommendations for further research are: firstly, it is necessary to state learning objectives with learners' behavioral terminology; secondly, to overcome locality in scope of this study, there is a need to analyze with nation-wide sampling by an in-depth statistical analysis; thirdly, because the subjects of this study are mostly three-year graduate nurses, there is a need to compare this study with other studies of different subjects.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.12
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• pp.1588-1594
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• 2020

In this paper, we discuss a data balancing method for learning a neural network that generates digital holograms using a deep neural network (DNN). Deep neural networks are based on deep learning (DL) technology and use a generative adversarial network (GAN) series. The fringe pattern, which is the basic unit of a hologram to be created through a deep neural network, has very different data types depending on the hologram plane and the position of the object. However, because the criteria for classifying the data are not clear, an imbalance in the training data may occur. The imbalance of learning data acts as a factor of instability in learning. Therefore, it presents a method for classifying and balancing data for which the classification criteria are not clear. And it shows that learning is stabilized through this.

• (The) Korean Journal of Educational Psychology
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• v.31 no.4
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• pp.819-845
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• 2017

Although testing is usually done for purposes of assessment, previous research over the past 100 years indicates that testing is an effective tool for learning. Testing or retrieval practice of previously studied materials can enhance learning of that previously studied information and/or learning of subsequently presented new information. The former is referred to as the backward effect of testing whereas the latter is referred to as the forward effect of testing. Thus far, however, the literature has not isolated these two effects and most previous research focused on the backward effect. Only recent laboratory research provided evidence that there is a forward effect of testing. The present study provides a review of research on this forward and backward effect of testing, focusing on testing procedures of the effects, empirical evidence, current theoretical explanations, and issues to resolve in order to make use of testing effect in educational settings. The reviews clearly show that testing enhances memory of previously learned information by working as memory modifier and learning of newly presented information by affecting learners' metacognition, implying that testing is not just an assessment of learning, but also an effective tool for learning.

• Journal of ILASS-Korea
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• v.27 no.2
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• pp.57-65
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• 2022

The purpose of this study is to use machine learning to build a model capable of predicting the flash boiling spray characteristics. In this study, the flash boiling spray was visualized using Shadowgraph visualization technology, and then the spray image was processed with MATLAB to obtain quantitative data of spray characteristics. The experimental conditions were used as input, and the spray characteristics were used as output to train the machine learning model. For the machine learning model, the XGB (extreme gradient boosting) algorithm was used. Finally, the performance of machine learning model was evaluated using R² and RMSE (root mean square error). In order to have enough data to train the machine learning model, this study used 12 injectors with different design parameters, and set various fuel temperatures and ambient pressures, resulting in about 12,000 data. By comparing the performance of the model with different amounts of training data, it was found that the number of training data must reach at least 7,000 before the model can show optimal performance. The model showed different prediction performances for different spray characteristics. Compared with the upstream spray angle and the downstream spray angle, the model had the best prediction performance for the spray tip penetration. In addition, the prediction performance of the model showed a relatively poor trend in the initial stage of injection and the final stage of injection. The model performance is expired to be further enhanced by optimizing the hyper-parameters input into the model.

• Journal of Internet Computing and Services
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• v.24 no.4
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• pp.85-92
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• 2023

Deep learning applications are increasingly being leveraged for disease detection tasks in medical imaging modalities such as X-ray, Computed Tomography (CT), and Magnetic Resonance Imaging (MRI). Most data-centric deep learning challenges necessitate the use of supervised learning methodologies to attain high accuracy and to facilitate performance evaluation through comparison with the ground truth. Supervised learning mandates a substantial amount of image and label sets, however, procuring an adequate volume of medical imaging data for training is a formidable task. Various data augmentation strategies can mitigate the underfitting issue inherent in supervised learning-based models that are trained on limited medical image and label sets. This research investigates the enhancement of a deep learning-based rib fracture segmentation model and the efficacy of data augmentation techniques such as left-right flipping, rotation, and scaling. Augmented dataset with L/R flipping and rotations(30°, 60°) increased model performance, however, dataset with rotation(90°) and ⨯0.5 rescaling decreased model performance. This indicates the usage of appropriate data augmentation methods depending on datasets and tasks.

• Convergence Security Journal
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• v.23 no.5
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• pp.117-123
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• 2023

Recently, as the demand for Generative Artificial Intelligence (AI) and artificial intelligence has increased, the seriousness of misuse and abuse has emerged. However, intelligent CCTV, which maximizes detection of abnormal behavior, is of great help to prevent crime in the military and police. AI performs learning as taught by humans and then proceeds with self-learning. Since AI makes judgments according to the learned results, it is necessary to clearly understand the characteristics of learning. However, it is often difficult to visually judge strange and abnormal behaviors that are ambiguous even for humans to judge. It is very difficult to learn this with the eyes of artificial intelligence, and the result of learning is very many False Positive, False Negative, and True Negative. In response, this paper presented standards and methods for clarifying the learning of AI's strange and abnormal behaviors, and presented learning measures to maximize the judgment ability of intelligent CCTV's False Positive, False Negative, and True Negative. Through this paper, it is expected that the artificial intelligence engine performance of intelligent CCTV currently in use can be maximized, and the ratio of False Positive and False Negative can be minimized..

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.35 no.2
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• pp.135-156
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• 2024

Librarians facing the new information environment need to actively cope with knowledge and skills, but librarians in outsourced public libraries are burdened with participation in external education due to concerns about work gaps or inadequate institutional support, making it difficult to continue their education. Therefore, in this study, in order to create an environment in which librarians can grow together, the current status of the activities of the learning community was checked and the effects were identified. An online survey was conducted from May 20 to 22, 2023, targeting librarians who had participated in learning community activities among librarians belonging to the Gangnam-gu Library, and 101 copies were distributed to collect 81 copies and use them as analysis data. As a result of the study, it was confirmed that librarians are satisfied with the activities of the learning community and have a high level of understanding and empathy for their necessity. Therefore, as a way to revitalize the learning community, we propose to form a culture that promotes collaboration among members, share visions and values for learning community activities, and provide institutional support.

• Journal of Korean society of Dental Hygiene
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• v.22 no.5
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• pp.323-332
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• 2022

Objectives: This study aims to confirm the educational necessity and utilization of VR media. And it was conducted to prepare basic data necessary for the use of VR in various dental hygiene education in the future and the development of innovative practical training courses. Methods: Before and after using VR in oral radiology practice classes, learning interest (4 items), learning commitment (9 items), learning motivation (5 items), educational media preference (4 items), and satisfaction (10 items) were investigated and analyzed. Friedman two way ANOVA by ran a nonparametric analysis corresponding to repeated measures ANOVA was performed. The statistical significance level was 0.05. Results: It was found that there were statistically significant differences in learning interest, learning immersion, and learning motivation according to the type of oral radiology practice education medium (p<0.05). Conclusions: VR is expected that the use of learning media using VR will lead to students' interest, immersion, and learning motivation in class, and that positive learning effects on VR education media can be sufficiently obtained.

• The Journal of Korean Association of Computer Education
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• v.8 no.1
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• pp.25-32
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• 2005

An analysis of the case study was reported to pioneer the perceptions of teachers, students, and parents for the educational use of tablet computers. The findings showed that the amount of learning, various learning activities, interest, and motivation of learners increased and that the teachers perceived the potentiality of the tablet computer and the necessity of a training for designing mobile-based instructions.