• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.12
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• pp.264-275
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• 2017

Objective: This study examines the effects of children's art activities through forest experience in relation with Nuri curriculum on environmental sensitivity. Method: A survey was administered to an experimental group of 20 children as well as a control group of 20 children for statistical analysis. after 16-class art activities through forest experience were performed to children aged 4 at a daycare center for 50 days. Result: Children's art activities through forest experience in relation with Nuri curriculum had a positive effect on their environmental sensitivity. In particular, their art activities had a positive effect on their self-awareness, self-regulation, empathy and motivation in the experimental group, supporting the educational effects and benefits of developmental children's art activities through forest experience in relation with Nuri curriculum. In other words, art activities through forest experience are useful in terms of providing children with creative expression activities in art by leading to observation and exploration, as well as educational experiences that have positive attitudes toward the environment. Conclusion: As children's art activities through forest experience have a positive effect on their environmental sensitivity, more useful information on teaching-learning methods will be able to be provided to early childhood teachers in the field. Consequently, children's art activities through forest experience need to be actively introduced in the field of early childhood education as an alternative for learning nature and improving environment-friendly emotional intelligence.

• (The) Research of the performance art and culture
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• no.37
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• pp.365-389
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• 2018

This research aims to study Michael Chekhov's acting techniques scientifically, because his techniques has been studied only theoretically or empirically. Especially, this study focuses on 'imagination' and 'Psychological Gesture' from the perspective of cognitive psychology. Chekhov thought 'imagination' as the basis and core of all the works of acting. In cognitive psychology, it is called as 'imagery' and means 'a representation of the mind of the object not communicated by the sensory organs currently'. This study starts with defining imagery and takes a brief look at the features and kinds of imagery. Then the researcher will prove scientifically the possibility of training acting using imagery as Chekhov's assertion. For the proof of the validity of imagery, we'll look for the theoretical evidences-functional equivalence hypothesis, psychoneuromuscular theory, symbolic learning theory, psychophygiological information processing-and experimental ones-measurements of cerebral blood flow or event-related potential, experiments with fMRI(functional magnetic resonance imaging) or PET(positron emission tomography). As a result, we can see that imagery is functionally identical to perception and improves fulfillment of cognitive and physical tasks. As proving physical changes can draw out psychological changes(feeling) on the medium of imagery, we can also see the validity of Psychological Gesture. From the above research, even if Chekhov developed the acting techniques only on the basis of his experience, his techniques can be thought as having scientific validity. Though insufficient, this study can be a help for actors or students as they using Chekhov's techniques.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.10
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• pp.397-402
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• 2019

Accurately predicting cancer prognosis to provide appropriate treatment strategies for patients is one of the critical challenges in bioinformatics. Many researches have suggested machine learning models to predict patients' outcomes based on their gene expression data. Gene expression data is high-dimensional numerical data containing about 17,000 genes, so traditional researches used feature selection or dimensionality reduction approaches to elevate the performance of prognostic prediction models. These approaches, however, have an issue of making it difficult for the predictive models to grasp any biological interaction between the selected genes because feature selection and model training stages are performed independently. In this paper, we propose a novel two-dimensional image formatting approach for gene expression data to achieve feature selection and prognostic prediction effectively. Node2Vec is exploited to integrate biological interaction network and gene expression data and a convolutional neural network learns the integrated two-dimensional gene expression image data and predicts cancer prognosis. We evaluated our proposed model through double cross-validation and confirmed superior prognostic prediction accuracy to traditional machine learning models based on raw gene expression data. As our proposed approach is able to improve prediction models without loss of information caused by feature selection steps, we expect this will contribute to development of personalized medicine.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.4
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• pp.169-178
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• 2022

Morphemes are most primitive units in a language that lose their original meaning when segmented into smaller parts. In Korean, a sentence is a sequence of eojeols (words) separated by spaces. Each eojeol comprises one or more morphemes. Korean morphological analysis (KMA) is to divide eojeols in a given Korean sentence into morpheme units. It also includes assigning appropriate part-of-speech(POS) tags to the resulting morphemes. KMA is one of the most important tasks in Korean natural language processing (NLP). Improving the performance of KMA is closely related to increasing performance of Korean NLP tasks. Recent research on KMA has begun to adopt the approach of machine translation (MT) models. MT is to convert a sequence (sentence) of units of one domain into a sequence (sentence) of units of another domain. Neural machine translation (NMT) stands for the approaches of MT that exploit neural network models. From a perspective of MT, KMA is to transform an input sequence of units belonging to the eojeol domain into a sequence of units in the morpheme domain. In this paper, we propose a deep learning model for KMA. The backbone of our model is based on the BERT-fused model which was shown to achieve high performance on NMT. The BERT-fused model utilizes Transformer, a representative model employed by NMT, and BERT which is a language representation model that has enabled a significant advance in NLP. The experimental results show that our model achieves 98.24 F1-Score.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.1
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• pp.51-58
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• 2023

In this paper, we propose an organ segmentation technique for the automatic extraction of medical diagnostic indicators from X-ray images. In order to calculate diagnostic indicators of heart disease and spinal disease such as VHS(vertebral heart scale) and Cobb angle, it is necessary to accurately segment the thoracic spine, carina, and heart in a chest X-ray image. A deep neural network model in which the high-resolution representation of the image for each layer and the structure converted into a low-resolution feature map are connected in parallel was adopted. This structure enables the relative position information in the image to be effectively reflected in the segmentation process. It is shown that learning performance can be improved by combining the OCR module, in which pixel information and object information are mutually interacted in a multi-step process, and the channel attention module, which allows each channel of the network to be reflected as different weight values. In addition, a method of augmenting learning data is presented in order to provide robust performance against changes in the position, shape, and size of the subject in the X-ray image. The effectiveness of the proposed theory was evaluated through an experiment using 145 human chest X-ray images and 118 animal X-ray images.

• International Journal of Computer Science & Network Security
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• v.22 no.8
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• pp.97-104
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• 2022

European integration processes have acted as a catalyst for the emergence of a new type of educational environment, which is characterized by competent flexibility of specialists. Therefore, the article focuses on professional training of teachers in the context of European integration processes using information technology and the search for innovative methods of training specialists. One of the educational priorities in Europe is to create a new model of a teacher who has an academic education, knows innovative methods, is able to perform functions and tasks efficiently and professionally, adequately, quickly and correctly respond to changes and innovations. The tasks facing education in the European dimension are formulated. The main trends in the education of teachers in modern Europe are described: the need to deepen and expand subject training programs in pedagogical institutions of Higher Education, which will allow autonomy of activity, awareness of responsibility for independent creative decisions, create favorable conditions for the development of professionalism through the use of Information Technology and the search for innovative methods of training specialists. At the present stage, various models of teacher training are being developed based on the University and practical concept using information technology and searching for innovative methods of training specialists. On this basis, two different theories of perception of teacher education were formed: as preparation of teachers for work throughout their professional career; as preparation for the first years of professional work, which is periodically repeated in the process of continuous professional training and improvement. Among the advantages that the use of Information Technology and the search for innovative methods of training specialists to implement the learning process, it is worth mentioning the following: simultaneous use of several channels of perception of the student or student in the learning process, thanks to which the integration of information processed by different sensory organs is achieved; the ability to simulate complex real experiments; visualization of abstract information by dynamic representation of processes, etc.

• Journal of Science Education
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• v.38 no.3
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• pp.718-736
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• 2014

This study was intended to determine PCK of the middle school science teachers on Mendelian genetics and factors influenced to form their PCKs. Two science teachers with biology major with a teaching experience over 5 years were chosen as the subject. Data were collected by class observation, semi-structured interview, teacher questionnaire survey, Content Representation and Pedagogical and Professional-experience Repertoire. The collected data were analyzed based on Magnusson's PCK for science teaching consisting of five components: (a) the orientation toward teaching science, (b) the knowledge of science curriculum, (c) the knowledge of students' understanding, (d) the knowledge of assessment, and (e) the knowledge and belief in the instructional strategies to teach science. Teachers could have the orientation toward teaching science served as an assisting role to support students' abilities. Both subject teachers seemed to focus on giving lectures. Their efforts to improve students' exploration methods and abilities were not expressed enough in their real classes and they found that students struggled to understand Mendelian genetics. Therefore, they should have explained them in an easier way and worked harder to make their students understood accurately and applied basic and advanced concepts of Mendelian genetics. They found students' preconception and misconception regarding Mendelian genetics and wished to enhance their learning effects by various teaching strategies such as correcting misconception, adding the history of science and simply assessing students' affirmative domains. It was also found that factors influenced to form PCK regarding Mendelian genetics by both teachers were as follows: teacher's personality and endeavor, textbooks and guidance books, schools and their circumstances, teaching experience, experience as a learner, interaction with their colleagues, and university curriculum. Both teachers said that it was important for teachers to make every efforts to give better classes.

• Korean Journal of Childcare and Education
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• v.11 no.2
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• pp.151-175
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• 2015

This study configured an integrated math program in which young children can directly participate through storytelling, a teaching technique that has recently earned great popularity. The purpose of the study is to have a positive effect on their mathematical disposition and self-efficacy through the adoption of this program. The program consists of the following five themes: 'understanding of the basic concept of numbers and calculation', 'understanding of the basic concept of space and figure', 'basic measurement', 'understanding of rules' and 'basic data collection and result representation'. The specific activities for each theme planned and executed according to a detailed plan were designed for 20 classes including integrated activities such as story sharing, fine arts and games. The study's participants were 48 five-year old children. The result of the research was that the experimental group's mathematical disposition and self-efficacy score was significantly higher than the control group. The Storytelling-Integrated Math Program was effective in young children's cultivating mathematical disposition and improving self-efficacy. Considering the reality that there has been some confusion and difficulty in carrying out storytelling math and an integrated math program based on the NURI curriculum, it appears that this study could provide a specific and effective teaching-learning program to teachers who want to introduce a program like this.g

• Journal of the Korean Chemical Society
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• v.63 no.6
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• pp.459-472
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• 2019

The purpose of this study was to investigate the effect of science writing for different audiences on preservice chemistry teachers' chemistry concept understanding and modeling ability in general chemistry experiment activities using Argument-based Modeling (AbM) strategy. And we also examined preservice chemistry teachers' perceptions of modeling in different audience groups. The participants of the study were 18 university students in the first grade of preservice chemistry teachers taking a general chemistry experiment course. They completed eleven topics of general chemistry experiment using argument-based modeling strategy. The understanding of chemistry concept was compared with the effect size of pre- and post-chemistry concept test scores. To find out modeling ability, we analyzed level of model by each preservice chemistry teacher. Analytical framework for the modeling ability was composed of three elements, explanation, representation, and communication. The questionnaire was conducted to check up on preservice chemistry teacher's recognition of modeling. The result of analyzing the effect of modeling for different audience on the understanding of chemistry concept and modeling ability, the preservice chemistry teachers' were found to be more effective when the level of audience was low. There was no difference in the recognition of modeling between the groups for audience. However, we could confirm that the responses of preservice chemistry teachers are changed in concrete when they have an experience in succession on modeling.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.43 no.6 s.312
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• pp.34-42
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• 2006

As art analytic method to uncover interesting patterns hidden under a large volume of data, data mining research has been actively done so far in various fields. However, current state-of-the-arts in data mining research have several challenging problems such as being too ad-hoc. The existing techniques are mostly the ones designed for individual problems, so there is no unifying theory applicable for more general data mining problems. In this paper, we address the problem of classification, which is one of significant data mining tasks. Specifically, our objective is to evaluate radial basis function (RBF) model for classification tasks and investigate its usefulness. For evaluation, we analyze the popular Monk's problems which are well-known datasets in data mining research. First, we develop RBF models by using the representational capacity based learning algorithm, and then perform a comparative assessment of the results with other models generated by the existing techniques. Through a variety of experiments, it is empirically shown that the RBF model has not only the superior performance on the Monk's problems but also its modeling process can be controlled in a systematic way, so the RBF model with RC-based algorithm might be a good candidate to handle the current ad-hoc problem.