• Journal of The Korean Association For Science Education
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• v.26 no.6
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• pp.691-702
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• 2006

The purposes of the study are to portray Korean beginning secondary science teachers' ways of arranging science content, sense-making strategy, and factors contributing to the tensions between teachers' intentions and actual practice. Six beginning secondary science teachers participated in this study. Science classes taught by the participating teachers were observed and videotaped. Semi-structured interviews were conducted for science teachers participated in this study after science classes were observed. Instructional materials were also collected for each science class. Video- and audio-taped data were transcribed and analyzed using conceptual framework developed by the Michigan State University. The findings of this study produce the following conclusions: (1) beginning teachers' science classes are arranged in ways compatible to traditional school science, (2) frequently used sense-making strategies are procedural display and narrative reasoning, (3) tensions between beginning teachers' intentions and practice arise from two factors such as assessment and differences in educational views with peer teachers, and (4) learning experiences, lack of perceptions and preparations on reform science teaching, and the absence of systematic program for professional development programs for beginning science teachers are major obstacles to reform science teaching for beginning teachers.

• Journal of The Korean Association For Science Education
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• v.44 no.3
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• pp.231-248
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• 2024

This study developed a chatbot for first-year high school students, employing open-source software and the Korean Sentence-BERT model for AI-powered document classification. The chatbot utilizes the Sentence-BERT model to find the six most similar Q&A pairs to a student's query and presents them in a carousel format. The initial dataset, built from online resources, was refined and expanded based on student feedback and usability throughout over the operational period. By the end of the 2023 academic year, the chatbot integrated a total of 30,819 datasets and recorded 3,457 student interactions. Analysis revealed students' inclination to use the chatbot when prompted by teachers during classes and primarily during self-study sessions after school, with an average of 2.1 to 2.2 inquiries per session, mostly via mobile phones. Text mining identified student input terms encompassing not only science-related queries but also aspects of school life such as assessment scope. Topic modeling using BERTopic, based on Sentence-BERT, categorized 88% of student questions into 35 topics, shedding light on common student interests. A year-end survey confirmed the efficacy of the carousel format and the chatbot's role in addressing curiosities beyond integrated science learning objectives. This study underscores the importance of developing chatbots tailored for student use in public education and highlights their educational potential through long-term usage analysis.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.17 no.1
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• pp.1-9
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• 2024

This paper analyzes the trends in generative AI technology based on patent application documents. To achieve this, we selected 5,433 generative AI-related patents filed in South Korea, the United States, and Europe from 2003 to 2023, and analyzed the data by country, technology category, year, and applicant, presenting it visually to find insights and understand the flow of technology. The analysis shows that patents in the image category account for 36.9%, the largest share, with a continuous increase in filings, while filings in the text/document and music/speech categories have either decreased or remained stable since 2019. Although the company with the highest number of filings is a South Korean company, four out of the top five filers are U.S. companies, and all companies have filed the majority of their patents in the U.S., indicating that generative AI is growing and competing centered around the U.S. market. The findings of this paper are expected to be useful for future research and development in generative AI, as well as for formulating strategies for acquiring intellectual property.

• Journal of Internet Computing and Services
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• v.25 no.1
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• pp.91-98
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• 2024

With the advancement of ICT (Information and Communication Technology), searching for judgments through the internet has become increasingly convenient. However, predicting sentencing based on judgments remains a challenging task for individuals. This is because sentencing involves a complex process of applying aggravating and mitigating factors within the framework of legal provisions, and it often depends on the subjective judgment of the judge. Therefore, this research aimed to develop a model for predicting sentencing using artificial intelligence by focusing on structuring the data from judgments, making it suitable for AI applications. Through theoretical and statistical analysis of previous studies, we identified variables with high explanatory power for predicting sentencing. Additionally, by analyzing 50 legal judgments related to serious crimes that are publicly available, we presented a framework for extracting essential information from judgments. This framework encompasses basic case information, sentencing details, reasons for sentencing, the reasons for the determination of the sentence, as well as information about offenders, victims, and accomplices evident within the specific content of the judgments. This research is expected to contribute to the development of artificial intelligence technologies in the field of law in the future.

• Korean Journal of Construction Engineering and Management
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• v.25 no.1
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• pp.42-49
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• 2024

In recent years, the construction industry has diligently focused on improving the quality and safety of buildings through smart technologies. However, there is a growing trend of leakage defects, especially in educational facilities, due to aging. The objective of this study is to analyze the causes of these defects in educational environments using the Fault Tree Analysis (FTA) technique and propose preventive measures based on the findings. The FTA technique is explained through a review of domestic literature, and data from the Educational Support Center from 2019 to 2021 are examined to identify major defects. The construction of the Fault Tree (FT) for leakage defects resulted in the identification of 12 basic events. Subsequently, a comprehensive understanding of the causes of leakage is achieved through FTA analysis, leading to the identification of the primary causes of defects. Leakage defects accounted for 46.8% of all reported issues in educational facilities, with roof (ceiling) leaks being the most common problem. FTA analysis revealed that poor substrate treatment was the main cause of roof (ceiling) leaks, which could be attributed to cracks in the waterproof layer, joint cracks, and microvoids in the waterproof layer. The primary achievement of this research is to provide essential data for preventing leakage defects in educational facilities and developing preventive measures through the FTA technique. These results are expected to significantly enhance the management of educational facilities and the prevention of leakage issues.

• Journal of Internet Computing and Services
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• v.25 no.2
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• pp.79-92
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• 2024

ChatGPT, as a representative chatbot leveraging generative artificial intelligence technology, is used valuable not only in scientific and technological domains but also across diverse sectors such as society, economy, industry, and culture. This study conducts an explorative analysis of user sentiments and needs for ChatGPT by examining global social media discourse on Reddit. We collected 10,796 comments on Reddit from December 2022 to August 2023 and then employed keyword analysis, sentiment analysis, and need-mining-based topic modeling to derive insights. The analysis reveals several key findings. The most frequently mentioned term in ChatGPT-related comments is "time," indicative of users' emphasis on prompt responses, time efficiency, and enhanced productivity. Users express sentiments of trust and anticipation in ChatGPT, yet simultaneously articulate concerns and frustrations regarding its societal impact, including fears and anger. In addition, the topic modeling analysis identifies 14 topics, shedding light on potential user needs. Notably, users exhibit a keen interest in the educational applications of ChatGPT and its societal implications. Moreover, our investigation uncovers various user-driven topics related to ChatGPT, encompassing language models, jobs, information retrieval, healthcare applications, services, gaming, regulations, energy, and ethical concerns. In conclusion, this analysis provides insights into user perspectives, emphasizing the significance of understanding and addressing user needs. The identified application directions offer valuable guidance for enhancing existing products and services or planning the development of new service platforms.

• Journal of Food Hygiene and Safety
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• v.39 no.2
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• pp.152-162
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• 2024

In this study, we investigated the levels of the natural preservatives, benzoic acid, sorbic acid, and propionic acid, in raw unprocessed vegetables. Quantitative analysis of benzoic acid and sorbic acid was performed using high-performance liquid chromatography with a diode array detector (HPLC-DAD) and confirmed using liquid chromatography-tandem mass spectrometry (LC-MS/MS). Propionic acid was analyzed using a gas chromatography-flame ionization detector (GC-FID) and confirmed using gas chromatography-mass spectrometry (GC-MS). From a total of 497 samples, benzoic acid, sorbic acid, and propionic acid were found in 50 (10%), 8 (0.2%), and 61 samples (12.3%), respectively. The highest quantity of benzoic acid, sorbic acid, and propionic acid was found in peony root (1,057 mg/kg), nut-bearing torreya seeds (27.3 mg/kg), and myrrha (175 mg/kg), respectively. The background concentration range of naturally occurring preservatives in raw vegetables determined in this study could be used as standard inspection criteria to address consumer complaints and trade disputes.

• Journal of Korean Academy of Rural Health Nursing
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• v.19 no.1
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• pp.25-34
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• 2024

Purpose: This study investigates the relationship between job stress and burnout among primary healthcare practitioners during COVID-19 pandemic through mixed methods study. Methods: Data were collected from October to November 2022 using Qualtrix, a web-based survey platform. 1,082 primary health care practitioners participated in the survey. Quantitative data were analyzed using correlation analysis using IBM SPSS/WIN 27.0. Qualitative data were analyzed using content analysis through open-ended questions. Results: Job stress and burnout among primary healthcare practitioners during COVID-19 were positively correlated. Four categories and seven subcategories were identified. Conclusion: Based on these findings, it is necessary to develop a support system for primary healthcare practitioners according to the type of residential area and the number of peopleto reduce job stress and burnout.

• Food Science and Preservation
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• v.30 no.4
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• pp.716-728
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• 2023

The anti-adipogenic activity of Glycyrrhiza uralensis was investigated by examining the effects of its ethanol extract (GUE) on a mouse model with a high-fat diet (HFD) and 3T3-L1 preadipocytes during adipocyte differentiation. GUE administration for eight weeks significantly reduced weight gain in mice fed an HFD. GUE effectively inhibited 3T3-L1 preadipocyte differentiation and lipid droplet accumulation. This inhibitory effect is associated with the downregulation of key adipogenic regulators, including PPARγ and C/EBPα, and the modulation of adipose metabolism regulators, such as Fasn and Fabp4. LC-Q-TOF-MS analysis identified twelve phenolic and flavonoid compounds, including liquiritigenin and licorice saponin, in the GUE. These findings demonstrate that the anti-obesity effect of the GUE is attributed to the biological activity of its phenolic and flavonoid compounds. Therefore, the GUE has potential anti-obesity activity. Moreover, further studies on the isolation of bioactive components from the GUE and the investigation of the underlying molecular mechanisms of the GUE are required to establish its efficacy in metabolic disorders, including obesity.

• Journal of Practical Agriculture & Fisheries Research
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• v.25 no.4
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• pp.84-89
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• 2024

Light is an important component among which plays a crucial role in determining the production and quality of fruit trees. Since the disturbance of light directly leads to reduced photosynthetic efficiency, their damage can be increased especially in fruit trees such as Japanese apricots with a short growing time. In this study, we investigated how the effects of shading condition can affect the production and quality of Japanese apricots according to increased damages by light disturbance in the main orchard complex. The average photosynthetically active radiation (PAR) level in Japanese apricots was rapidly dropped as the shading time was increased compared to the control (304 μmol/m²/s) and the PAR level decreased to 142 μmol/m²/s after shaded for eight hours. The maximum photosynthetic efficiency, with a PAR value of 900 to 1,000 μmol/m²/s, corresponds to the time period without shading and the time period with 2 hours of shading, and these times range from 11 a.m. to 3 p.m. And the time period for shading for 4 hours was from 1:00 p.m. to 2:00 p.m., and under conditions of shading for 6 and 8 hours, the effect was a low amount of light. There was no difference in the weight of Japanese apricots during 2 hours shading time, however, it was significantly reduced as shading time were increased. The difference of the acid content and L/D ratio was not significant on shading time, but the SSC was decreased as times going on. In conclusion, our results indicate that the shading for more than 2 hours make negative effects to decrease the weight and SSC and the yield and affects directly to drop in fruit quality.