• Journal of the Korea Institute of Building Construction
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• v.21 no.6
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• pp.689-700
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• 2021

Due to the COVID-19 pandemic, higher education in South Korea has rapidly shifted online. In addition, the advent of the Fourth Industrial Revolution has highlighted the need to introduce innovation teaching methods. To cope with these changes and demands, the learning management system used by domestic universities must therefore be improved. This study developed a learning management system for the application of team-based learning to improve and develop the learning management system of domestic universities. The major research findings are as follows. The analysis results showed that there is not sufficient function to apply new innovation teaching methods, such as team-based learning, in the current learning management system of domestic universities. Therefore, a learning management system capable of supporting team-based learning courses was developed in this study. The system consisted of 'pre-class learning,' 'team activities' and 'participation learning,' which were further classified into nine sub-items. In the future, a survey of system users is planned in order to further improve the system. It is believed that if the proposed learning management system were introduced to domestic universities, it would improve the educational environment and contribute to the spread and use of innovative teaching methods.

• Journal of The Korean Association For Science Education
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• v.41 no.2
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• pp.133-144
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• 2021

The purpose of this study is to explore the teaching and learning experiences to improve the positive experiences about science (PES) of scientifically-gifted elementary school students. For this study, 36 students in grades 5~6 at a gifted science education institutes in Seoul were selected and conducted pre-test and post-test for 'Test for Indicators of Positive Experiences about Science (TIPES).' After the weekly science classes of the institutions were over, individual in-depth interviews were also conducted with some students to explore the teaching and learning experiences for improving their PES. The analysis of the results reveals that the science classes for scientifically-gifted students have been shown to improve PES of scientifically-gifted elementary school students. The teaching and learning experiences to improve their PES included eight teaching and learning experiences that appeared in general elementary school students of previous study such as 'practice-centered exploratory activities,' 'student-led class,' 'positive and professional feedback,' 'construction of knowledge through exploration,' 'class considering student's interest and aptitude,' 'use of materials related to real life,' 'smooth communication and collaboration in group activities,' and 'appropriate difficulty in learning content.' There were also six teaching and learning experiences that newly emerged from science-gifted students: 'Learning experience through the strategies for improving scientific creativity,' 'inquiry experience as a little scientist,' 'advanced or accelerated learning experience,' 'learning experience with excellent students,' 'experience helping other students,' and 'experience with high or low achievement'. Based on these results, the practical implications for improving the student's PES are suggested.

• School Mathematics
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• v.2 no.1
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• pp.29-51
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• 2000

In this study, we tried to suggest an example of the analysis on the use of mathematical manipulatives. Taking algebra tiles as an example of mathematical manipulatives, we analysed several effects resulted from the use of algebra tiles. The algebra tiles make it possible to do activities that are needed to introduce and explain the distributive law and factoring. The algebra tiles have a several advantages; First of all, This model is simple. Even though they cannot make algebra easy, this model can play an important role in the transition to a new algebra course. This model provides access to symbol manipulation for students who had previously been frozen out of the course because of their weak number sense. This model provides a geometric interpretation of symbol manipulation, thereby enriching students' understanding, This model supports cooperative learning, and help improve discourse in the algebra class by giving students objects to think with and talk about. On the other hand, The disadvantages of this model are as follows; the model reinforces the misconception that -x is negative, and x is positive; the area model of multiplication is not geometrically sound when minus is involved; only the simplest expressions involving minus can be represented; It is ineffective when be used the learning of already known concept. Mathematics teachers must have a correct understanding about these advantages and disadvantages of manipulatives. Therefore, they have to plan classroom work that be maximized the positive effect of manipulatives and minimized the negative effect of manipulatives.

• Journal of Korea Society of Digital Industry and Information Management
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• v.20 no.1
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• pp.75-85
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• 2024

This study explored the overall perception of teaching media from the perspective of pre-service teachers. For this purpose, reflection journals written by 138 pre-service teachers were analyzed, and positive and negative perceptions of teaching media were explored. In the reflection diary, pre-service teachers were asked to write down examples of appropriate use of instructional media and the effects of their experiences, cases of inappropriate use of instructional media, and areas requiring improvement. The main research results are as follows. First, the positive aspects of teaching media perceived by prospective teachers were that it presented examples that could help understanding, aroused curiosity to focus attention, and encouraged participation to motivate learning. Second, the negative aspects of teaching media perceived by prospective teachers include cases where the playback time of the video containing learning content is excessively long and the content of the video is unclear or is higher than the learner's level and difficult to understand. This study is significant in that it presents pre-service teachers experiences and perceptions of teaching media and their desirable use methods.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.1
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• pp.69-74
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• 2005

In this paper, we suggested user assistant soft computing method for 3D effect optimization. In order to maximize 3D effect of image, intervals among cameras have to be set up properly according to distance between cameras and an object. Two data such as interval and distance was obtained to use in neural network as the data for learning. However, if the data for learning was obtained by only human's subjective views, it could be that the obtained data was not optimal for learning because the data had an accidental ewer To obtain optimal data lot learning, we added candidature data to obtained data through data analysis, and then selected the most proper data between the candidature data and the obtained data for learning in neural network. Usually, 3D effect of image was affected by both distance from an object to cameras and an object size. Therefore, we suggested fuzzy inference model which was able to represent two factors like distance and size. Candidature data was added by fuzzy model. In the simulation result, we verified that the mote the obtained data was affected by human's subjective views, the more effective the suggested system was.

• Journal of Korean Academy of Fundamentals of Nursing
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• v.12 no.3
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• pp.290-297
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• 2005

Purpose: This study was done to compare the effectiveness of web learning versus traditional education for learning Asepsis technique in Basic Nursing Practice. Method: This research was a quasi-experimental study. The participants were 60 students who were taking Basic Nursing at a nursing college in G city. Thirty students each were assigned to the experimental and the control group. Data were collected between September 26 and 30, 2005, The data were analyzed using descriptive statistics, Independent t-test and ANCOVA Results: The mean score for competence in Asepsis technique practice in the experimental group was 24.73 and in the control group 22.80. This result was statistically significant(t = 3.723, P = .001). The mean score for knowledge in the experimental group was 42.93 and in the control group 38.93. This result was statistically significant(t = 2.119, P = .038). The mean score for satisfaction with degree of study in the experimental group was 55.50 for the pre-test and 56.17 for the post-test, and in the control group 60.23 and 58.00 respectively. This result was statistically significant(F = 10.537, P = .002). Conclusion: The Web based learning was found to be effective in nursing practice, student satisfaction and knowledge, but web-based programs have to be continuously managed and various demands from learners have to be accepted. Eventually we have to use web-based program as educational medium to promote effective learning nursing education.

• Journal of Platform Technology
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• v.8 no.1
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• pp.16-23
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• 2020

For protecting copyright for trademark, convolutional neural network can be used to confirm genuine of trademark image. For this, repeated training one trademark image degrades the performance of machine learning because of overfitting problem. Therefore, this type of machine learning application generates training data in various way. But if genuine trademark image is rotated, this image is classified as not genuine trademark. In this paper, we propose the way for extending training data to confirm genuine of trademark image which is rotated. Our proposed way generates rotated image from genuine trademark image as training data. To show effectiveness of our proposed way, we use CNN machine learning model, and evaluate the accuracy with test image. From evaluation result, our way can be used to generate training data for machine learning application which confirms genuine of rotated trademark image.

• Journal of the Korea Society of Computer and Information
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• v.25 no.12
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• pp.165-172
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• 2020

Recently, due to exponential growth of access information on the web, the importance of predicting a user's next web page use has been increasing. One of the methods that can be used for predicting user's next web page is deep learning. To predict next web page, web logs are analyzed by data preprocessing and then a user's next web page is predicted on the output of the analyzed web logs using a deep learning algorithm. In this paper, we propose a framework for web page prediction that includes methods for web log preprocessing followed by deep learning techniques for web prediction. To increase the speed of preprocessing of large web log, a Hadoop based MapReduce programming model is used. In addition, we present a web prediction system that uses an efficient deep learning technique on the output of web log preprocessing for training and prediction. Through experiment, we show the performance improvement of our proposed method over traditional methods. We also show the accuracy of our prediction.

• Journal of IKEEE
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• v.26 no.3
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• pp.341-348
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• 2022

Wi-Fi Sensing, which uses Wi-Fi technology to sense the surrounding environments, has strong potentials in a variety of sensing applications. Recently several advanced deep learning-based solutions using CSI (Channel State Information) data have achieved high performance, but it is still difficult to use in practice without explicit data collection, which requires expensive adaptation efforts for model retraining. In this study, we propose a Channel State Information Automatic Labeling System (CALS) that automatically collects and labels training CSI data for deep learning-based Wi-Fi sensing systems. The proposed system allows the CSI data collection process to efficiently collect labeled CSI for labeling for supervised learning using computer vision technologies such as object detection algorithms. We built a prototype of CALS to demonstrate its efficiency and collected data to train deep learning models for detecting the presence of a person in an indoor environment, showing to achieve an accuracy of over 90% with the auto-labeled data sets generated by CALS.

• Journal of the Korean Society of Marine Environment & Safety
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• v.24 no.7
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• pp.870-874
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• 2018

In the worst maritime accidents, people should abandon ship, but ship structures are narrow and complex and operation takes place on rough seas, so escape is not easy. In particular, passengers on cruise ships are untrained and varied, making evacuation prospects worse. In such a case, the evacuation management of the crew plays a very important role. If a rescuer enters a ship at distress and conducts rescue activities, which zones represent the most effective entry should be examined. Generally, crew and rescuers take the shortest route, but if an accident occurs along the shortest route, it is necessary to select the second-best alternative. To solve this situation, this study aims to calculate evacuation routes using Q-Learning of Reinforcement Learning, which is a machine learning technique. Reinforcement learning is one of the most important functions of artificial intelligence and is currently used in many fields. Most evacuation analysis programs developed so far use the shortest path search method. For this reason, this study explored optimal paths using reinforcement learning. In the future, machine learning techniques will be applicable to various marine-related industries for such purposes as the selection of optimal routes for autonomous vessels and risk avoidance.