• 한국수학교육학회지시리즈A:수학교육
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• 제54권1호
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• pp.31-47
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• 2015

School Math Field Trips(SMFT) for School Mathematics can be defined as teaching and learning activity of mathematics going into the field of Korean history, culture, science and technology. This is a literature analysis study to systemize teaching and learning method of mathematics based on literature analysis and real SMFT activity. First, SMFT was introduced to improve cognitive affective and cultural-mathematical teaching and learning method of mathematics. Second, SMFT has three purposes of cognitive, affective and cultural-mathematical. Third, to conduct mathematical education activity the direction of teaching was set. Forth, the progressing way of developing material and SMFT was researched. Fifth, developing the evaluation standard of SMFT and evaluation method was suggested.

• 대한수학교육학회지:학교수학
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• 제6권1호
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• pp.37-57
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• 2004

본 연구에서는 7차 교육과정에서 요구하는 교실 수업 개선의 한 방법으로 웹 자료의 활용이 초등학교 수학과의 자기 주도적 학습에 어떻게 영향을 주는가를 알아보는데 그 목적이 있다. 본 연구를 통하여 웹 자료가 아동들에게 적극적인 학습태도를 갖게 해 주며, 수학 개념 형성을 용이하게 해 주며 협동학습에 도움을 주며 수준별 학습을 강화시켜 주고 문제해결력을 신장시키고 스스로 객관적 평가를 할 수 있도록 하여 자기 주도적 학습에 긍정적 영향을 주었음을 알 수 있었다.

• 한국초등과학교육학회지:초등과학교육
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• 제29권4호
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• pp.567-575
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• 2010

The purpose of this study is to investigate the effectiveness of academic achievements, science process skill and scientific attitude. The subjects of this study were 68 fourth-grade elementary school students who were 33 students for the 5E learning cycle instruction and 35 students for traditional instruction. The control group was taught with traditional teaching method, while the experimental group was taught 'the change to the volume of material due to heat' unit of 4th grade with the developed learning cycle model. The results were as fellows: First, the learning cycle instruction is more effective for understanding of a concept related to the change to the volume of material due to heat. Second, the learning cycle model seems more effective for the expansion of both scientific inquiry ability and scientific attitude.

• 소성∙가공
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• 제31권4호
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• pp.229-239
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• 2022

Convolution Neural Network(CNN) is a class of deep learning algorithms and can be used for image analysis. In particular, it has excellent performance in finding the pattern of images. Therefore, CNN is commonly applied for recognizing, learning and classifying images. In this study, the surface defect classification performance of Al 6061 extruded material using CNN-based algorithms were compared and evaluated. First, the data collection criteria were suggested and a total of 2,024 datasets were prepared. And they were randomly classified into 1,417 learning data and 607 evaluation data. After that, the size and quality of the training data set were improved using data augmentation techniques to increase the performance of deep learning. The CNN-based algorithms used in this study were VGGNet-16, VGGNet-19, ResNet-50 and DenseNet-121. The evaluation of the defect classification performance was made by comparing the accuracy, loss, and learning speed using verification data. The DenseNet-121 algorithm showed better performance than other algorithms with an accuracy of 99.13% and a loss value of 0.037. This was due to the structural characteristics of the DenseNet model, and the information loss was reduced by acquiring information from all previous layers for image identification in this algorithm. Based on the above results, the possibility of machine vision application of CNN-based model for the surface defect classification of Al extruded materials was also discussed.

• 한국재료학회지
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• 제33권4호
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• pp.164-174
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• 2023

The bandgap characteristics of semiconductor materials are an important factor when utilizing semiconductor materials for various applications. In this study, based on data provided by AFLOW (Automatic-FLOW for Materials Discovery), the bandgap of a semiconductor material was predicted using only the material's compositional features. The compositional features were generated using the python module of 'Pymatgen' and 'Matminer'. Pearson's correlation coefficients (PCC) between the compositional features were calculated and those with a correlation coefficient value larger than 0.95 were removed in order to avoid overfitting. The bandgap prediction performance was compared using the metrics of R² score and root-mean-squared error. By predicting the bandgap with randomforest and xgboost as representatives of the ensemble algorithm, it was found that xgboost gave better results after cross-validation and hyper-parameter tuning. To investigate the effect of compositional feature selection on the bandgap prediction of the machine learning model, the prediction performance was studied according to the number of features based on feature importance methods. It was found that there were no significant changes in prediction performance beyond the appropriate feature. Furthermore, artificial neural networks were employed to compare the prediction performance by adjusting the number of features guided by the PCC values, resulting in the best R² score of 0.811. By comparing and analyzing the bandgap distribution and prediction performance according to the material group containing specific elements (F, N, Yb, Eu, Zn, B, Si, Ge, Fe Al), various information for material design was obtained.

• 로봇학회논문지
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• 제10권2호
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• pp.79-89
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• 2015

The purpose of this study was to examine the concept of r-learning based on existing studies of r-learning. It also aimed to analyze r-learning environments in an effort to determine prerequisites for the successful entrenchment of r-learning in material(technology and infrastructure), human(young children and teacher) and institutional(law and policy) aspects. This study intended to suggest some of the right directions for the revitalization of r-learning. In conclusion, the position of r-learning and its interrelationship with related systems in the ecosystem of early childhood education should accurately be grasped to accelerate the integration of r-learning into kindergarten education to maximize the effects of the convergence of the two. Intensive efforts should be made from diverse angles to expedite the spread and enrichment of r-learning.

• Educational Technology International
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• 제16권1호
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• pp.58-83
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• 2015

Digital textbooks draw attention as a new format of educational material, using the advantages of information technology; this innovative learning tool requires consideration as a part of successful and effective learning. The main purpose of the article is to investigate the mediating role of self-regulation between digital literacy and learning outcomes (academic performance and learning motivation) when using digital textbooks as a learning tool in Middle School English. Both descriptive and regression analysis were used as data analyses methods. The main findings of this study were as follows: first, digital literacy and self-regulation significantly predicted academic performance and learning motivation; second, self-regulation fully mediated between digital literacy and academic performance; third, self-regulation partially mediated between digital literacy and learning motivation. The research results proved the effects of digital literacy and self-regulation on the learning outcomes and mediating role of self-regulation between digital literacy and learning outcomes. These results help to design and implement effective lessons when using a digital textbook in Middle school English.

• 한국디지털정책학회:학술대회논문집
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• 한국디지털정책학회 2005년도 춘계학술대회
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• pp.605-615
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• 2005

This paper represent a method of U-Learning based on advanced e-Learning. Ubiquitous computing configuration and advanced Information technology. As we know well, the 21th century is called knowledge based informational society. Many scholar stress that the improved 21th century's educational paradigm be able to success based on advanced educational paradigm. Therefore, we discuss the material for e-Learning fields including with necessity, vision, law, quality authorization etc. Also, we discuss the relational technologies including with meta data, standardization, identification etc. Finally, we propose a method for constructing the U-Learning based on advanced e-Learning and Ubiquitous computing configuration.

• International Journal of Internet, Broadcasting and Communication
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• 제16권3호
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• pp.388-393
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• 2024

The convergence of machine learning and smart farm is becoming more and more important. The purpose of this research is to quantitatively analyze machine learning and smart farm with bibliographic data from 2013 to 2023. This study analyzed the 251 articles, filtered from the Web of Science, with regard to the article publication trend, the article citation trend, the top 10 research area, and the top 10 keywords representing the articles. The quantitative analysis results reveal the four points: First, the number of article publications in machine learning and smart farm continued growing from 2016. Second, the article citations in machine learning and smart farm drastically increased since 2018. Third, Computer Science, Engineering, Agriculture, Telecommunications, Chemistry, Environmental Sciences Ecology, Material Science, Instruments Instrumentation, Science Technology Other Topics, and Physics are top 10 research areas. Fourth, it is 'machine learning', 'smart farming', 'internet of things', 'precision agriculture', 'deep learning', 'agriculture', 'big data', 'machine', 'smart' and 'smart agriculture' that are the top 10 keywords composing authors' keywords in the articles in machine learning and smart farm from 2013 to 2023.

• 한국초등수학교육학회지
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• 제14권3호
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• pp.745-768
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• 2010

본 연구는 수준이 다른 여러 영재 집단의 소속 학생들이 도형수와 관련된 과제를 해결하고 창의적 산출물을 도출하는 가운데 그들의 수학적 사고력과 창의적인 아이디어를 발휘할 수 있도록 수준별 수학 영재 교수 학습 자료를 개발하는 절차와 방법을 탐구해 보는 데 그 목적이 있다. 이를 위해 교수 학습 자료 개발의 준거와 절차 모형에 따라 도형수 과제의 교수 학습 자료의 원형과 실제적인 자료를 개발하고 그것을 현장 수업에 적용하면서 학생들의 다양한 해결과정을 분석하면서 그 자료의 문제점과 개선점을 제시하였다. 그리고 초등학교에서 집단의 수준별로 산출물 탐구가 가능한 도형수의 내용 범위를 설정해 보면서 차후 유사한 다른 수학 영재 교수 학습 자료 개발할 때 고려한 네 가지의 시사점을 제안하였다.