• 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.28 no.9
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• pp.177-188
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• 2023

The purpose of this study is to develop and validate a scale for measuring digital literacy by identifying the factors consisting of digital literacy and extracting items for each factor. Preliminary items for the Delphi study were developed through the analysis of previous literature and the deliberation of the research team. As a result of two rounds of the expert Delphi study, 65 items were selected for the main survey. The validation of the items was carried out in the process of exploratory and confirmatory factor analyses, reliability test, and criterion validity test using the data collected in the main survey. As a result, a 4-factor structure composed of 31 questions(factor 1: digital technology & data literacy- 9 questions, factor 2: digital content & media literacy- 8 questions, factor 3: digital communication & community literacy- 9 questions, factor 4: digital wellness literacy - 5 questions) was confirmed. Also, the goodness of fit indices of the model were found to be good and the result of reliability test revealed the scale had a very appropriate level of Cronbach's alpha(α=.956). In addition, a statistically significantly positive correlations(p<.001) were found between digital literacy and internet self-efficacy and between digital literacy and self-directed learning ability, which were predicted in the existing evidence, therefore the criterion validity of the developed scale was secured. Finally, practical and academic implications of the study are provided and future study and limitations of the study are discussed.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.16 no.2
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• pp.104-108
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• 2023

Because of recent expansion of the electric car market. it is highly growing that should be supplemented its performance and safely issue. The EMI problem due to the interlocking of electrical components that causes various safety problems such as fire in electric vehicles is emerging every time. We strive to achieve optimal charging efficiency by combining various technologies and reduce radioactive noise among the EMI noise of a weirless charging control module, one of the important parts of an electric vehicle was designed and tested. In order to analyze the EMI problems occurring in the wireless charging control module, the optimized wireless charging control module by applying the optimization design technology by learning the accumulated test data for critical factors by utilizing the Python-based script function in the Ansys simulation tool. It showed an EMI noise improvement effect of 25 dBu V/m compared to the charge control module. These results not only contribute to the development of a more stable and reliable weirless charging function in electric vehicles, but also increase the usability and efficiency of electric vehicles. This allows electric vehicles to be more usable and efficient, making them an environmentally friendly alternative.

• Journal of Technology Innovation
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• v.30 no.1
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• pp.57-104
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• 2022

The government has supported the innovation of private firms by intervening the market for various purposes, such as preventing market failure, alleviating information asymmetry, and allocating resources efficiently. Although the government's R&D budget increased rapidly in the 2000s, it is not clear whether the government intervention has made desirable impact on the market. To address this, the current study attempts to explore this issue by doing a systematic literature review on foreign and domestic papers in an integrated way. In total, 168 studies are analyzed using contents analysis approach and various lens, such as policy additionality, policy tools, firm size, unit of analysis, data and method, are adopted for analysis. Overlapping policy target, time lag between government intervention and policy effects, non-linearity of financial supports, interference between different polices, and out-dated R&D tax incentive system are reported as factors hampering the effect of the government intervention. Many policy prescriptions, such as program evaluation indices reflecting behavioral additionality, an introduction of policy mix and evidence-based policy using machine learning, are suggested to improve these hurdles.

• Journal of Intelligence and Information Systems
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• v.28 no.1
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• pp.217-242
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• 2022

With the launch of Artificial Intelligence(AI)-based intelligent products on the market, innovative changes are taking place not only in business but also in consumers' daily lives. Intelligent products have the potential to realize technology differentiation and increase market competitiveness through advanced functions of artificial intelligence. However, there is no new product development methodology that can sufficiently reflect the characteristics of artificial intelligence for the purpose of developing intelligent products with high market acceptance. This study proposes a KANO-QFD integrated model as a methodology for intelligent product development. As a specific example of the empirical analysis, the types of consumer requirements for hair loss prediction and treatment device were classified, and the relative importance and priority of engineering characteristics were derived to suggest the direction of intelligent medical product development. As a result of a survey of 130 consumers, accurate prediction of future hair loss progress, future hair loss and improved future after treatment realized and viewed on a smartphone, sophisticated design, and treatment using laser and LED combined light energy were realized as attractive quality factors among the KANO categories. As a result of the analysis based on House of Quality of QFD, learning data for hair loss diagnosis and prediction, micro camera resolution for scalp scan, hair loss type classification model, customized personal account management, and hair loss progress diagnosis model were derived. This study is significant in that it presented directions for the development of artificial intelligence-based intelligent medical product that were not previously preceded.

• Journal of Korean Library and Information Science Society
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• v.54 no.2
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• pp.43-65
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• 2023

The purpose of all public libraries is to provide knowledge and information services for the intellectual and reading activities of local residents, program services that provide opportunities for cultural enjoyment and lifelong learning, and third-party spaces and facilities that contribute to the development of the community. To this end, social needs must be reflected in a timely manner with the establishment of a sound infrastructure as a prerequisite. This study analyzed the current status centered on key indicators of the public libraries in Daegu City and presented policy issues that need to be improved through a survey. The key indicators in Daegu City, compared to the national average, were not only significantly weak for the 3rd largest city but also showed considerable variation among local governments. While Daegu citizens valued public libraries in their daily lives, the dissatisfaction rate was high in the order of transportation inconvenience, lack of necessary materials and desired programs, and various regulations. Therefore, Daegu City should focus on increasing the acquisition budget, strengthening the development of new book collections, expanding the number of librarians, expanding the construction of public libraries to address service disparities among local governments, improving accessibility, and addressing factors that hinder usage. In addition, with the establishment and operation of the Daegu Library in 2024, efforts should be made to establish an operational system for public libraries, expand the scope through collaborative partnerships with other knowledge and cultural institutions, and enhance knowledge and cultural services for the Daegu citizen.

• Journal of Korea Water Resources Association
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• v.56 no.7
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• pp.439-449
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• 2023

Recent advancements in data measuring technology have facilitated the installation of various sensors, such as pressure meters and flow meters, to effectively assess the real-time conditions of water distribution systems (WDSs). However, as cities expand extensively, the factors that impact the reliability of measurements have become increasingly diverse. In particular, demand data, one of the most significant hydraulic variable in WDS, is challenging to be measured directly and is prone to missing values, making the development of accurate data generation models more important. Therefore, this paper proposes an adversarially trained autoencoder (ATAE) model based on generative deep learning techniques to accurately estimate demand data in WDSs. The proposed model utilizes two neural networks: a generative network and a discriminative network. The generative network generates demand data using the information provided from the measured pressure data, while the discriminative network evaluates the generated demand outputs and provides feedback to the generator to learn the distinctive features of the data. To validate its performance, the ATAE model is applied to a real distribution system in Austin, Texas, USA. The study analyzes the impact of data uncertainty by calculating the accuracy of ATAE's prediction results for varying levels of uncertainty in the demand and the pressure time series data. Additionally, the model's performance is evaluated by comparing the results for different data collection periods (low, average, and high demand hours) to assess its ability to generate demand data based on water consumption levels.

• The Journal of the Convergence on Culture Technology
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• v.9 no.4
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• pp.303-308
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• 2023

The purpose of this study is to provide a practical guide for establishing BSC that can be practically applied by SMEs. To this end, a case study was conducted to establish a performance evaluation system through a field-required Balanced Scorecard (BSC) for company J, a tent pole manufacturer, and to provide a management strategy system map. As a survey method, the requirements of the ordering organization were organized through a comparison of the BSC-related proposal requests in the first stage. The BSC establishment method was organized through the arrangement of the second stage result report. The 3rd stage BSC derived KPI indicators for SMEs for each of the 4 perspectives. A corporate vision was derived through a 4-step SWOT analysis. A strategy map was developed through 5-step field-required KPI, weight setting, and BSC. The 6-step final strategy system was also drawn up. As a result of the study, the four perspectives of the BSC were reconstructed by department. That is, the financial (financial) perspective is from the executives' perspective, the customer's perspective is from the sales department's perspective, the internal process perspective is from the design department/production quality department's perspective, and the learning/innovation perspective is from the management department's perspective. In addition, a total of 11 CSFs and a total of 49 KPIs of J company were derived. The limitation of the study is that the final strategy system through the company's BSC has only been carried out, and it needs to be linked with the company's compensation system in the future.

• Journal of the Korean earth science society
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• v.44 no.1
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• pp.79-89
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• 2023

The purpose of this study was to investigate the effects of engineering design-based ocean cleanup classes on STEAM attitudes and creative engineering problem-solving dispositions. Furthermore, during this process, we tried to determine interesting points that students encountered in engineering design-based classes. For this study, a science class with six lessons based on engineering design was developed and reviewed by a professor who majored in engineering design, along with five engineering design experts with a master's degree or higher. The subject of the class was selected as the design and implementation of scientific and engineering measures to reduce marine pollution based on the method implemented in an actual Ocean Clean-up Project. The engineering design process utilized the engineering design model presented by NGSS (2013), and was configured to experience redesign through the optimization process. To verify effectiveness, the STEAM attitude questionnaire developed by Park et al. (2019) and the creative engineering problemsolving propensity test tool developed by Kang and Nam (2016) were used. A pre and post t-test was used for statistical analysis for the effectiveness test. In addition, the contents of interesting points experienced by the learners were transcribed after receiving descriptive responses, and were analyzed and visualized through degree centrality analysis. Results confirmed that engineering design in science classes had a positive effect on both STEAM attitude and creative engineering problem-solving disposition (p< .05). In addition, as a result of unstructured data analysis, science and engineering knowledge, engineering experience, and cooperation and collaboration appeared as factors in which learners were interested in learning, confirming that engineering experience was the main factor.

• Educational Technology International
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• v.6 no.2
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• pp.23-41
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• 2005

One of the main factors that determine the quality of instruction is the teaching ability of the instructor administering the class. To evaluate teaching ability, methods such as peer review, student feedback, and teaching portfolio can be used. Among these, because feedback from the students is directly associated with how well the students feel they have learned, it is essential to improving instruction and teaching ability. The principal aim of instruction evaluation lies in the evaluation of instructor's qualification and the improvement of instruction quality by enhancing professionalism. However, the mandatory instruction evaluations currently being carried out at the term's end in universities today have limitations in improving instruction in terms of its evaluation items and times. To improve the quality of instruction and raise teaching abilities, instruction evaluations should not stop at simply being carried out but also be utilized as useful data for students and teachers. In other words, they need to be used to develop teaching and improve instruction for teachers, and consequently, should also exert a positive influence on students' scholastic achievements and learning ability. The most important thing in evaluation is the acquisition of accurate information and how to utilize it to improve instruction. The online instruction diagnosis item pool is a more realistic feedback device developed to improve instruction quality. The instruction diagnosis item pool is a cafeteria-like collection of hundreds of feedback questions provided to enable instructors to diagnose their instruction through self-diagnosis or students' feedback, and the instructors can directly select the questions that are appropriate to the special characteristics of their instruction voluntarily make use of them whenever they are needed. The current study, in order to find out if the online instruction diagnosis item pool is truly useful in reforming and improving instruction, conducted pre and post tests using 256 undergraduate students from Y university as subjects, and studied the effects of student feedback on instructions. Results showed that the implementation of instruction diagnosis improved students' responsibility regarding their classes, and students had positive opinions regarding the usefulness of online instruction diagnosis item pool in instruction evaluation. Also, after instruction diagnosis, analyzing the results through consultations with education development specialists, and then establishing and carrying out instruction reforms were shown to be more effective. In order to utilize the instruction diagnostic system more effectively, from planning the execution of instruction diagnosis to analyzing the results, consulting, and deciding how those results could be utilized to instruction, a systematic strategy is needed. In addition, professors and students need to develop a more active sense of ownership in order to elevate the level of their instruction.