• Journal of Korean Elementary Science Education
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• v.19 no.1
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• pp.113-129
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• 2000

The purposes of this study were to investigate children's preconception about matter, to analyze children's conceptual change. of ,matter by science learning activities, and to investigate understanding possibility about mallet The subjects of this study were the second grade students of an elementary school and science learning activities were conducted to 2 boys and 3 girls for investigating their conceptual change. Video tape recording, interview, drawing pictures and writing statements were used to collect data. The concepts in this study were properties of matter, states of matter, uses of matter and origins and changes in matter. Then, the collected data were analyzed by the way of categorization and qualitative method. The conclusions of this study are as follows: science loaming activities were effective for changing misconceptions of matter to scientific concepts. Conceptual changes of high achievers happened easier and more than of low achievers. In the ideas about matter, some children had supernatural ideas and animism, which were most second grade students' characteristics.

• Research in Mathematical Education
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• v.7 no.3
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• pp.151-161
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• 2003

In this paper, we discuss how to assess students' understanding of concepts during class, after class and in regular exams in the mathematics classes using the handheld graphing technology. We show some methods of assessment that are compatible with the class using the handheld graphing technology. These methods are adjustable to students' learning during class, homework after class or in regular exams. As a feedback of these methods we give students additional opportunity to understand concepts by giving additional concept provoking problems or giving additional help if necessary.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.2
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• pp.307-312
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• 2021

Artificial Intelligence has a long history and is used in various fields including image recognition and automatic translation. Therefore, when we first encounter artificial intelligence, many terms, concepts and technologies often have difficulty in setting or implementing research direction. This study summarized important concepts related to artificial intelligence and summarized the progress of the past 60 years to help researcher suffering from these difficulties. Through this, it is possible to establish the basis for the use of vast artificial intelligence technologies and establish the right direction for research.

• Communications for Statistical Applications and Methods
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• v.6 no.1
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• pp.251-259
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• 1999

The purpose of this study is to develope a program for statistics education. This program deals with simulation which is helpful in understanding some elementary statistical concepts. This program under multimedia environment which includes sound video animation etc. doesn't show only the result but make it possible for students to execute the program by stages. This type of dynamic learning is efficient to overcome the limits of teaching materials or classroom work. Also it can interest students greatly. By executing it the students can understand the method and meaning of simulation and acquire concepts of probability and statistical inference naturally.

• The Mathematical Education
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• v.47 no.2
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• pp.221-224
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• 2008

The article shows that the concept 'weight' and the concept 'heaviness' give rise to different abstractions in the sense of Piaget and that these two concepts are differentiated by set-theoretic devices. The failure of differentiation of these two concepts 'weight' and the 'heaviness' can cause the failure of learning of the difference between reflective abstraction and empirical reflective abstraction. To explain the Piagetian abstrcation in a classroom, the author suggests to use the concept 'color' instead of the concept 'weigtht'.

• The Mathematical Education
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• v.42 no.1
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• pp.11-18
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• 2003

One of the terms that are most often used in mathematics classrooms by either teachers or students might be about 'understanding' of mathematical concepts. Although 'understanding' in mathematics teaching and learning has been highly emphasized by many people, there is no exact and undebatable definition of 'understanding' as of yet. This paper tries to contribute to unfolding the meaning and the structure of understanding in mathematics education along with various literature and finally enhance our understanding of 'understanding' in mathematics education.

• The Mathematical Education
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• v.48 no.3
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• pp.353-358
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• 2009

The article discusses the independence concept occurring in the learning of probability. The author does not distinguishes the independence in the events from the independence in the trials. Instead, the author suggests the physico-empirical independence and the logico-mathematical independence to distinguish between the two concepts.

• Journal of the Korean Regional Science Association
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• v.15 no.1
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• pp.93-116
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• 1999

Recently the concept of learning has become very fashionable among academics from different economic disciplines. Economic geographers and spatial planners joined this fashion by increasingly speaking about the 'learning region'. This paper makes clear that this learning region'. This paper makes clear that this learning region concept has been launched from three angles; as spatial outcome of grand societal changes, as spatial concentration of entrepreneurial learning for innovation and as regional development concept. Despite the deficits and flaws such a young concept is faced with, such as vague definitions, the lack of empirical research and an insufficiently clear separation from existing concepts, the learning region concept might provide economic geography with more insight in agglomeration effects, stronger links with policy-making and more knowledge on path dependency and thus on unravelling the distinction between 'good' and 'bad' industrial agglomerations.

• Electronics and Telecommunications Trends
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• v.34 no.1
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• pp.98-110
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• 2019

Machine learning has been applied to medical imaging and has shown an excellent recognition rate. Recently, there has been much interest in preventive medicine. If data are accessible, machine learning packages can be used easily in digital healthcare fields. However, it is necessary to prepare the data in advance, and model evaluation and tuning are required to construct a reliable model. On average, these processes take more than 80% of the total effort required. In this study, we describe the basic concepts of machine learning, pre-processing and visualization of datasets, feature engineering for reliable models, model evaluation and tuning, and the latest trends in popular machine learning frameworks. Finally, we survey a explainable machine learning analysis tool and will discuss the future direction of machine learning.

• Korean Journal of Radiology
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• v.21 no.1
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• pp.33-41
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• 2020

Artificial intelligence has been applied to many industries, including medicine. Among the various techniques in artificial intelligence, deep learning has attained the highest popularity in medical imaging in recent years. Many articles on deep learning have been published in radiologic journals. However, radiologists may have difficulty in understanding and interpreting these studies because the study methods of deep learning differ from those of traditional radiology. This review article aims to explain the concepts and terms that are frequently used in deep learning radiology articles, facilitating general radiologists' understanding.