• Journal of Korean Elementary Science Education
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• v.41 no.2
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• pp.338-355
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• 2022

This study explored the effects of pre-service elementary teachers' inquiries into science class dilemmas. By closely examining the characteristics of the pre-service teachers' inquiry processes and changes in their educational decisions, the effectiveness of using dilemmas as part of teacher education was determined. Twenty fourth-year university pre-service teachers participated and conducted inquiries into science class dilemmas over seven weeks. Based on pre- and post-questionnaires, KWHL tables, inquiry reports, discussions, and group class presentations, the major factors that influence the pre-service teacher's decision-making changes were extracted. The pre-service teachers found the science inquiry process meaningful when exploring the science topics covered in the dilemmas, and claimed that elementary school students would be able to engage in meaningful science explorations if they learned science through inquiry. Furthermore, the pre-service teachers explored the thinking processes and background knowledge of the students in different ways. Documents such as teacher's guides and the curriculum were examined and the students' thought processes were identified through interviews with the teachers and students, which were found to reflect their educational decision-making. Moreover, it was recognized by the pre-service teachers that depending on the situation, alternative teaching methods were possible. The focus on the unstructured dilemma problems provided the pre-service teachers with problem-solving situations that triggered scientific inquiry and exploration of student thinking and revealed the complexity of science teaching and learning. Based on these results, the teacher education implications for using dilemma cases are discussed.

• Journal of the Korea Society of Computer and Information
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• v.27 no.10
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• pp.29-41
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• 2022

Recently, with the development of deep learning technology, a variety of huge models with excellent performance have been devised by pre-training massive amounts of text data. However, in order for such a model to be applied to real-life services, the inference speed must be fast and the amount of computation must be low, so the technology for model compression is attracting attention. Knowledge distillation, a representative model compression, is attracting attention as it can be used in a variety of ways as a method of transferring the knowledge already learned by the teacher model to a relatively small-sized student model. However, knowledge distillation has a limitation in that it is difficult to solve problems with low similarity to previously learned data because only knowledge necessary for solving a given problem is learned in a teacher model and knowledge distillation to a student model is performed from the same point of view. Therefore, we propose a heterogeneous knowledge distillation method in which the teacher model learns a higher-level concept rather than the knowledge required for the task that the student model needs to solve, and the teacher model distills this knowledge to the student model. In addition, through classification experiments on about 18,000 documents, we confirmed that the heterogeneous knowledge distillation method showed superior performance in all aspects of learning efficiency and accuracy compared to the traditional knowledge distillation.

• Geophysics and Geophysical Exploration
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• v.25 no.3
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• pp.140-161
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• 2022

Seismic data with missing traces are often obtained regularly or irregularly due to environmental and economic constraints in their acquisition. Accordingly, seismic data interpolation is an essential step in seismic data processing. Recently, research activity on machine learning-based seismic data interpolation has been flourishing. In particular, convolutional neural network (CNN) and generative adversarial network (GAN), which are widely used algorithms for super-resolution problem solving in the image processing field, are also used for seismic data interpolation. In this study, CNN-based algorithm, U-Net and GAN-based algorithm, and conditional Wasserstein GAN (cWGAN) were used as seismic data interpolation methods. The results and performances of the methods were evaluated thoroughly to find an optimal interpolation method, which reconstructs with high accuracy missing seismic data. The work process for model training and performance evaluation was divided into two cases (i.e., Cases I and II). In Case I, we trained the model using only the regularly sampled data with 50% missing traces. We evaluated the model performance by applying the trained model to a total of six different test datasets, which consisted of a combination of regular, irregular, and sampling ratios. In Case II, six different models were generated using the training datasets sampled in the same way as the six test datasets. The models were applied to the same test datasets used in Case I to compare the results. We found that cWGAN showed better prediction performance than U-Net with higher PSNR and SSIM. However, cWGAN generated additional noise to the prediction results; thus, an ensemble technique was performed to remove the noise and improve the accuracy. The cWGAN ensemble model removed successfully the noise and showed improved PSNR and SSIM compared with existing individual models.

• Journal of Industrial Convergence
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• v.20 no.11
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• pp.113-126
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• 2022

In Small and Medium-sized Enterprises(SMEs) compared to major, competence of CEO relatively has a large impact on management performance, so the biggest factor to strengthen the competitiveness is the competence of CEO. Meanwhile, a consultant is defined as a subject of execution that directly and indirectly participates in management by inducing objective and rational decision-making on various management issues and problems facing companies. The management expertise, problem-solving skills, communication skills, insights, and leadership that a consultant must have in order to perform his or her duties are the same as the role and capabilities that the CEO must have in enhancing the company's performance and competitiveness. Therefore, through previous studies, this study divided consultant competences of CEO into job competence, communication competence, learning competence, and innovation competence and tried to understand whether those competences affect corporate's innovation performance and management performance. The survey was conducted on SMEs and the analysis techniques were reliability and validity analysis, confirmatory factor analysis, and structural equation analysis. As a result, it was found that the CEO's job competence, communication competence, learning competence, and innovation competence had a significant effect on innovation performance of the company, and second, innovation performance had a significant effect on the management performance. Through, this study derived a common factor of consultant competences of SMEs CEO, and derived implications for the competence characteristics of the CEO necessary to improve the performance of SMEs.

• The Mathematical Education
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• v.62 no.2
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• pp.289-302
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• 2023

In today's digital information society, student knowledge and skills to analyze big data and make informed decisions have become an important goal of school mathematics. Integrating big data statistical projects with digital technologies in high school <Artificial Intelligence> mathematics courses has the potential to provide students with a learning experience of high impact that can develop these essential skills. This paper proposes a set of guidelines for designing effective big data statistical project-based tasks and evaluates the tasks in the artificial intelligence mathematics textbook against these criteria. The proposed guidelines recommend that projects should: (1) align knowledge and skills with the national school mathematics curriculum; (2) use preprocessed massive datasets; (3) employ data scientists' problem-solving methods; (4) encourage decision-making; (5) leverage technological tools; and (6) promote collaborative learning. The findings indicate that few textbooks fully align with these guidelines, with most failing to incorporate elements corresponding to Guideline 2 in their project tasks. In addition, most tasks in the textbooks overlook or omit data preprocessing, either by using smaller datasets or by using big data without any form of preprocessing. This can potentially result in misconceptions among students regarding the nature of big data. Furthermore, this paper discusses the relevant mathematical knowledge and skills necessary for artificial intelligence, as well as the potential benefits and pedagogical considerations associated with integrating technology into big data tasks. This research sheds light on teaching mathematical concepts with machine learning algorithms and the effective use of technology tools in big data education.

• Journal of Korea Society of Industrial Information Systems
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• v.28 no.4
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• pp.45-58
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• 2023

In a world of uncertainty and complexity, leadership is essential to lead collaborative and positive interactions among employees. In other words, if members share opinions and work through voluntary leadership, they will respond more effectively to uncertain challenges and get closer to the targeted management performance. Therefore, in this study, we would like to elucidate the importance of shared leadership, which has recently become an issue. We will examine the impact of shared leadership on team efficacy, team organizational citizenship behavior, and turnover intention. A survey was conducted among members working in a team organization in Busan, and the results were as follows. First, the effects of shared leadership on team efficacy were found to have significant positive(+) effects, such as the hypotheses set at planning and organizing 0.202(C.R.=2.853), problem solving 0.463(C.R.=5.620), support and caring 0.237(C.R.=3.326), and development and mentoring 0.366(C.R.=5.132), respectively. Second, the effects of team efficacy on team organizational citizenship behavior and turnover intention were 0.545(C.R.=5.895) and -0.143(C.R.=-0.817), respectively, and team efficacy was found to have a positive(+)positive(+) effect on team organizational citizenship behavior, but team efficacy did not have a significant effect on turnover intention.

• The Korean Society of Law and Medicine
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• v.24 no.1
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• pp.101-130
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• 2023

Korea has very easy access to tertiary hospitals, including university hospitals, among OECD countries, and patients can reach the emergency room of a university hospital within 1-2 hours. However, there are many so-called 'essential medical' blind spots where people die because they do not receive surgery in time. In particular, in the case of essential medical care in the surgical field, despite basic surgery, there is a shortage of medical staff to perform outpatient surgery in emergency situations at university hospitals. Although this lack of manpower has a problem with low insurance premiums for surgery, it also has a very large impact on the burden of criminal liability for medical malpractice, which increases the incidence in case of emergencies. Here, we propose crime immunity to solve the manpower shortage of converged smart surgical essential medical (SES) hospitals. Currently, the medical community agrees on the need for crime immunity, but it is an ambiguous scope of immunity that has not reached a national consensus. We would like to present clear standards for essential medical practices (surgery) that require criminal immunity.

• Journal of Korean Home Economics Education Association
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• v.34 no.4
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• pp.77-92
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• 2022

Recently, digital transformation in the financial industry has been accelerated, and it has become an important task to improve the level of utilization of Internet banking by elderly consumers, who are vulnerable to Internet use. Accordingly, this study analyzed 3,101 respondents in their 60s or older from the 11th year of the Media Panel Survey to identify demographic, experiential, and psychological factors that affect the self-efficacy of elderly consumers' usage of Internet banking. The main research findings are as follows. First, gender, education, occupation, and income were identified as demographic variables. Second, the Internet shopping experience was identified as an experiential factor. Also, concerns about information security, digital literacy, and high will for problem-solving were identified as psychological factors. Third, as a result of the moderating effect analysis on whether the experiential and psychological factors have different influences according to the group divided into the 60s and 70s, the effect on self-efficacy in the usage of the Internet was classified by age. The results of this study will be able to enrich the discussions related to the intention to utilize technology among elderly consumers by empirically revealing that there are characteristics that cause differences in financial behavior even within one group called the elderly.

• Journal of Korean Home Economics Education Association
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• v.34 no.4
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• pp.93-111
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• 2022

This study started with the question of 'What innovative task should elementary and secondary school clothing life education perform in accordance with the changes in the future educational environment?' It is time to prepare for a major shift in the educational paradigm that improves the quality of life for all everyone, based on social innovations such as the 4th industrial revolution and the transition to the post-corona era. This study examined the literature for the characteristics of changes in the future educational environment from an educational perspective, and examined the curriculum focusing on the clothing life with the porpose of presenting the direction for the clothing life education. In order to carry out this study, various literature including previous studies related to clothing life education and the national curriculum from the first curriculum to the 2015 revision were analyzed. In conclusion, the direction of the clothing life education curriculum according to the changes in the future educational environment is proposed as follows: First, nurturing convergence education experts that can combine human emotion, environment, and clothing life culture to artificial intelligence(AI); second, developing a clothing life education curriculum that links software competency and practical problem-solving competency; and lastly, implementing fashion maker education using artificial intelligence(AI) and value-oriented clothing life education. In the future, it is expected that the direction of teaching/learning methods and evaluation in clothing life education curriculum is proposed, and that this educational discussion process will help establish the identity of clothing life education in school education.

• 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.