• The Journal of the Korea Contents Association
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• v.22 no.2
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• pp.390-399
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• 2022

Purpose: This study was conducted to identify the factors affecting nursing students' resilience. Methods: The subjects were 192 nursing students from a college in G city. Data were collected from september 23 to 26, 2019 and analyzed using SPSS 22.0 and descriptive statistics, t-test, ANOVA, Sheffé test, Pearson's correlation coefficients, and multiple regression. Results: Resilience showed a statistically significant difference according to gender, grade, personal relation, motive for application, major satisfaction, grade point in general characteristics. Academic engagement and resilience showed apparent positive correlation (r=.37, p<.001), stress and resilience showed weak negative correlation (r=-.23, p=.001). In multiple regression analysis, the most affecting factor was the academic engagement (𝛽=.24), poor of subjective health status (𝛽=-.21), female (𝛽=-.19), junior of grade (𝛽=.13). These variables explained 33.0% of the total variance in resilience. Conclusion: To strengthen resilience in nursing students, learning atmosphere creation through intrinsic motivation in the regular class. Also, a variable academic engagement program should be provided to be able to positive thinking about academic study and achievement.

• Journal of The Korean Association For Science Education
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• v.42 no.4
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• pp.383-396
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• 2022

In this study, pre-service chemistry teachers' questions in their teaching practices were analyzed considering the context of discourse. Five pre-service teachers participated in the study. Their questions were analyzed by considering various data including class videos, interviews, and teaching-learning materials. Their questions were classified into relevant question, affective question, dead-end question, rhetorical question, and structuring question. Each question was also classified into appropriate question and convenient question by the aspect of proper responses of students. The analyses of the results indicate the differences in the frequencies of several types of questions depending on the content of the lessons. After using convenient questions, pre-service teachers proceeded to prepared classes as they rather than prompted students' responses. The affective questions were rarely used. The dead-end questions were found to be used for promoting interaction with students. The rhetorical questions were used for various purposes such as arousing students' attention or promoting their thinking. Practical implications were discussed based on the results.

• Journal of The Korean Association For Science Education
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• v.42 no.4
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• pp.429-438
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• 2022

In this study, we propose a way to improve science teacher expertise through infographics creation teacher training program by analyzing the infographics types focusing on the Earth Science subject of the 2015 revised curriculum, and inspecting the teachers' utilization of graphic tools. The data visualization characteristics of Earth Science textbooks were analyzed, the execution results of the infographics creation teacher training program were presented, and a survey on science teachers' change in perception and competency of infographics. As a result of the Earth Science textbook analysis, diagram-type, map-type, and comparative analysis-type infographics were frequently used, and were mainly presented as text-assisted-type infographics. The infographics creation teacher training program was conducted five times for 112 science teachers to create the complete, text-assisted, incomplete, and gradient-type infographics. Incomplete infographics for development of evaluation questions were most needed. Although many science teachers recognize the importance of infographics, they lacked the competency to create high-quality infographics because there were no training opportunities for infographics creation. After completing the training, 74.1% of teachers felt that the quality of developments of supplementary textbooks and evaluation questions had improved, and answered that it was helpful in re-educating knowledge and improving teaching-learning methods. Based on the research results, ways to improve science teacher expertise through infographics creation teacher training program were suggested.

• Communications of Mathematical Education
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• v.36 no.3
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• pp.335-353
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• 2022

The purpose of this study is to identify the characteristics and types of errors in the conceptual image of Korean language learners according to the types of terms in mathematics that are the basis for solving mathematical word problems, and to prepare basic data for effective teaching and learning methods in solving the word problems of Korean language learners. To do this, a case study was conducted targeting four Korean language learners to analyze the specific conceptual images of terms registered in curriculum and terms that were not registered in curriculum but used in textbooks. As a result of this study, first, it is necessary to guide Korean language learners by using sufficient visualization material so that they can form appropriate conceptual definitions for terms in school mathematics. Second, it is necessary to understand the specific relationship between the language used in the home of Korean language learners and the conceptual image of terms in school mathematics. Third, it is necessary to pay attention to the passive term, which has difficulty in understanding the meaning rather than the active term. Fourth, even for Korean language learners who do not have difficulties in daily communication, it is necessary to instruct them on everyday language that are not registered in the curriculum but used in math textbooks. Fifth, terms in school mathematics should be taught in consideration of the types of errors that reflect the linguistic characteristics of Korean language learners shown in the explanation of terms. This recognition is expected to be helpful in teaching word problem solving for Korean language learners with different linguistic backgrounds.

• Journal of Korean Elementary Science Education
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• v.41 no.1
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• pp.44-64
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• 2022

This study explored the elementary school teachers' experiences while teaching students with low achievement in science based on the grounded theory. In-depth interviews and analysis were conducted on 13 teachers with experiences in teaching students with low achievement in science within the last three years and more than five years of field experience until the theoretical saturation of data on the teaching experiences for students with low achievement in science. The analysis results were as follows. First, the teaching experiences of elementary school teachers for underachievers in science were classified into 119 concepts, 41 subcategories, and 17 categories. Based on the paradigm model, the categories were structured and presented as causal conditions, contextual conditions, intervening conditions, action/interaction strategies and consequences based on the central phenomenon of 'difficulty in teaching students with low achievement in science'. Second, the core category of elementary school teachers' teaching underachievers in science was assumed to be 'overcoming difficulties and teaching underachievers in science'. And according to the properties and dimensions of the core category, teachers who teaching students with low achievement in science were divided into four types: 'compromising-', 'overcoming-', 'accepting-', and 'conflicting-reality type'. Third, a conditional matrix was presented to summarize and integrate the results of this study by classifying the teaching experience of elementary school teachers for underachievers in science into educational providers and educational demanders. On the basis of these findings, educational implications for teaching students with low achievement in science were discussed.

• Knowledge Management Research
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• v.23 no.1
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• pp.89-109
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• 2022

Due to COVID-19 and soaring participation of individual investors, large-scale transactions exceeding system capacity limits have been reported frequently in the capital market. The capital market IT systems, which the impact of system failure is very critical, have encountered unexpectedly tremendous transactions in 2020, resulting in a sharp increase in system failures. Despite the fact that many companies maintained large-scale system capacity planning policies, recent transaction influx suggests that a new approach to capacity planning is required. Therefore, this study developed capital market IT system capacity planning models using machine learning techniques and analyzed those performances. In addition, the performance of the best proposed model was improved by using sentiment index that can promptly reflect the behavior of investors. The model uses empirical data including the COVID-19 period, and has high performance and stability that can be used in practice. In practical significance, this study maximizes the cost-efficiency of a company, but also presents optimal parameters in consideration of the practical constraints involved in changing the system. Additionally, by proving that the sentiment index can be used as a major variable in system capacity planning, it shows that the sentiment index can be actively used for various other forecasting demands.

• Journal of the Korean Society of Earth Science Education
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• v.15 no.2
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• pp.310-321
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• 2022

This study aimed to find out how creative education programs using AR/VR affect student's creative problem-solving skills and class satisfaction. For this purpose, a total of 179 students in 7 classes of the first grade of J High school located in the chungbuk region were the subjects of this study. The data were analyzed by performing two-dependent samples (t-test) based on the difference between the pre- and post-scores of creative problem-solving ability test, and the value of class satisfaction was analyzed and interpret using descriptive statistics and interview. The results of this study are as follows. First, except for 'execution', 'problem discovery and analysis', 'idea generation', 'execution plan', 'conviction and communication', and 'innovation tendency' showed statistically significant results. Second, in terms of class satisfaction of the creative education program, it was an average of 3.75 and it was difficult for learners to derive creative ideas, outputs, and results through groups within a given time in regular class, but generally showed a positive response. Therefore, it was confirmed that the creative education program using AR/VR increased student's learning motivation and interest in the process of generation or expanding ideas to solve problems like educational effect of STEAM.

• The Korean Journal of Food And Nutrition
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• v.36 no.2
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• pp.93-102
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• 2023

Centella asiatica (C. asiatica) has been widely used in food, cosmetics, and pharmaceutical industry as a functional material. In a previous study, we have investigated not only pharmacological effects such as antioxidative and anti-inflammatory effects, but also analyzed various functional ingredients. In this study, triterpenoids were analyzed using HPLC-DAD to determine marker compounds among functional ingredients. When triterpenoids were analyzed, asiaticoside from C. asiatica was determined as an optimal marker compound. Next, specificity, linearity, limited of detection (LOD), limited of quantification (LOQ), precision, accuracy, and range were evaluated using HPLC-DAD to determine asiaticoside contents in C. asiatica juice and extracts. The specificity was elucidated by chromatogram and retention time using an established analytical method. The coefficient of correlation obtained was 0.9996. LOD was 4.99 ㎍/mL and LOQ was 15.12 ㎍/mL. Intra- and inter-day precision of asiaticoside were determined to be 0.48~1.68% and 0.08~1.09%, respectively. Furthermore, the recovery rate of asiaticoside was 98.88% and the analytical range of Field-70E was determined to be 0.625~10 mg/mL. As a results of evaluating ABTS, DPPH, and FRAP antioxidative effect, Field-70E showed potent antioxidant activities. Results of this study could be used as basic data for quality standardization of C. astiatica juice and extracts.

• Journal of Intelligence and Information Systems
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• v.29 no.3
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• pp.125-144
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• 2023

Recently, the use of Large Language Models (LLMs) such as ChatGPT has been increasing in various fields such as interactive commerce and mobile financial services. However, LMMs, which are mainly created by learning existing documents, can also learn various human biases inherent in documents. Nevertheless, there have been few comparative studies on the aspects of bias and discrimination in LLMs. The purpose of this study is to examine the existence and extent of nine types of discrimination (Age, Disability status, Gender identity, Nationality, Physical appearance, Race ethnicity, Religion, Socio-economic status, Sexual orientation) in LLMs and suggest ways to improve them. For this purpose, we utilized BBQ (Bias Benchmark for QA), a tool for identifying discrimination, to compare three large-scale language models including ChatGPT, GPT-3, and Bing Chat. As a result of the evaluation, a large number of discriminatory responses were observed in the mega-language models, and the patterns differed depending on the mega-language model. In particular, problems were exposed in elder discrimination and disability discrimination, which are not traditional AI ethics issues such as sexism, racism, and economic inequality, and a new perspective on AI ethics was found. Based on the results of the comparison, this paper describes how to improve and develop large-scale language models in the future.

• Journal of radiological science and technology
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• v.46 no.4
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• pp.315-323
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• 2023

Post-mortem computed tomography (PMCT) is commonly employed in the field of forensic pathology. PMCT was mainly performed using a whole-body scan with a wide field of view (FOV), which lead to a decrease in spatial resolution due to the increased pixel size. This study aims to evaluate the potential for developing a super-resolution model based on conditional generative adversarial networks (CGAN) to enhance the image quality of CT. 1761 low-resolution images were obtained using a whole-body scan with a wide FOV of the head phantom, and 341 high-resolution images were obtained using the appropriate FOV for the head phantom. Of the 150 paired images in the total dataset, which were divided into training set (96 paired images) and validation set (54 paired images). Data augmentation was perform to improve the effectiveness of training by implementing rotations and flips. To evaluate the performance of the proposed model, we used the Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM) and Deep Image Structure and Texture Similarity (DISTS). Obtained the PSNR, SSIM, and DISTS values of the entire image and the Medial orbital wall, the zygomatic arch, and the temporal bone, where fractures often occur during head trauma. The proposed method demonstrated improvements in values of PSNR by 13.14%, SSIM by 13.10% and DISTS by 45.45% when compared to low-resolution images. The image quality of the three areas where fractures commonly occur during head trauma has also improved compared to low-resolution images.