• Journal of Digital Convergence
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• v.17 no.6
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• pp.219-227
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• 2019

This study aims to examine Blockchain research trend using bibliometrics-based network analysis. The data were collected from WoS, Scopus, Korea Citation Index and National science & Technology Information Service, from 2009 to 2018. As results, the number of publications has started increasing rapidly from 2017 and it showed the initial stage of formation of coauthor network. Words often used in the title of the publications were related to application development, controversy and technology development. In addition, the majority of domestic papers are in the subject of social science, while international papers tend to focus on engineering issues. The results of the temporal analysis show that Korean researchers' block chain 3.0 started in 2017 and are rapidly increasing in 2018. The number of citations was associated with publication year in a statistically signifiant way. By examining these research trends, we hope that this paper can be a useful basis for the development of blockchain. Future research is expected to reveal more clearly the knowledge structure and characteristics of blockchain around the world.

• Journal of the Korean Society of Earth Science Education
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• v.13 no.3
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• pp.226-237
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• 2020

The purpose of the research is to analyze research trends related to climate change education by network analysis based on keywords extracted from the research title. For this purpose, 62 papers were selected from Korean Citation Index(KCI) journals published from 2011 to 2020 using such keywords as "climate change" and "climate change education" in the Research Information Sharing Service. The analysis procedure consisted of selection of analysis papers, keyword extraction and purification, and keyword network analysis and visualization. Textom, Ucinet 6.0, and NetDraw were used to analyze the frequency, degree centrality, and betweenness centrality. The results of the research showed that, first, Early 'Energy and Climate Change Education' had the highest frequency of papers examining climate change education. Second, the keywords/phrases that appeared most frequently in research on climate change education were "program" "energy," "analysis," "elementary school," "elementary school," "elementary school students," "development," and "impact." Third, the analysis of the centrality of betweenness centrality showed that the index of 'program', 'primary students' and 'primary schools' were the highest, and the largest group was 'development and effect of teaching and learning programs'. Based on these results, it was concluded that future research on climate change education needs to be examined in further detail and expanded into more specific areas.

• The Mathematical Education
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• v.62 no.3
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• pp.435-455
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• 2023

This study primarily focused on the development of an Explainable Artificial Intelligence (XAI) model to discern and analyze papers with significant impact in the field of mathematics education. To achieve this, meta-information from 29 domestic and international mathematics education journals was utilized to construct a comprehensive academic research network in mathematics education. This academic network was built by integrating five sub-networks: 'paper and its citation network', 'paper and author network', 'paper and journal network', 'co-authorship network', and 'author and affiliation network'. The Random Forest machine learning model was employed to evaluate the impact of individual papers within the mathematics education research network. The SHAP, an XAI model, was used to analyze the reasons behind the AI's assessment of impactful papers. Key features identified for determining impactful papers in the field of mathematics education through the XAI included 'paper network PageRank', 'changes in citations per paper', 'total citations', 'changes in the author's h-index', and 'citations per paper of the journal'. It became evident that papers, authors, and journals play significant roles when evaluating individual papers. When analyzing and comparing domestic and international mathematics education research, variations in these discernment patterns were observed. Notably, the significance of 'co-authorship network PageRank' was emphasized in domestic mathematics education research. The XAI model proposed in this study serves as a tool for determining the impact of papers using AI, providing researchers with strategic direction when writing papers. For instance, expanding the paper network, presenting at academic conferences, and activating the author network through co-authorship were identified as major elements enhancing the impact of a paper. Based on these findings, researchers can have a clear understanding of how their work is perceived and evaluated in academia and identify the key factors influencing these evaluations. This study offers a novel approach to evaluating the impact of mathematics education papers using an explainable AI model, traditionally a process that consumed significant time and resources. This approach not only presents a new paradigm that can be applied to evaluations in various academic fields beyond mathematics education but also is expected to substantially enhance the efficiency and effectiveness of research activities.

• Journal of the Korean Society for information Management
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• v.31 no.2
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• pp.57-77
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• 2014

As co-authorship has been prevalent within science communities, counting the credit of co-authors appropriately is an important consideration, particularly in the context of identifying the knowledge structure of fields with author-based analysis. The purpose of this study is to compare the characteristics of co-author credit counting methods by utilizing correlations, multidimensional scaling, and pathfinder networks. To achieve this purpose, this study analyzed a dataset of 2,014 journal articles and 3,892 cited authors from the Journal of the Architectural Institute of Korea: Planning & Design from 2003 to 2008 in the field of Architecture in Korea. In this study, six different methods of crediting co-authors are selected for comparative analyses. These methods are first-author counting (m1), straight full counting (m2), and fractional counting (m3), proportional counting with a total score of 1 (m4), proportional counting with a total score between 1 and 2 (m5), and first-author-weighted fractional counting (m6). As shown in the data analysis, m1 and m2 are found as extreme opposites, since m1 counts only first authors and m2 assigns all co-authors equally with a credit score of 1. With correlation and multidimensional scaling analyses, among five counting methods (from m2 to m6), a group of counting methods including m3, m4, and m5 are found to be relatively similar. When the knowledge structure is visualized with pathfinder network, the knowledge structure networks from different counting methods are differently presented due to the connections of individual links. In addition, the internal validity shows that first-author-weighted fractional counting (m6) might be considered a better method to author clustering. Findings demonstrate that different co-author counting methods influence the network results of knowledge structure and a better counting method is revealed for author clustering.

• Journal of Korean Home Economics Education Association
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• v.32 no.3
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• pp.179-197
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• 2020

This study analyzed the trends in home economics education research using the language network analysis method, focusing on papers published in the KCI list for 20 years from 2000 to 2019. A total of 501 home economics education papers analyzed through word cloud, centrality analysis, and topic modeling using NetMiner 4.4, and the results are as follows. First, the number of papers in home economics education published in the KCI listing increased gradually to 186 in the 2000s and 315 in the 2010s. The academic journals in which home economics education papers were published have been diversified to 16 in the 2000s and 22 in the 2010s. 60% of all papers were published in the 'Journal of Korean Home Economics Education Association', and since 2018, the number of papers published in the 'Journal of Learner-Centered Curriculum and Instruction' has increased dramatically. Second, in the 2000s and 2010s, home economics education studies published in KCI were categorized into home economics education content analysis, home economics educational program development & application, curriculum analysis, perception survey & direction exploration. In the 2000s, 'Home Economics Teacher' appeared as the main keyword, and a lot of perception survey & direction exploration were conducted. Relatively, the influence of 'development' increased in the 2010s, and many studies were conducted to analyze home economics education contents and develop and apply home economics programs. This study has significance in that it analyzed the research trend of HEE by expanding the analysis target and analysis period of the existing studies.

• Journal of the Korean Society for Library and Information Science
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• v.55 no.4
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• pp.141-157
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• 2021

In this study, we aimed to understand research trends of domestic artificial intelligence research. To achieve the goal, we applied network analysis and dynamic topic modeling to domestic research papers on artificial intelligence. Among the papers that have been indexed in KCI (Korean Journal of Citation Index) by 2020, metadata and abstracts of 2,552 papers where the titles or indexed keywords include 'artificial intelligence' both in Korean and English were collected. Keyword, affiliation, subject field, and abstract were extracted and preprocessed for further analyses. We identified main keywords in the field by analyzing keyword co-occurrence networks as well as the degree and characteristics of research collaboration between domestic and foreign institutions and between industry and university by analyzing institutional collaboration networks. Dynamic topic modeling was performed on 1845 abstracts written in Korean, and 13 topics were obtained from the labeling process. This study broadens the understanding of domestic artificial intelligence research by identifying research trends through dynamic topic modeling from abstracts as well as the degree and characteristics of research collaboration through institutional collaboration networks from author affiliation information. In addition, the results of this study can be used by governmental institutions for making policies in accordance with artificial intelligence era.

• Journal of Korean Library and Information Science Society
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• v.51 no.4
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• pp.59-81
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• 2020

The purpose of this study is to compare and analyze the trend of reading research in Korea's LIS Fields in the past 20 years, divided into the 2000s and 2010s, by establishing a keyword network. To achieve this purpose, keywords were extracted from 489 related articles in the four major journals in the LIS field sourced from the Korean Journal Citation Index (KCI) and then analyzed using NetMiner4. The results of the study were as follows: First, in the case of the 2000s, 'Public Library', 'Bibliotherapy', 'Reading Education', and 'School Library' showed high values of Frequency Analysis, Degree Centrality, and Betweenness Centrality. In the 2010s, 'Reading Education', 'School Library', 'Children', 'Adolescents', and 'Public Library' showed high values of the aforementioned measures. Second, in the 2000s, the establishment of library infrastructure for reading and reading education, the improvement of policies and systems, and reading research through the reading movement were actively conducted. In the 2010s, based on the work and research done in the 2000s, customized user reading studies and various detailed reading research were conducted. Third, to meet the demands of the times for the restoration of humanity with creativity and imagination in the Fourth Industrial Revolution, reading research and professional in-depth research should be conducted in various environments beyond public and school libraries and interdisciplinary research and active joint research between the field and academia are needed.

• Journal of the Korean Society of Marine Environment & Safety
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• v.28 no.4
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• pp.549-566
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• 2022

Recently, artificial intelligence (AI) technology has progressed rapidly, and industries using this technology are significantly increasing. Further, analysis research using text mining, which is an application of artificial intelligence, is being actively developed in the field of social science research. About 125 laws, including joint laws, have been enacted under the Ministry of Oceans and Fisheries in various sectors including marine environment, fisheries, ships, fishing villages, ports, etc. Research on the laws under the Ministry of Oceans and Fisheries has been progressively conducted, and is steadily increasing quantitatively. In this study, the domestic research trends were analyzed through text mining, targeting the research papers related to laws of the Ministry of Oceans and Fisheries. As part of this research method, first, topic modeling which is a type of text mining was performed to identify potential topics. Second, co-occurrence network analysis was performed, focusing on the keywords in the research papers dealing with specific laws to derive the key themes covered. Finally, author network analysis was performed to explore social networks among authors. The results showed that key topics have been changed by period, and subjects were explored by targeting Ship Safety Law, Marine Environment Management Law, Fisheries Law, etc. Furthermore, in this study, core researchers were selected based on author network analysis, and the tendency for joint research performed by authors was identified. Through this study, changes in the topics for research related to the laws of the Ministry of Oceans and Fisheries were identified up to date, and it is expected that future research topics will be further diversified, and there will be growth of quantitative and qualitative research in the field of oceans and fisheries.

• Journal of Preventive Medicine and Public Health
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• v.41 no.1
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• pp.1-9
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• 2008

Objectives : This study evaluated knowledge structure and its effect factor by analysis of co-author and keyword networks in Korea's preventive medicine sector. Methods : The data was extracted from 873 papers listed in the Journal of Preventive Medicine and Public Health, and was transformed into a co-author and keyword matrix where the existence of a 'link' was judged by impact factors calculated by the weight value of the role and rate of author participation. Research achievement was dependent upon the author's status and networking index, as analyzed by neighborhood degree, multidimensional scaling, correspondence analysis, and multiple regression. Results : Co-author networks developed as randomness network in the center of a few high-productivity researchers. In particular, closeness centrality was more developed than degree centrality. Also, power law distribution was discovered in impact factor and research productivity by college affiliation. In multiple regression, the effect of the author's role was significant in both the impact factor calculated by the participatory rate and the number of listed articles. However, the number of listed articles varied by sex. Conclusions : This study shows that the small world phenomenon exists in co-author and keyword networks in a journal, as in citation networks. However, the differentiation of knowledge structure in the field of preventive medicine was relatively restricted by specialization.

• Journal of Digital Convergence
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• v.13 no.9
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• pp.129-136
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• 2015

We investigate the underlying structure of big data research domain, which is diversified and complicated using bottom-up approach. For that purpose, we derive a set of articles by searching "big data" through the Korea Citation Index System provided by National Research Foundation of Korea. With some preprocessing on the author-provided keywords, we analyze bibliometric data such as author-provided keywords, publication year, author, and journal characteristics. From the analysis, we both identify major sub-domains of big data research area and discover the hidden issues which made big data complex. Major keywords identified include SOCIAL NETWORK ANALYSIS, HADOOP, MAPREDUCE, PERSONAL INFORMATION POLICY/PROTECTION/PRIVATE INFORMATION, CLOUD COMPUTING, VISUALIZATION, and DATA MINING. We finally suggest missing research themes to make big data a sustainable management innovation and convergence medium.