• Journal of the Korean Geographical Society
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• v.46 no.4
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• pp.534-553
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• 2011

Analogical thinking is a problem-solving strategy to use a familiar problem (or base analog) to solve a novel problem of the same type (the target problem). The purpose of this study is to provide new insight into geography teaching and learning by connecting cognitive science research on analogical thinking with issues of geography education and suggest that teaching with analogies can be a productive instructional strategy for geography. In this study, using the various examples of analogical thinking used in geography we defined analogical thinking, addressed the theoretical models on analogical transfer, and discussed conditions that make an effective analogical transfer. The major research findings include the following: a) the spatial analogy, indicating skills to find places that may be far apart but have similar locations, and therefore have other similar conditions and/or connections, can provide a useful way to design contents for place learning; b) representational transfer, specifying a common representation for two problems, can play a key role in solving geographic problems requiring data visualization and spatialization processes; and c) either asking learners to compare/analyze similar examples sharing common structure or providing them examples bridging the gap between concrete, real-life phenomena and the ideas and models can contribute to learning in geographic concepts and skills. The spatial analogy requiring both geographic content knowledge and visual/spatial thinking has the potential to become a content-specific problem-solving strategy. We ended with recommendations for future research on analogy that is important in geography education.

• Journal of the Korea Society of Computer and Information
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• v.25 no.11
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• pp.27-40
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• 2020

In this paper, we investigate the correlation between the amount of English sentences that Korean English learners (L2ers) are exposed to and their sentence processing patterns by examining what Long Short-Term Memory (LSTM) language models (LMs) can learn about implicit syntactic relationship: that is, the filler-gap dependency. The filler-gap dependency refers to a relationship between a (wh-)filler, which is a wh-phrase like 'what' or 'who' overtly in clause-peripheral position, and its gap in clause-internal position, which is an invisible, empty syntactic position to be filled by the (wh-)filler for proper interpretation. Here to implement L2ers' English learning, we build LSTM LMs that in turn learn a subset of the known restrictions on the filler-gap dependency from English sentences in the L2 corpus that L2ers can potentially encounter in their English learning. Examining LSTM LMs' behaviors on controlled sentences designed with the filler-gap dependency, we show the characteristics of L2ers' sentence processing using the information-theoretic metric of surprisal that quantifies violations of the filler-gap dependency or wh-licensing interaction effects. Furthermore, comparing L2ers' LMs with native speakers' LM in light of processing the filler-gap dependency, we not only note that in their sentence processing both L2ers' LM and native speakers' LM can track abstract syntactic structures involved in the filler-gap dependency, but also show using linear mixed-effects regression models that there exist significant differences between them in processing such a dependency.

• The KIPS Transactions:PartB
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• v.11B no.6
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• pp.717-722
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• 2004

This paper presents fuzzy rules to predict diagnosis of Wisconsin breast cancer using neural network with weighted fuzzy membership functions (NNWFM). NNWFM is capable of self-adapting weighted membership functions to enhance accuracy in prediction from the given clinical training data. n set of small, medium, and large weighted triangular membership functions in a hyperbox are used for representing n set of featured input. The membership functions are randomly distributed and weighted initially, and then their positions and weights are adjusted during learning. After learning, prediction rules are extracted directly from the enhanced bounded sums of n set of weighted fuzzy membership functions. Two number of prediction rules extracted from NNWFM outperforms to the current published results in number of rules and accuracy with 99.41%.

• Journal of The Korean Association of Information Education
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• v.23 no.6
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• pp.655-663
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• 2019

Recent interest in software education has naturally led to interest in computer science. Computer science is the study of theory and principle of computer and computational systems. This knowledge and concept of computer science is fundamental to software education. Recently, "Computer Science (CS) Unplugged", which is an educational method for introducing students to concepts of computer science without using a computer, has been widely used in classes around the world. CS Unplugged is a method for learning the basic principles of computer science through diverse playing activities. The problem, however, is that these CS unplugged activities take place in class without an analysis of how closely they relate to the core subjects learned in real computer science. Therefore, in this study, we will study on the comparison between the core contents of computer science and the core contents of computer science contained in the various activities of CS Unplugged.

• Journal of The Korean Association of Information Education
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• v.25 no.3
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• pp.491-499
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• 2021

Major countries in the world have established strategies for educating about artificial intelligence(AI) and with large investments are actively implementing these strategies. With this trend, domestic ministries have made efforts to establish national strategies to better educate students about AI. This paper presents the syllabus of AI classrooms which has been developed and presented to pre-service and in-service elementary school teachers for their use. In addition, the AI education tools they particularly preferred and their future plans for utilizing them in the elementary school classroom were investigated. Through this study, it was found that pre-service and in-service elementary school teachers strongly prefer lectures about AI education tools that can be immediately applied in the classroom, rather than learning about the theoretical basis of AI. At issue, however, is that the ability to utilize AI is usually based on a sufficient understanding of the theory. Thus, this paper suggests further study to identify better pedagogical practices to improve students' understanding the theoretical basis of AI.

• Journal of the Korean Society for Library and Information Science
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• v.42 no.4
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• pp.223-241
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• 2008

This study presents the discourse for academic generation of 'School Library Education Science' based on integrative research between library & information science and education science. The study discusses research fields and themes of 'School Library Education Science' as the base to define the roles of the school library and of teacher-librarians. It contributes to upgrading the research level of the school library. The research field in the school library analyzes and integrates both aspects of library & information science and of education science. The developed theories of 'School Library Education Science' present Library-Based Education and Library-Based Learning, Base Connectionism, Base Integrationism, Top456 Approach, Curriculum-Based Approach, Viewpoint Approach, and Education of Information and the Library as a Subject Matter. The trends in research of the school library analyze the thesis numbers of academic journals. The future research challenges of 'School Library Education Science' discuss the educational basics of the school library, operational management, electronic school libraries, and educational activities. Conclusively, this study analyzes the current situation of the school library & of teacher-librarians in Korea, and provides its solution by the aspects of 'School Library Education Science'.

• Journal of Biomedical Engineering Research
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• v.38 no.6
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• pp.342-351
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• 2017

The neural decoding is a procedure that uses spike trains fired by neurons to estimate features of original stimulus. This is a fundamental step for understanding how neurons talk each other and, ultimately, how brains manage information. In this paper, the strategies of neural decoding are classified into three methodologies: rate decoding, temporal decoding, and population decoding, which are explained. Rate decoding is the firstly used and simplest decoding method in which the stimulus is reconstructed from the numbers of the spike at given time (e. g. spike rates). Since spike number is a discrete number, the spike rate itself is often not continuous and quantized, therefore if the stimulus is not static and simple, rate decoding may not provide good estimation for stimulus. Temporal decoding is the decoding method in which stimulus is reconstructed from the timing information when the spike fires. It can be useful even for rapidly changing stimulus, and our sensory system is believed to have temporal rather than rate decoding strategy. Since the use of large numbers of neurons is one of the operating principles of most nervous systems, population decoding has advantages such as reduction of uncertainty due to neuronal variability and the ability to represent a stimulus attributes simultaneously. Here, in this paper, three different decoding methods are introduced, how the information theory can be used in the neural decoding area is also given, and at the last machinelearning based algorithms for neural decoding are introduced.

• 한국정보교육학회:학술대회논문집
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• 2010.08a
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• pp.15-18
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• 2010

As robots are proved to be effective in enhancing students' creativity and problem-solving abilities and satisfying various needs in special education for the gifted, many students participate in private education and after-school robot classes. However, it is difficult for students in the lower social economy class to use robots for their learning because of the high expense of robots. On this point, as a part of u-Learnng project, this research attempts to provide students in the lower social economy class with the opportunities to use robots for one year. At the end of the year, we will compare the experimental group and the control group in order to examine learning effects of using robots. Until now we have found many cases that show positive effects of the use of robots in students' learning.

• Journal of Korean Society for Geospatial Information Science
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• v.14 no.4 s.38
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• pp.71-77
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• 2006

Support Vector Machine (SVM) which has roots in a statistical learning theory is a training algorithm based on structural risk minimization. Generally, SVM algorithm uses the kernel for determining a linearly non-separable boundary and classifying the data. But, classical kernels can not apply to effectively the hyperspectral image classification because it measures similarity using vector's dot-product or euclidian distance. So, This paper proposes the spectral similarity kernel to solve this problem. The spectral similariy kernel that calculate both vector's euclidian and angle distance is a local kernel, it can effectively consider a reflectance property of hyperspectral image. For validating our algorithm, SVM which used polynomial kernel, RBF kernel and proposed kernel was applied to land cover classification in Hyperion image. It appears that SVM classifier using spectral similarity kernel has the most outstanding result in qualitative and spatial estimation.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.2
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• pp.246-251
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• 2021

This study is analyzed survey based on classes of computational thinking to identify problem solving ability, to reflect them in curriculum plan for software education. Through analyzing difficulties on computational thinking learners by pre/post test on problem solving ability, the education method of software curriculum was proposed. For this study, the subject, scope of content, and activity plan were organized into 15 weeks of software curriculum for 2 hours per week, and questionnaire was conducted for 63 students. As a result, the 'Humanities Departments' have shown higher problem solving ability improvement than 'Science and Engineering Departments'. Based on the results, in order to cultivate creative fusion-type talent, the theoretical systems that fundamentally define thinking and perception must be fused with each other. In addition, software education should be improved to be extended to non-majors in various fields.