• Journal of The Korean Association For Science Education
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• v.31 no.4
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• pp.491-504
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• 2011

We investigated science-gifted students' conceptions on science learning. The inventory instruments used for our study were a questionnaire on the conceptions of learning science (COLS) and a questionnaire on the approaches to learning science (ALS). Our analysis of the questionnaires showed that there are differences in the conceptions of science learning between the science-gifted and ordinary students. Science-gifted students perceive science learning as storing up of scientific knowledge, expansion of knowledge structure and achievement of a new view. There are no differences in the conceptions of science learning between male and female science-gifted students. There are also no differences in the conceptions of science learning in terms of subject preference such as physics, chemistry, biology and earth science. Our analysis offer assistance to teaching material and teaching method for science courses.

• Health Communication
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• v.13 no.2
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• pp.109-115
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• 2018

Background: The study is descriptive research study to investigate the self-directed learning ability to explore the facts that influence of anatomy using cadaver dissection beginning nursing students. Methods: A descriptive research design was used. The data was collected from 31st May to 7 June, 2016. The participants were total 121 first-year nursing students in C University. This anatomy practicum course was composed of three session, and each session was composed of 3hours, 60minutes of body structure and anatomy lecture, 90 minutes of cadaver dissection, and 30minutes of summary. Results: The results of the study showed that satisfaction with cadaver dissection was statistically significant in the usefulness in connection with the major of nursing (r=.543, P<.001), educational understanding (r=.465, p<.001), and nursing learning motivation (r=.517, p<.001). As the nursing learning motivation increased, self-directed learning ability increased. Also nursing learning motivation influenced self-directed learning (${\beta}=0.266$, p<.01). Conclusion: It is necessary to develop a program that can link theoretical education with practicum education of anatomy using cadaver dissection for efficient learning of the anatomy major courses of nursing students.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.4
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• pp.111-117
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• 2022

Many studies have been conducted on software fault prediction models for decades, and the models using machine learning techniques showed the best performance. Deep learning techniques have become the most popular in the field of machine learning, but few studies have used them as classifiers for fault prediction models. Some studies have used deep learning to obtain semantic information from the model input source code or syntactic data. In this paper, we produced several models by changing the model structure and hyperparameters using MLP with three or more hidden layers. As a result of the model evaluation experiment, the MLP-based deep learning models showed similar performance to the existing models in terms of Accuracy, but significantly better in AUC. It also outperformed another deep learning model, the CNN model.

• Journal of Korea Society of Digital Industry and Information Management
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• v.19 no.3
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• pp.245-251
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• 2023

Facial expression recognition plays a significant role in understanding human emotional states. With the advancement of AI and computer vision technologies, extensive research has been conducted in various fields, including improving customer service, medical diagnosis, and assessing learners' understanding in education. In this study, we develop a model that can infer emotions in real-time from a webcam using transfer learning with TensorFlow.js and MobileNet. While existing studies focus on achieving high accuracy using deep learning models, these models often require substantial resources due to their complex structure and computational demands. Consequently, there is a growing interest in developing lightweight deep learning models and transfer learning methods for restricted environments such as web browsers and edge devices. By employing MobileNet as the base model and performing transfer learning, our study develops a deep learning transfer model utilizing JavaScript-based TensorFlow.js, which can predict emotions in real-time using facial input from a webcam. This transfer model provides a foundation for implementing facial expression recognition in resource-constrained environments such as web and mobile applications, enabling its application in various industries.

• Journal of Elementary Mathematics Education in Korea
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• v.14 no.1
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• pp.23-41
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• 2010

This research applies the climbing learning method that, a Japanese professor, Saito Noboru established and practiced, to fourth and sixth graders in an elementary school in order to analyze its effect on mathematical creativity, attitude toward mathematical creativity, so called CAS(Creative Attitude Scale) and academic achievement of the subject. The goal is to explore methods that can enhance students' mathematical creativity. To address these tasks, the research developed a teaching-learning scheme and learning structure chart that applies the climbing learning method. Next, the research organized two homogeneous groups among 124 students in fourth and sixth grades in S elementary school, located in the city of Busan. The experiment group went through classes that applied climbing learning method, while the control group received regular teaching. The following describes the research findings. After the experiment, the research conducted t-test for the independent sample based on the test result in terms of mathematical creativity, CAS and academic achievement of the subject. For mathematical creativity, all four constructing factor showed statistically significant differences at significance level of 5%. For CAS, statistically significant difference was revealed at significance level of 0.1%. However, in regard to a test of academic achievement for fourth and sixth graders, statistically significant difference was not detected at significance level of 5% even though the average score of the students in the experiment group was higher by 6 points. The research drew the following conclusion. Firstly, classes that apply climbing learning method can be more effective than regular classes in enhancing mathematical creativity of elementary school students. Secondly, the climbing learning method has positive impact on inclination for mathematical creativity of elementary school students. The research suggests that the climbing learning method can be an effective teaching-learning tool to improve students' mathematical creativity and inclination for mathematical creativity.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.9
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• pp.340-350
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• 2016

This study analyzes the structural causal relationship among adult learners' characteristics, participatory motivation, methods of learning process by instructor, institutes satisfaction, and learning outcome. In addition, the study reviews the mechanism by which three different variables work as intermediates in the relationship between adult learners' characteristics and learning outcomes. The study subjects were 444 adult learners who participated in lifelong education, and the research hypothesis was verified through Structure Equation Modeling analysis. The results are as follows. First, the characteristics of adult learners affect learning outcomes, as well as the methods of learning process by instructor affects, participatory motivation and institutes satisfaction, and have a significantly positive(+) effect. Secondly, the methods of learning process by instructor affect institutes satisfaction, and the effect is significant. However, it does not influence participatory motivation. Moreover, it has a negative influence on learning outcomes. Additionally, participatory motivation has a significant effect on both learning outcomes and institutes satisfaction. Thirdly, when examining the ultimate intermediate between the characteristics of adult learners and their learning outcomes, institutes satisfaction was the optimal channel. These study results suggest not only the role of lifelong education institute, but also ways to improve academic outcomes of adult learners within the lifelong education field.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.6
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• pp.1143-1148
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• 2017

Currently, Artificial Intelligence and Deep Learning are rising as hot social issues, and these technologies are applied to various fields. A good method among the various algorithms in Artificial Intelligence is Convolutional Neural Networks. Convolutional Neural Network is a form that adds Convolution Layers to Multi Layer Neural Network. If you use Convolutional Neural Networks for small amount of data, or if the structure of layers is not complicated, you don't have to pay attention to speed. But the learning should take long time when the size of the learning data is large and the structure of layers is complicated. In these cases, GPU-based parallel processing is frequently needed. In this paper, we developed Convolutional Neural Networks using CUDA, and show that its learning is faster and more efficient than learning using some other frameworks or programs.

• Philosophy of Education
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• no.39
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• pp.191-221
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• 2009

The aim of this study is to analyse of elements and structure of consciousness theory in the 1887 Psychology written by John Dewey, and to research its educational implications. Conclusions are as follows: Firstly, consciousness theory articulated in first edition of Dewey's Psychology was influenced by neo-Hegelian G. S. Hall, and then characteristics of its theory was metaphysical and idealistic. But after of researching the work of William James, his approach to consciousness changed surprisingly from idealistic to experimental. His experimental approach and scientific attitude to it influenced the formation and development of advanced theories in his epistemology, axiology and pedagogy. Secondly, the structure of consciousness expressed by Dewey has three forms such as knowledge, feeling and will(or volition). This forms are too dynamic and unitary. Dewey considered cognition, feeling, will to be integral functions of each self. The tripartite functions of self, moreover, are unified in will. In other word, will combines subjective feeling and objective knowledge as one self. Will regulates impulse because it powers some stimulus into activity of self. In this view point, his theory of consciousness differs from traditional theories about consciousness for emphasizing dynamic relations and functions. Thirdly, Dewey's theory of consciousness will give some important implications to educational field. It is necessary to fundamental arguments about conscious conditions of learners as a human. For it is impossible to establish some aim of learning, to organize meaningful contents of learning, and also to create some effective methods of learning without consideration of this conditions. And it is important to construct and organize the contents and methods of learning for widening and deepening of educational experiences. Then consciousness and experiences of learners interact each other, so then they will produce some meaningful results of learning in this process.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.6
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• pp.2168-2187
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• 2021

The smart grid replaces the traditional power structure with information inventiveness that contributes to a new physical structure. In such a field, malicious information injection can potentially lead to extreme results. Incorrect, FDI attacks will never be identified by typical residual techniques for false data identification. Most of the work on the detection of FDI attacks is based on the linearized power system model DC and does not detect attacks from the AC model. Also, the overwhelming majority of current FDIA recognition approaches focus on FDIA, whilst significant injection location data cannot be achieved. Building on the continuous developments in deep learning, we propose a Deep Learning based Locational Detection technique to continuously recognize the specific areas of FDIA. In the development area solver gap happiness is a False Data Detector (FDD) that incorporates a Convolutional Neural Network (CNN). The FDD is established enough to catch the fake information. As a multi-label classifier, the following CNN is utilized to evaluate the irregularity and cooccurrence dependency of power flow calculations due to the possible attacks. There are no earlier statistical assumptions in the architecture proposed, as they are "model-free." It is also "cost-accommodating" since it does not alter the current FDD framework and it is only several microseconds on a household computer during the identification procedure. We have shown that ANN-MLP, SVM-RBF, and CNN can conduct locational detection under different noise and attack circumstances through broad experience in IEEE 14, 30, 57, and 118 bus systems. Moreover, the multi-name classification method used successfully improves the precision of the present identification.

• Journal of Intelligence and Information Systems
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• v.29 no.2
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• pp.115-127
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• 2023

Video stabilization is one of the camera technologies that the importance is gradually increasing as the personal media market has recently become huge. For deep learning-based video stabilization, existing methods collect pairs of video datas before and after stabilization, but it takes a lot of time and effort to create synchronized datas. Recently, to solve this problem, unsupervised learning method using only unstable video data has been proposed. In this paper, we propose a network structure that learns the stabilized trajectory only with the unstable video image without the pair of unstable and stable video pair using the Convolutional Auto Encoder structure, one of the unsupervised learning methods. Optical flow data is used as network input and output, and optical flow data was mapped into grid units to simplify the network and minimize noise. In addition, to generate a stabilized trajectory with an unsupervised learning method, we define the loss function that smoothing the input optical flow data. And through comparison of the results, we confirmed that the network is learned as intended by the loss function.