• Journal of Korean society of Dental Hygiene
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• 제18권5호
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• pp.729-740
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• 2018

Objectives: The purpose of this study was to investigate the factors affecting robot utilization in the education of pre-dental hygienists. Methods: A self-reported questionnaire was completed by 238 dental hygiene students studying in the Daejeon, Chungcheong, and Jeolla provinces during the period March 1-31, 2017. Results: Future oral health education media had high selection of 'movies,' 'video,' '3D printer,' 'robot,' and 'drone' In general education and oral health education, robots were appropriate as educators, assistant teachers, and media. This group had high levels of interest, experience, attitude, and learning scope of robots. Robot utilization education showed a significant positive correlation with the 'interest,' 'experience,' 'attitude,' and 'learning' subfactors (p<0.01). Factors influencing robot utilization education were the relationships among actual experience of robot, learning of robot production, social influence of robot, emotional exchange with robot, and the predictive power was 25.5% (p<0.05). Conclusions: Oral health education curricula using robots should be developed considering the emotional exchange and social influence between educator and learner.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 한국정보통신학회 2018년도 추계학술대회
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• pp.228-230
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• 2018

Recently, many universities have opened lectures on programming in liberal arts subjects. However, learners find it very difficult to learn programming, and efforts to increase interest and interest continue. Especially, in the previous research, it was found that there is a difference in programming learning between boys and girls. In this study, we analyzed the resilience of learners of liberal arts programming lectures. The pre-test was conducted on 399 students. It was found that the male students had a higher average resilience than the female students but a larger deviation. Through this study, we can find a plan for learners' learning strategy for programming learning.

• Journal of Industrial Technology
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• 제39권1호
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• pp.27-31
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• 2019

Semiconductor logistics systems are facing difficulties in increasing production as production processes become more complicated due to the upgrading of fine processes. Therefore, the purpose of the research is to design predictive models that can predict traffic during the pre-planning stage, identify the risk zones that occur during the production process, and prevent them in advance. As a solution, we build FABs using automode simulation to collect data. Then, the traffic prediction model of the areas of interest is constructed using deep learning techniques (keras - multistory conceptron structure). The design of the predictive model gave an estimate of the traffic in the area of interest with an accuracy of about 87%. The expected effect can be used as an indicator for making decisions by proactively identifying congestion risk areas during the Fab Design or Factory Expansion Planning stage, as the maximum traffic per section is predicted.

• Smart Media Journal
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• 제10권2호
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• pp.22-29
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• 2021

In the past decade, deep learning has been applied to various medical image analysis tasks. Skeletal bone age estimation is clinically important as it can help prevent age-related illness and pave the way for new anti-aging therapies. Recent research has applied deep learning techniques to the task of bone age assessment and achieved positive results. In this paper, we propose a bone age prediction method using a deep convolutional neural network. Specifically, we first train a classification model that automatically localizes the most discriminative region of an image and crops it from the original image. The regions of interest are then used as input for a regression model to estimate the age of the patient. The experiments are conducted on a whole-body scintigraphy dataset that was collected by Chonnam National University Hwasun Hospital. The experimental results illustrate the potential of our proposed method, which has a mean absolute error of 3.35 years. Our proposed framework can be used as a robust supporting tool for clinicians to prevent age-related diseases.

• Educational Technology International
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• 제21권2호
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• pp.193-216
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• 2020

This study investigated university students' perspectives on good class and instructional practices through social network analysis. The subjects were 321 students in the third and fourth academic years in a Korean university. The subjects completed four open-ended questions, asking about experience of good class, good instructors' teaching practice, and their feelings and attitudes when participating in good class. As social network analysis, KrKwic (Korea Key Words in Context) was used to compute word frequencies and analyze semantic network structures and Ucinet Netdraw to assess centrality in the social network, consisting of degree centrality, closeness centrality, and between centrality. The results are as follows. First, students showed 5 keywords to depict what good class is, including 'understanding', 'example', 'video', 'interest', and 'communication'. Second, the characteristics of teaching methods by professors who practice good class indicate 'assignments', 'questions', 'understanding', 'example', and 'feedback'. Third, the top 5 keywords of students' attitudes as participating in good class are 'active', 'participation', 'focus', 'listening', and 'asking'. Last, keywords depicting desirable class that students most wanted to take next time are 'assignments', 'rewards', 'understanding', 'difficulty', and 'interest'. The findings from this study include the meanings of the semantic network structures of words in the text making up messages. Also this study can provide empirical evidence for educators and educational practitioners in higher education to create effective learning environments.

• Journal of the Korean Society of Industry Convergence
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• 제25권3호
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• pp.467-476
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• 2022

The Bank of Korea raised the benchmark interest rate by a quarter percentage point to 1.75 percent per year, and analysts predict that South Korea's policy rate will reach 2.00 percent by the end of calendar year 2022. Furthermore, because market volatility has been significantly increased by a variety of factors, including rising rates, inflation, and market volatility, many investors have struggled to meet their financial objectives or deliver returns. Banks and financial institutions are attempting to provide Robo-Advisors to manage client portfolios without human intervention in this situation. In this regard, determining the best hyper-parameter combination is becoming increasingly important. This study compares some activation functions of the Deep Deterministic Policy Gradient(DDPG) and Twin-delayed Deep Deterministic Policy Gradient (TD3) Algorithms to choose a sequence of actions that maximizes long-term reward. The DDPG and TD3 outperformed its benchmark index, according to the results. One reason for this is that we need to understand the action probabilities in order to choose an action and receive a reward, which we then compare to the state value to determine an advantage. As interest in machine learning has grown and research into deep reinforcement learning has become more active, finding an optimal hyper-parameter combination for DDPG and TD3 has become increasingly important.

• Journal of Korean Dental Hygiene Science
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• 제5권2호
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• pp.61-72
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• 2022

Background: This study was aimed at surveying the health science students' interest, demand, and awareness of virtual reality (VR) devices for learning to accumulate data necessary to develop and implement a curriculum with VR devices. Methods: We investigated the perception of health science students regarding VR device application and utilization. Statistical analyses were performed using SPSS 25.0 (IBM SPSS Statistics). Frequency and descriptive analyses were performed for the perception level of VR device use for university education. An independent twosamples t-test was performed to statistically analyze the perception level according to the VR device experience. A p-value < 0.05 was set to indicate statistical significance. Results: To the question "Do you wish to use VR devices for educational purposes?," 73% of the participants answered "yes." To the question "Do you think VR is necessary for the course curriculum?," over 65% answered "yes." Conclusion: In this study, health science students reported a great need for VR devices for education. VR-based classroom curriculum is expected to improve students' concentration, interest, and motivation.

• Journal of Korean Elementary Science Education
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• 제35권2호
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• pp.253-264
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• 2016

The purpose of this study was to investigate implications of self-directed learning experiments in elementary science education through understanding elementary school students' awareness of their experiences in self-directed learning experiments. Twenty students joined the school science club voluntarily and conducted self-directed learning experiments. We collected data through observation of the experiments, interviews, and questionnaires. The students who participated in the club showed high satisfaction with self-directed learning experiments. The participants were aware that their scientific interest and knowledge, and the confidence in conducting experiments were increased. The students felt positive about the inquiry process of conducting self-directed learning experiments with their own subjects. They also felt a sense of achievement in attempting their experiments in defiance of several failures. The participants realized that the self-directed inquires led to increased declarative and procedural knowledge of science. The students stated that they had some difficulties in coping with the different results contrary to expectations and preparing laboratory materials and instruments. Nonetheless, they showed the promotion of their scientific literacy during overcoming those difficulties. We suggest that self-directed learning experiments can be a more effective way in science learning to make students experience the nature of science than existing school experiments. This can be implemented through a creative experience activities such as science clubs.

• Journal of Korean Elementary Science Education
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• 제39권2호
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• pp.255-267
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• 2020

Students' engagement is important for meaningful learning and it has multifaceted aspects for their science learning. This study investigated elementary students' perceptions of science learning engagement. The subjects of this study were 341 4^th to 6^th elementary students. The survey questionnaires were 5-Likert scale questions and free response questions on science learning engagement. The results showed that elementary students' perceptions of behavioral engagement were higher than emotional and cognitive engagement. Keyword network analysis with NetMiner program showed that the frequent key words of science learning engagement were 'experiment', 'listening', and 'teachers' explanation', which were mostly the behavioral types of engagement. The degree centrality and eigenvector centrality of these key words appeared high. 'Interest', which is emotional engagement, were also one of the frequent key words, but the centralities of this word were relatively low. The Frequent key words of science learning disengagement were mostly related with off-tasks, not doing expected behaviors and negative emotions about science and science learning. Educational implications on science learning engagement were discussed.

• Journal of Engineering Education Research
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• 제20권3호
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• pp.25-31
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• 2017

As the world enters the era of the Fourth Industrial Revolution, which is represented by advanced technology, it not only changes the industrial field but also the education field. In recent years, Smart Learning has enriched learning by using diverse forms and technologies that utilize vast amount of information about learners' individual knowledge through the emergence of realistic and intelligent contents that combine high technology such as artificial intelligence, big data and virtual reality and there is an increasing interest in intelligent adaptive learning, which can customize individual education. Therefore, the purpose of this study is to explore intelligent adaptive learning method through recent smart education environment, beyond traditional writing-based communication education which is highly dependent on the competency of instructors. In this study, we analyzed the various learner information collected in the communication course and constructed a concrete teaching and learning method of intelligent adaptive learning based on the instructor's intended smart contents. The result of this study is expected to be the basis of highly personalized teaching and learning method of digital method in communication education which is emphasized in the fourth industrial revolution era.