• Journal of The Korean Association For Science Education
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• 제16권4호
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• pp.441-450
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• 1996

Various contexts are involved in the processes of science learning and instruction. In the perspective that the results of science learning and instruction usually depend on the nature of learning task content and context, content effects or context effects have been researched up to now. But, the discrimination between them was very ambiguous. For the clarity of them, it was supposed that science content would be composed of decontextualized knowledges and contexts, which were respectively dichotomized in common and special ones among disciplines of science. Science learning and instruction was discussed in view of interactions between cognitive, learning task, and social-cultural contexts. Especially, it was emphasized that task contexts, as a bridging role among contexts should be constructed considering cognitive and social cultural contexts.

• Journal of The Korean Association For Science Education
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• 제19권3호
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• pp.367-376
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• 1999

When 37 7th-graders learned science in cooperative learning environments, their small-group processes were audio/video taped. The verbal behaviors that appeared in cooperative learning processes were categorized, and the relationships between verbal behaviors and academic achievement were investigated. Students' verbal behaviors were classified into learning behaviors and management behaviors. Learning behaviors were further coded into giving help. reading problem, and asking help. Giving help was the most frequent behavior among the categories. In studying zero-order correlation between verbal behaviors and academic achievement, giving help and reading problem were found to have positive relationships with academic achievement. Giving specific content, which is a subcategory of giving help. showed the closest correlation with academic achievement. In studying partial correlation between verbal behaviors and the improvement of academic achievement, only application subtest score. which demands higher-order thinking, was positively related with some verbal behaviors including giving specific content.

• Journal of the Korean association of regional geographers
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• 제8권4호
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• pp.529-541
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• 2002

This paper aims to provide a context-specific interpretation on the shifting process of in-house R&D spaces in a large Korean firm in the context of rapidly changing markets and technology. Drawing on the case study of LG Electronics Company, one of the Korea's flagship companies, I examine the causes and mechanisms leading to a shift in domestic R&D spaces and the nature of learning processes between R&D teams and between R&D and other organizational units, particularly manufacturing. It appears that the current reshaping processes of domestic R&D spaces in LGE focus more on the clustering of core R&D laboratories than the geographical integration of conception and execution. However, it should not simply be viewed that such a move would be reduced to the linear model of innovation and organizational learning. Instead, it involves the firm-specific mode of regulating organizational competences. As contextual variables to induce such a firm-specific mode of organizational change, I consider the spatial form of organization, the spatial sources of knowledge and learning, and the powers of relational learning that can be made between distanciated actors and teams.

• Journal of the Korea Institute of Information and Communication Engineering
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• 제24권2호
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• pp.219-224
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• 2020

Binary classification has been broadly investigated in machine learning. In addition, binary classification can be easily extended to multi class problems. To successfully utilize machine learning methods for classification tasks, preprocessing and feature extraction steps are essential. These are important steps to improve their classification performances. In this paper, we propose a new learning method based on weighted least squares. In the weighted least squares, designing weights has a significant role. Due to this necessity, we also propose a new technique to obtain weights that can achieve feature transformation. Based on this weighting technique, we also propose a method to combine the learning and feature extraction processes together to perform both processes simultaneously in one step. The proposed method shows the promising performance on five UCI machine learning data sets.

• Korean Institute of Interior Design Journal
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• 제19권6호
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• pp.11-19
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• 2010

Technological development of information and communication have brought sharp changes to every sector such as society, culture, economy and education because of knowledge and information-oriented society. The number of classes has decreased, and some schools are closed or incorporated due to decrease of the population. Such phenomenon has brought changes to learning using digital technology and space. A change called digital innovation is characterized by interactive communication centering on the internet network. Therefore, it is very important to predict educational environment to be changed according to digital environment and to note how real learning space is changed. The development of digital technology in society in general presents two concepts of digital and contents, digitalized information. Such technology is recognized as a new paradigm in education sector and a new space is created through participation of instructors and learners in learning space. This study analysed cases of learning space of elementary schools based on bibliographical examination and related bibliography including data from academic presentations and news release to present developed leaning space. To present healthy and creative learning environment which can lead knowledge and information-based society in the future, the preface described the background, purpose, methods and range of the study, and analysed transitional processes of society and culture, digital learning contents, and learning space in education of elementary schools. Finally the study identified trends and cases of research on learning space and suggested digital learning contents space.

• The Journal of Korean Academic Society of Nursing Education
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• 제20권4호
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• pp.577-586
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• 2014

Purpose: This study investigates patterns of small group interaction and examines the influence among graduate nursing students of online collaborative learning strategies on small group interaction patterns, task performance and learning attitude in web-based team learning environments. Method: To analyze patterns of small group interaction, group discussion dialogues were reviewed by two instructors. Groups were divided into two categories depending on the type of feedback given (passive or active). For task performance, evaluation of learning processes and numbers of postings were examined. Learning attitude toward group study and coursework were measured via scales. Results: Explorative interactions were still low among graduate nursing students. Among the students given active feedback, considerable individual variability in interaction frequency was revealed and some students did not show any specific type of interaction pattern. Whether given active or passive feedback, groups exhibited no significant differences in terms of task performance and learning attitude. Also, frequent group interaction was significantly related to greater task performance. Conclusion: Active feedback strategies should be modified to improve task performance and learning attitude among graduate nursing students.

• Korean Journal of Radiology
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• 제24권7호
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• pp.698-714
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• 2023

In recent years, artificial intelligence, especially object detection-based deep learning in computer vision, has made significant advancements, driven by the development of computing power and the widespread use of graphic processor units. Object detection-based deep learning techniques have been applied in various fields, including the medical imaging domain, where remarkable achievements have been reported in disease detection. However, the application of deep learning does not always guarantee satisfactory performance, and researchers have been employing trial-and-error to identify the factors contributing to performance degradation and enhance their models. Moreover, due to the black-box problem, the intermediate processes of a deep learning network cannot be comprehended by humans; as a result, identifying problems in a deep learning model that exhibits poor performance can be challenging. This article highlights potential issues that may cause performance degradation at each deep learning step in the medical imaging domain and discusses factors that must be considered to improve the performance of deep learning models. Researchers who wish to begin deep learning research can reduce the required amount of trial-and-error by understanding the issues discussed in this study.

• Journal of Information Technology Applications and Management
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• 제23권3호
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• pp.13-23
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• 2016

In recent years, big data has usefully been deployed by organizations with the aim of getting a better prediction for the future. Moreover, knowledge management systems are being used by organizations to identify and create knowledge. Here, the output from analysis of big data and a knowledge management system are used to develop a new model with the goal of minimizing the cost of implementing new recognized processes including staff training, transferring and employment costs. Strategies are proposed from big data analysis and new processes are defined accordingly. The company requires various skills to execute the proposed processes. Organization's current experts and their skills are known through a pre-established knowledge management system. After a gap analysis, managers can make decisions about the expert arrangement, training programs and employment to bridge the gap and accomplish their goals. Finally, deduction graph is used to analyze the model.

• Journal of Science and Technology Studies
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• 제11권1호
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• pp.1-29
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• 2011

The purpose of this study is to develop a theoretical framework to analyze the social processes of embedding new technologies, among others, green technologies, in society, and based on this, to identify problems and challenges in introducing and assimilating biogas technologies in local communities in Korea. Chapter Two strives to develop a framework to analyze the social processes of embedding new technologies in society. A couple of key concepts such as technology community, technology learning and technology politics are introduced and discussed. Chapter Three and Four examine the problems arising from the social processes of embedding biogas plant technologies in local communities in Korea and tries to suggest policy options to tackle these problems.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 한국경영과학회 1997년도 추계학술대회발표논문집; 홍익대학교, 서울; 1 Nov. 1997
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• pp.181-184
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• 1997

Practical applications of learning systems usually involve complex domains exhibiting nonlinear behavior and dilution by noise. Consequently, an intelligent system must be able to adapt to nonlinear processes as well as probabilistic phenomena. An important class of application for a knowledge based systems in prediction: forecasting the future trajectory of a process as well as the consequences of any decision made by e system. This paper examines the robustness of data mining tools under varying levels of noise while predicting nonlinear processes in the form of chaotic behavior. The evaluated models include the perceptron neural network using backpropagation (BPN), the recurrent neural network (RNN) and case based reasoning (CBR). The concepts are crystallized through a case study in predicting a Henon process in the presence of various patterns of noise.