• Journal of Korean Academy of Nursing
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• v.54 no.2
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• pp.266-278
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• 2024

Purpose: This study aimed to investigate healthcare consumers' interest in patient safety on social media using structural topic modeling (STM) and to identify changes in interest over time. Methods: Analyzing 105,727 posts from Naver news comments, blogs, internet cafés, and Twitter between 2010 and 2022, this study deployed a Python script for data collection and preprocessing. STM analysis was conducted using R, with the documents' publication years serving as metadata to trace the evolution of discussions on patient safety. Results: The analysis identified a total of 13 distinct topics, organized into three primary communities: (1) "Demand for systemic improvement of medical accidents," underscoring the need for legal and regulatory reform to enhance accountability; (2) "Efforts of the government and organizations for safety management," highlighting proactive risk mitigation strategies; and (3) "Medical accidents exposed in the media," reflecting widespread concerns over medical negligence and its repercussions. These findings indicate pervasive concerns regarding medical accountability and transparency among healthcare consumers. Conclusion: The findings emphasize the importance of transparent healthcare policies and practices that openly address patient safety incidents. There is clear advocacy for policy reforms aimed at increasing the accountability and transparency of healthcare providers. Moreover, this study highlights the significance of educational and engagement initiatives involving healthcare consumers in fostering a culture of patient safety. Integrating consumer perspectives into patient safety strategies is crucial for developing a robust safety culture in healthcare.

• Journal of the HCI Society of Korea
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• v.10 no.1
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• pp.5-17
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• 2015

Nowadays various public data have been serviced to the public. Through the opening of public data, the transparency and effectiveness of public policy developed by governments are increased and users can lead to the growth of industry related to public data. Since end-users of using public data are citizens, it is very important for everyone to figure out the meaning of public data using proper visualization techniques. In this work, to indicate the significance of widespread public data, we consider UN voting record as public data in which many people may be interested. In general, it has high utilization value by diplomatic and educational purposes, and is available in public. If we use proper data mining and visualization algorithms, we can get an insight regarding the voting patterns of UN members. To visualize, it is necessary to measure the voting similarity values among UN members and then a social graph is created by the similarity values. Next, using a graph layout algorithm, the social graph is rendered on the screen. If we use the existing method for visualizing the social graph, it is hard to understand the meaning of the social graph because the graph is usually dense. To improve the weak point of the existing social graph visualization, we propose Friend-Matching, Friend-Rival Matching, and Bubble Heap algorithms in this paper. We also validate that our proposed algorithms can improve the quality of visualizing social graphs displayed by the existing method. Finally, our prototype system has been released in http://datalab.kunsan.ac.kr/politiz/un/. Please, see if it is useful in the aspect of public data utilization.

• Journal of the Korea Society for Simulation
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• v.25 no.3
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• pp.107-116
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• 2016

The recent diversification in terms of the scope and techniques used for simulations has highlighted the importance of analyzing state of the art trends and applying these for educational and study purposes. While qualitative methods such as literature research or experts' assessments have previously been used, such methods are in fact likely to reflect the subjective viewpoint of experts, and to involve too much time and money for the results obtained. For the purpose of an objective analysis, a quantitative analysis that included the examination of topics found in domestic academic journal articles was conducted in the present study. In this regard, simulation was found to be most actively used domestically in the electrical and electronic fields. In addition, simulation was also found to be employed for the purpose of education and entertainment in the social sciences. The results of this study are expected to help to facilitate the prediction of the direction of the development of not only the Korea Society for Simulation, but also domestic simulation studies. This study also raises the possibility of applying text mining to trend analysis, and proves that it can be a useful method for deriving future key topics and helping experts' decisions regarding quantitative data.

• Journal of Digital Convergence
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• v.20 no.5
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• pp.627-635
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• 2022

As the non-face-to-face situation continues for a long time due to COVID-19, the underlying technologies of the 4th industrial revolution such as IOT, AR, VR, and big data are affecting the metaverse platform overall. Such changes in the external environment such as society and culture can affect the development of academics, and it is very important to systematically organize existing achievements in preparation for changes. The Korea Educational Research Information Service (RISS) collected data including the 'metaverse platform' in the keyword and used the text mining technique, one of the big data analysis. The collected data were analyzed for word cloud frequency, connection strength between keywords, and semantic network analysis to examine the trends of metaverse platform research. As a result of the study, keywords appeared in the order of 'use', 'digital', 'technology', and 'education' in word cloud analysis. As a result of analyzing the connection strength (N-gram) between keywords, 'Edue→Tech' showed the highest connection strength and a total of three clusters of word chain clusters were derived. Detailed research areas were classified into five areas, including 'digital technology'. Considering the analysis results comprehensively, It seems necessary to discover and discuss more active research topics from the long-term perspective of developing a metaverse platform.

• Korea journal of population studies
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• v.31 no.2
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• pp.1-20
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• 2008

This research examines individual attitudes toward immigration and the determinants of these attitudes in Korea, Taiwan, and Japan. Using 2003 National Identity module from ISSP data, descriptive statistics showed that Taiwan seems to be the most opposed to immigration, Japan in the middle, and Korea the least opposed. Despite being the least opposed to immigration, Koreans agreed most strongly with having a homogeneous sense of ethnic identity. In addition, Korean respondents saw immigrants are being good for the national economy, but not for cultural diversity. Binary logistic regression analysis showed that age is negatively associated with the individual's attitude toward immigrants in Korea, Taiwan, and Japan. Effects of educational attainment is only significant in Taiwan. Manual worker and others, and those who work in farming, mining, and fishing tend to have negative attitudes toward increases in number of immigrants. Also, individual's values toward immigrants/immigration are related to the individual's attitude toward increases in number of immigrants in Korea, Japan, and Taiwan.

• Journal of the Korean Society for Library and Information Science
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• v.55 no.4
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• pp.267-291
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• 2021

Due to the global pandemic caused by COVID-19 in 2020, there have been major changes in the education sites. Universities have fully introduced remote learning, which was considered as an auxiliary education, and non-face-to-face classes have become commonplace, and professors and students are making great efforts to adapt to the new educational environment. In order to improve the quality of non-face-to-face lectures amid these changes, it is necessary to study the factors affecting lecture satisfaction. Therefore, This paper presents a new methodology using big data to identify the factors affecting university lecture satisfaction changed before and after COVID-19. We use Topic Modeling method to analyze lecture reviews before and after COVID-19, and identify factors affecting lecture satisfaction. Through this, we suggest the direction for university education to move forward. In addition, we can identify the factors of satisfaction and dissatisfaction of lectures from multiangle by establishing a topic classification model with an F1-score of 0.84 based on KoBERT, a deep learning language model, and further contribute to continuous qualitative improvement of lecture satisfaction.

• Journal of The Korean Association of Information Education
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• v.26 no.5
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• pp.397-406
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• 2022

In this study, We confirmed the three major policy directions presented in "Educational Policy Direction and Core Tasks in the Age of Artificial Intelligence" announced by the government in 2020, and analyzed how the direction and key tasks are reflected in the policy from keywords selected from government policy data related to artificial intelligence education published between '20 and '22. It was extracted and analyzed how the direction and key tasks are reflected in the policy. As a result of text mining and the topic analysis, the direction of education set was analyzed and various types of activities for nurturing talents in the field of artificial intelligence were confirmed. Ultimately, the government's policy direction is to apply the '25 revised curriculum in earnest, while advancing and activating the AI education policy and allowing it to settle naturally in the field. It could be predicted that related policies and tasks would appear more and more.

• Journal of Internet Computing and Services
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• v.24 no.6
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• pp.57-64
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• 2023

This study analyzed the perceptions of elementary and secondary school teachers regarding the importance of SW/AI education in fostering students' fundamental knowledge and the necessity of integrating SW/AI into education. A total of 830 elementary and secondary school teachers were selected as study subjects using the judgment sampling method. The analysis of survey data revealed that elementary and secondary teachers exhibited a strong awareness of the importance and necessity of SW/AI education, irrespective of school characteristics, region, educational experience, or prior involvement in SW and AI education. Nevertheless, the primary reasons for not implementing SW/AI education were identified as excessive workload and a lack of pedagogical expertise. An analysis of opinions on the essential conditions for implementing SW/AI education revealed that workload reduction, budget support, teacher training to enhance teacher competency, content distribution, expansion of subject-linked courses, and dedicated instructional time allocation were the major influencing factors. These findings indicate a significant demand for comprehensive instructional support and teacher capacity-building programs.