• Korean Security Journal
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• no.51
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• pp.61-78
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• 2017

This study aims to empirically validate the relationship between organizational learning transfer climate and the transfer of training and to enhance the transfer of training among South Korean coast guards. The empirical data was collected through 526 South Korean coast guards admitted to the institute, and support by managers and peers, and potential for organizational change were selected as independent variables for multiple regression. As a result, the transfer of training is positively correlated with support of mangers and peers, and potential for organizational change, thus suggesting factors like supervisor participation and long-term educational planning as policy implications for the effective transfer of training to work environment. Though findings from research cannot be generalized to the broader population due to limitations of sampling, this study does find its significance in that organizational learning transfer climate was considered as a key factor influencing the transfer of learning for the first time.

• The Journal of Information Systems
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• v.30 no.2
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• pp.29-56
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• 2021

Purpose Cyber deviance of adolescents has become a serious social problem. With a widespread use of smartphones, incidents of cyber deviance have increased in Korea and both quantitative and qualitative damages such as suicide and depression are increasing. Research has been conducted to understand diverse factors that explain adolescents' delinquency in cyber space. However, most previous studies have focused on a single theory or perspective. Therefore, this study aims to comprehensively analyze motivations of juvenile cyber deviance and to develop a predictive model for delinquent adolescents by integrating four different theories on cyber deviance. Design/methodology/approach By using data from Korean Children & Youth Panel Survey 2010, this study extracts 27 potential factors for cyber deivance based on four background theories including general strain, social learning, social bonding, and routine activity theories. Then this study employs econometric analysis to empirically assess the impact of potential factors and utilizes a machine learning approach to predict the likelihood of cyber deviance by adolescents. Findings This study found that general strain factors as well as social learning factors have positive effects on cyber deviance. Routine activity-related factors such as real-life delinquent behaviors and online activities also positively influence the likelihood of cyber diviance. On the other hand, social bonding factors such as community commitment and attachment to community lessen the likelihood of cyber deviance while social factors related to school activities are found to have positive impacts on cyber deviance. This study also found a predictive model using a deep learning algorithm indicates the highest prediction performance. This study contributes to the prevention of cyber deviance of teenagers in practice by understanding motivations for adolescents' delinquency and predicting potential cyber deviants.

• Proceedings of the National Institute of Ecology of the Republic of Korea
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• v.4 no.4
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• pp.159-176
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• 2023

The conservation of the raccoon dog (Nyctereutes procyonoides) in South Korea requires the protection and preservation of natural habitats while additionally ensuring coexistence with human activities. Applying habitat map modeling techniques provides information regarding the distributional patterns of raccoon dogs and assists in the development of future conservation strategies. The purpose of this study is to generate potential habitat distribution maps for the raccoon dog in South Korea using geospatial technology-based models. These models include the frequency ratio (FR) as a bivariate statistical approach, the group method of data handling (GMDH) as a machine learning algorithm, and convolutional neural network (CNN) and long short-term memory (LSTM) as deep learning algorithms. Moreover, the imperialist competitive algorithm (ICA) is used to fine-tune the hyperparameters of the machine learning and deep learning models. Moreover, there are 14 habitat characteristics used for developing the models: elevation, slope, valley depth, topographic wetness index, terrain roughness index, slope height, surface area, slope length and steepness factor (LS factor), normalized difference vegetation index, normalized difference water index, distance to drainage, distance to roads, drainage density, and morphometric features. The accuracy of prediction is evaluated using the area under the receiver operating characteristic curve. The results indicate comparable performances of all models. However, the CNN demonstrates superior capacity for prediction, achieving accuracies of 76.3% and 75.7% for the training and validation processes, respectively. The maps of potential habitat distribution are generated for five different levels of potentiality: very low, low, moderate, high, and very high.

• Journal of Practical Engineering Education
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• v.15 no.2
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• pp.419-427
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• 2023

In this study, optimal adhesion conditions to alleviate defects caused by heat shrinkage with FDM type 3D printers with machine learning are researched. Machine learning is one of the "statistical methods of extracting the law from data" and can be classified as supervised learning, unsupervised learning and reinforcement learning. Among them, a function model for adhesion between the bed and the output is presented using supervised learning specialized for optimization, which can be expected to reduce output defects with FDM type 3D printers by deriving conditions for optimum adhesion between the bed and the output. Machine learning codes prepared using Python generate a function model that predicts the effect of operating variables on adhesion using data obtained through adhesion testing. The adhesion prediction data and verification data have been shown to be very consistent, and the potential of this method is explained by conclusions.

• The Journal of Korean Association of Computer Education
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• v.16 no.1
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• pp.1-9
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• 2013

Computational thinking has been recently highlighted as an essential ability of the 21st Century so that many educational efforts have focused on broadening participation in computing and promoting computational thinking in K-12 settings. This paper describes the impact of creative learning activities with the Scratch on middle school students' computational thinking and creative potential. The learning activities were designed and implemented in 12 sessions with 34 middle school students. The pre and post creative potential assessment results show that students' creative personality and ideational behavior were significantly enhanced. Also, project portfolio analysis shows that students came to understand several computational concepts that are useful in a wide range of programming contexts: sequences, loops, conditionals, events, and operators.

• Nuclear Engineering and Technology
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• v.54 no.2
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• pp.644-652
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• 2022

During the pyroprocessing operation, various signals can be collected by process monitoring (PM). These signals are utilized to diagnose process states. In this study, feasibility of using PM for nuclear safeguards of electrorefining operation was examined based on the use of machine learning for detecting off-normal operations. The off-normal operation, in this study, is defined as co-deposition of key elements through reduction on cathode. The monitored process signal selected for PM was cathode potential. The necessary data were produced through electrodeposition experiments in a laboratory molten salt system. Model-based cathodic surface area data were also generated and used to support model development. Computer models for classification were developed using a series of recurrent neural network architectures. The concept of transfer learning was also employed by combining pre-training and fine-tuning to minimize data requirement for training. The resulting models were found to classify the normal and the off-normal operation states with a 95% accuracy. With the availability of more process data, the approach is expected to have higher reliability.

• School Mathematics
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• v.1 no.1
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• pp.109-122
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• 1999

In this paper we give a description of students' obstacles in their learning of geometry, especially resulted from their confusing a physical drawing with a figure, a geometrical object which a physical drawing represents. In computer-based teaching-learning environment, we could relieve such obstacles through providing students for experiences in which they must focus on elements of a figure and relations of them. But there may be potential in computer-based environment if we offer students only visual experience for validity of geometrical gacts: students' lack of understanding for need of proof and experience of cognitive obstacles which is very important for students to reflect their thinking and activities. Thus an didactical treatment must follows, which we also give an example.

• English Language & Literature Teaching
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• v.18 no.3
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• pp.77-92
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• 2012

This paper explores the learners' experience process and features in the initial stage of extensive reading process through college students' daily based journal writings. The subjects of this study were 10 volunteer students and they kept their journals from 30 minutes to 2 hours daily based for two weeks. The participants took pre and post tests in order to find out how their reading comprehension ability improved. Four of them improved it while the rest of them did not. After writing journals for two weeks, all students agreed on the potential power of extensive reading. In addition, they realized their learning problems and tried to overcome them on their own ways. Although the research period was only two weeks, the students showed the potential of extensive reading in learning English. From the results of the study, extensive reading encouraged the students to read more and they were convinced that extensive reading will lead to successful learning English. It can be meaningful outcome from the 2-week period research. Thus, a longer period scheme could provide more detail information to the extensive reading.

• Architectural research
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• v.26 no.1
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• pp.1-12
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• 2024

This study introduces a deep reinforcement learning (DRL)-based methodology for optimizing apartment space arrangements, addressing the limitations of human capability in evaluating all potential spatial configurations. Leveraging computational power, the methodology facilitates the autonomous exploration and evaluation of innovative layout options, considering architectural principles, legal standards, and client re-quirements. Through comprehensive simulation tests across various apartment types, the research demonstrates the DRL approach's effec-tiveness in generating efficient spatial arrangements that align with current design trends and meet predefined performance objectives. The comparative analysis of AI-generated layouts with those designed by professionals validates the methodology's applicability and potential in enhancing architectural design practices by offering novel, optimized spatial configuration solutions.

• Journal of Gifted/Talented Education
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• v.20 no.2
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• pp.571-594
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• 2010

Learning styles affect how students access and handle their task, so it is very important to understand how they study, when planning teaching-learning process, to enhance their potential to the maximum. In addition, in order to improve the quality of gifted education, there is a need to examine the curriculum and teaching-learning process which reflect learner characteristics. In this study, gifted student's preferred learning styles are investigated using questionnaires and learning style inventory. Also by analyzing the characteristics of the learners, it is hoped to get parents and teachers to understand the gifted who have various talents, and to support teaching programs for the gifted in order to develop their potential. This study shows that there are differences in the studying style between the gifted child and the average child. Namely, learner's physical and psychological environment can affect learning styles. Also there is a difference between the studying style which the gifted students prefer and the teaching style which teachers use most frequently. Programs for the gifted should be planned through better understanding of the characteristics of the learners.