• Journal of the Korea Convergence Society
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• v.10 no.10
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• pp.275-281
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• 2019

This study aimed to analyse the mediating effects of learning attitudes in respect to the correlation between achievement goal orientation and learning flow. For the purpose, this study surveyed achievement goal orientation, learning flow and learning attitude of beauty specialized high school students located in Jeonnam with the use of a questionnaire. 335 copies of the answers were analysed with the use of the SPSS(PASW, ver 21.0). The results are presented as follows: Mastery approach of the achievement goal orientation and learning attitude had a significant effect on learning flow. It was discovered that learning attitude had a partial effect on the relations between mastery approach and learning flow, but it was reported that it had a complete mediating effect on the relations between performance avoidance and learning flow.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.2
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• pp.742-756
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• 2022

A flood of information has occurred with the rise of the internet and digital devices in the fourth industrial revolution era. Every millisecond, massive amounts of structured and unstructured data are generated; smartphones, wearable devices, sensors, and self-driving cars are just a few examples of devices that currently generate massive amounts of data in our daily. Machine learning has been considered an approach to support and recognize patterns in data in many areas to provide a convenient way to other sectors, including the healthcare sector, government sector, banks, military sector, and more. However, the conventional machine learning model requires the data owner to upload their information to train the model in one central location to perform the model training. This classical model has caused data owners to worry about the risks of transferring private information because traditional machine learning is required to push their data to the cloud to process the model training. Furthermore, the training of machine learning and deep learning models requires massive computing resources. Thus, many researchers have jumped to a new model known as "Federated Learning". Federated learning is emerging to train Artificial Intelligence models over distributed clients, and it provides secure privacy information to the data owner. Hence, this paper implements Federated Averaging with a Deep Neural Network to classify the handwriting image and protect the sensitive data. Moreover, we compare the centralized machine learning model with federated averaging. The result shows the centralized machine learning model outperforms federated learning in terms of accuracy, but this classical model produces another risk, like privacy concern, due to the data being stored in the data center. The MNIST dataset was used in this experiment.

• Journal of the Korea Convergence Society
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• v.9 no.7
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• pp.355-364
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• 2018

The purpose of this study is to investigate the effect of simulation practice combining problem-based learning on nursing knowledge, self-confidence, critical thinking tendency and problem solving ability. The subjects of this study were 45 students who took two courses of nursing situation practice in the fourth grade of S university in S area. Data were collected before and after the simulation exercise using the self-report questionnaire. The problem-based learning and the simulation practice convergence training were conducted for three weeks with two hours per week, and the post- integration nursing knowledge, self-confidence, critical thinking disposition, and problem solving ability scores were improved. Nursing knowledge, self-confidence, and critical thinking tendency were positively correlated with problem solving ability and proved the effectiveness of problem-based learning and simulation training. Therefore, education programs combining problem-based learning and simulation training on various topics should be developed and utilized.

• Journal of Digital Convergence
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• v.15 no.4
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• pp.243-250
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• 2017

Since the global financial crisis in 2008, countries around the world have emphasized the activation of entrepreneurship and entrepreneurship as essential strategies for survival. In the case of developed countries, the United States, the European Union and China actively promote entrepreneurship and entrepreneurship. In Korea, on the other hand, the importance of entrepreneurship is emphasized by stagnating growth of companies and strengthening their competitiveness through convergence within companies or companies. The purpose of this study is to investigate the relationship between entrepreneurship and learning orientation in order to enhance competitiveness of Korean companies and to be the basis of management strategy for growth of convergence companies based on this. For the progress of this study, the convergence companies were targeted and the hypothesis was verified through the questionnaire survey through the statistical program. The results of the study showed that Innovation influenced learning orientation. Second, Initiative was found to affect learning orientation. Finally, it is shown that risk sensitivity does not affect learning orientation.

• Journal of the Korea Convergence Society
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• v.10 no.11
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• pp.181-188
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• 2019

The purpose of this study is to implement the convergence thinking in problem-based learning (PBL) on paramedic education. PBL scenario course was conducted for 78 students in the third year of emergency medical technology during the first semester of 15 weeks in 2017. After 15 weeks, data of 73 students were analyzed. Among questions about learning interest in PBL, 'neutral' was the most frequent response with 38% for "PBL scenario classes were more effective in learning and acquiring knowledge than lecture class". For "The lessons learned in the class helped to improve the ability to come up with appropriate solutions for problem solving", 57.5% responded 'agree', and for "The lessons learned in the class helped with confidence in the emergency scene", 50.7% responded 'agree'. PBL will be an effective and efficient way of teaching as a learning curriculum for understanding the field situation.

• Journal of the Korea Convergence Society
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• v.7 no.3
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• pp.121-129
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• 2016

This study explains the fundamental problems of MOOC (Massive Open Online Course) based on major survey results for MOOC and online courses conducted in the United States. Consequently, this study integrates the following conclusions and suggestions on how to improve MOOC and convergence of MOOC and universities under the current IT technology. First, the division into small sized classes will solve the problem of massiveness with MOOC. The problem of openness will be solved by providing differentiated courses based on placement tests; and the weakness of onlineness can be complemented through Flipped Learning methodologies. Second, in convergence of universities and MOOC, there are two desirable approache s: credit-free courses use the improved MOOC suggested in this study while credit courses are conducted by Flipped Learning, based on core online courses within departments. In addition, the credit courses offer intensive and supplementary ones together if possible. Third, MOOC will be utilized more widely as it will offer differentiated courses and be produced by education-based universities.

• Journal of Convergence for Information Technology
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• v.9 no.7
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• pp.106-113
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• 2019

The purpose of this paper is aimed to derive suggestions for convergence technology for effective management of distance education by analyzing a long-term case. The experiment was designed with notebook, smartphone or tablet based robot controlled by a remote instructor and a learner, who have experience of distance learning including robot assisted learning. The tablet based robot has the display system of feedback to speakers. During five months, three types of experiments were conducted randomly and a participant was interviewed thoroughly. The result, like the previous research, demonstrates that the task performance of the learner in telepresence robot-assisted learning was better than that in the notebook, and smartphone based. However, it is believed to be necessary to adjust the system for eye-contact and voice transmission for the remote instructor. The instructor required an additional sight by supplementing an extra camera and automatic direction control to source of sound.

• Journal of The Korean Society of Agricultural Engineers
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• v.64 no.1
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• pp.15-26
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• 2022

3D open-field farm model developed from UAV (Unmanned Aerial Vehicle) data could make crop monitoring easier, also could be an important dataset for various fields like remote sensing or precision agriculture. It is essential to separate crops from the non-crop area because labeling in a manual way is extremely laborious and not appropriate for continuous monitoring. We, therefore, made a 3D open-field farm model based on UAV images and developed a crop segmentation model using a supervised machine learning algorithm. We compared performances from various models using different data features like color or geographic coordinates, and two supervised learning algorithms which are SVM (Support Vector Machine) and KNN (K-Nearest Neighbors). The best approach was trained with 2-dimensional data, ExGR (Excess of Green minus Excess of Red) and z coordinate value, using KNN algorithm, whose accuracy, precision, recall, F1 score was 97.85, 96.51, 88.54, 92.35% respectively. Also, we compared our model performance with similar previous work. Our approach showed slightly better accuracy, and it detected the actual crop better than the previous approach, while it also classified actual non-crop points (e.g. weeds) as crops.

• Advanced Industrial SCIence
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• v.3 no.1
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• pp.1-16
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• 2024

The purpose of this study is to develop a machine learning-based AI convergence class model and class design principles that can foster data literacy in high school students, and to develop detailed guidelines accordingly. We developed a machine learning-based teaching model, design principles, and detailed guidelines through research on prior literature, and applied them to 15 students at a specialized high school in Seoul. As a result of the study, students' data literacy improved statistically significantly (p<.001), so we confirmed that the model of this study has a positive effect on improving learners' data literacy, and it is expected that it will lead to related research in the future.

• International Journal of Control, Automation, and Systems
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• v.6 no.2
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• pp.171-179
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• 2008

This paper addresses the synthesis of an iterative learning controller for a class of linear systems with norm-bounded parameter uncertainties. We take into account an iterative learning algorithm with current cycle feedback in order to achieve both robust convergence and robust stability. The synthesis problem of the developed iterative learning control (ILC) system is reformulated as the ${\gamma}$-suboptimal $H_{\infty}$ control problem via the linear fractional transformation (LFT). A sufficient convergence condition of the ILC system is presented in terms of linear matrix inequalities (LMIs). Furthermore, the ILC system with fast convergence rate is constructed using a convex optimization technique with LMI constraints. The simulation results demonstrate the effectiveness of the proposed method.