• Journal of Software Assessment and Valuation
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• v.16 no.2
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• pp.127-133
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• 2020

As the usages of artificial intelligence is increased, the demand to algorithms for small portable devices increases. Also as the embedded system becomes high-performance, it is possible to implement algorithms for high-speed computation and machine learning as well as operating systems. As the machine learning algorithms process repetitive calculations, it depend on the cloud environment by network connection. For an stand alone system, low power consumption and fast execution by optimized algorithm are required. In this study, for the purpose of smart control, an energy measurement sensor is connected to an embedded system, and a real-time monitoring system is implemented to store measurement information as a database. Continuously measured and stored data is applied to a learning algorithm, which can be utilized for optimal power control, and a system interfacing various sensors required for energy measurement was constructed.

• Journal of The Korean Association For Science Education
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• v.20 no.4
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• pp.611-623
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• 2000

The cooperative learning movement began as parts of the desegregation process in America, aiming at increasing academic achievement and social skills among diverse students. Cooperative learning may be defined as a classroom learning environment in which students work together in small heterogeneous groups. Although many studies have shown the effectiveness of cooperative learning in a variety of subjects, relatively few have focused on biology. In this study, we investigated the effects of cooperative learning on students' achievement and attitude of middle school biology students. For this purpose this study compared three sections. In one section, a cooperative learning strategy was used. Second section was taught in small groups and the third section was instructed in the traditional method. The unit 'Structures and functions of animals' was used. A total of 188 students were included in this study. These classes were treated for 10hours during 10weeks from September 1 to November 28, 1999. The pretests-posttests control group design was applyed. An analysis of covariance(ANCOVA) was used as the data analysis procedure. Significant differences were found in the achievement and the attitude of students using cooperative learning strategy(p<.05) when compared to traditional classroom structure and small group learning. Cooperative learning was more effective in the low-ability and average-ability students than the high-ability students in the science achievement. Cooperative learning is effective in both male and female students. And students in the cooperative group achieved better than those in other groups in affective, behavioral, and intention-cognitive domain of science attitude.

• The Journal of the Korea Contents Association
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• v.21 no.12
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• pp.175-181
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• 2021

The purpose of this study is to improve and develop the quality of non-face-to-face classes according to the types of presence by analyzing the effects of teaching presence and learning presence on the learning satisfaction of the non-face-to-face classes that have been suddenly conducted due to COVID-19. For this purpose, a survey on online classes of H University in Gwangju Metropolitan City was conducted to analyze learning satisfaction, teaching presence (learning design, direct promotion), and learning presence (cognitive presence, social presence). The results of the analysis showed that the learning contents of cognitive presence, which is a sub-factor of learning presence, were understood (=.589, p<.001), the direct promotion (=.420, p<.001), and the learning design (=.397, p<.01), which are the sub-factors of teaching presence, were influential in order.This means that the suddenly changed teaching method should have an attitude to improve the intimacy between the instructor and the fellow learners with positive emotional exchange or interaction. The instructor should try to overcome the limitations of time and space through blended learning that is both online and offline for high quality learning design, but the learning medium and learning method considering the physical fatigue of the learner should be developed.

• Journal of Internet Computing and Services
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• v.21 no.3
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• pp.123-131
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• 2020

Along with the rapid development of artificial intelligence technology, natural language processing, which deals with human language, is also actively studied. In particular, BERT, a language model recently proposed by Google, has been performing well in many areas of natural language processing by providing pre-trained model using a large number of corpus. Although BERT supports multilingual model, we should use the pre-trained model using large amounts of Korean corpus because there are limitations when we apply the original pre-trained BERT model directly to Korean. Also, text contains not only vocabulary, grammar, but contextual meanings such as the relation between the front and the rear, and situation. In the existing natural language processing field, research has been conducted mainly on vocabulary or grammatical meaning. Accurate identification of contextual information embedded in text plays an important role in understanding context. Knowledge graphs, which are linked using the relationship of words, have the advantage of being able to learn context easily from computer. In this paper, we propose a system to extract Korean contextual information using pre-trained BERT model with Korean language corpus and knowledge graph. We build models that can extract person, relationship, emotion, space, and time information that is important in the text and validate the proposed system through experiments.

• Journal of Christian Education in Korea
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• v.71
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• pp.155-179
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• 2022

This study aims to apply Hannah Arendt's concept of 'collective responsibility' to the Christian education on environmental issues around the world, focusing on climate change. This study prepares the concept of 'collective responsibility' and the concept of 'collective guilt' and emphasizes the fact that the current climate change problem should be seen as a political task rather than a task of personal ethics. According to Arendt's theory, Christian education activities applying 'collective responsibility' for climate change can become action. This study has four suggestions for Christian learning to understand and recognize climate change. First, presenting and justifying the anxiety and anger toward climate change in the classroom. Second, transcending self-interest (egocentrism) through "Common Sense (enlarged mentality)" in Kantian terms. Third, building education communities through 'citizen participatory education,' running communication, and conversation. Fourth, encouraging experience and practice in every education community with "faith expressing itself through love (Gal 5:6)." Then, to be sure, this refers to not only love of neighbor in Christianity but also political friendship (philia politikē). The academic significance of this study is that it is the first interdisciplinary research paper in Korea which dealt with Arendt's political theory in relation to Christian education. Although it claims to be a theoretical work that applies Arendt's political theory from a systematic theological perspective to Christian education, the author is proud that it is accompanied by practical elements that can be actualized in the education field.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.6
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• pp.710-715
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• 2003

In this paper, we propose the new emotion recognition method for intelligently recognizing the human's emotion using the template vector and neural network. In the proposed method, human's emotion is divided into four emotion (surprise, anger, happiness, sadness). The proposed method is based on the template vector extraction and the template location recognition by using the color difference. It is not easy to extract the skin color area correctly using the single color space. To solve this problem, we propose the extraction method using the various color spaces and using the each template vectors. And then we apply the back-propagation algorithm by using the template vectors among the feature points). Finally, we show the practical application possibility of the proposed method.

• Journal of Software Assessment and Valuation
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• v.16 no.2
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• pp.63-68
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• 2020

This paper proposes a face authentication system based on deep learning framework. The proposed system is consisted of face region detection and feature extraction using deep learning algorithm, and performed the face authentication using joint-bayesian matrix learning algorithm. The performance of proposed paper is evaluated by various face database , and the face image of one person consists of 2 images. The face authentication algorithm was performed by measuring similarity by applying 2048 dimension characteristic and combined Bayesian algorithm through Deep Neural network and calculating the same error rate that failed face certification. The result of proposed paper shows that the proposed system using deep learning and joint bayesian algorithms showed the equal error rate of 1.2%, and have a good performance compared to previous approach.

• The Journal of the Convergence on Culture Technology
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• v.6 no.4
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• pp.165-170
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• 2020

In this study, after applying VR 360-degree video contents for intravenous infusion education, basic data on whether VR 360-degree video can be applied as educational content in the future is prepared by grasping the empathy and flow of nursing students in graduating grades. The VR 360 degree intravenous infusion educational content was developed in four-step process of planning, production, modification and completion. The design of this study was descriptive research, and the study period was from November 9 to November 22, 2019. The subjects of this study were 4th grade nursing students at a university, totaling 64 students. Nursing students watched VR 360 degree intravenous infusion educational content using HMD(head mounted display) under the safety management of the researcher. As a result of the study, the empathy of nursing students was 5.32±0.88 points and the flow was 6.02±0.84 points out of 7-point scale. The VR 360 degree intravenous infusion educational content developed in this study can be used as an educational medium in subjects and comparative departments, and it is necessary to specifically develop and verify teaching and learning methods in future studies.

• Journal of Software Assessment and Valuation
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• v.17 no.1
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• pp.31-40
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• 2021

Plagiarism refers to the act of using the original data as if it were one's own without revealing the source. The plagiarism of source code causes a variety of problems, including legal disputes. Plagiarism in software projects is usually determined by measuring similarity by comparing every pair of source code within two projects. However, blindly comparing every pair has been a huge computational burden, causing a major factor of not using tools of better accuracy. If we can only compare pairs that are probable to be clones, eliminating pairs that are impossible to be clones, we can concentrate more on improving the accuracy of detection. In this paper, we propose a method of selecting highly probable candidates of clone pairs by pre-classifying suspected source-codes using a machine-learning model called code2vec.

• Journal of Korean Elementary Science Education
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• v.42 no.3
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• pp.421-433
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• 2023

This study examined the reasoning of gifted elementary science students in a socioscientific issues (SSI) classroom discussion on COVID-19-related trash disposal challenges. This study aimed to understand the characteristics of evidence use and decision-making difficulties in each type of SSI-related reasoning. To this end, the transcripts of 17 gifted students of elementary science discussing SSIs in a classroom were analyzed within the framework of informal reasoning. The analysis framework was categorized into three types according to the primary influence involved in reasoning: rational, emotional, and intuitive. The analysis showed that students exhibited four categories of evidence use in SSI reasoning. First, in the rational reasoning category, students deemed and recorded scientific knowledge, numbers, and statistics as objective evidence. However, students who experienced difficulty in investigating such scientific data were less likely to have factored them in subsequent decisions. Second, in the emotional reasoning category, students' solutions varied considerably depending on the perspective they empathized with and reasoned from. Differences in their views led to conflicting perspectives on SSIs and consequent disagreement. Third, in the intuitive reasoning category, students disagreed with the opinions of their peers but did not explain their positions precisely. Intuitive reasoning also created challenges as students avoided problem-solving in the discussion and did not critically examine their opinions. Fourth, a mixed category of reasoning emerged: intuition combined with rationality or emotion. When combined with emotion, intuitive reasoning was characterized by deep empathy arising from personal experience, and when combined with rationality, the result was only an impulsive reaction. These findings indicate that research on student understanding and faculty knowledge of SSIs discussed in classrooms should consider the difficulties in informal reasoning and decision-making.