• 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 The Korean Association For Science Education
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• v.42 no.5
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• pp.543-555
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• 2022

This study aims to identify the trends in 'Aesthetic Science-Education' research through bibliographic analysis and provide future implications for research in this field. To this research, 100 studies were extracted using the search function of the Web of Science provided by Clarivate Analytics. Detailed bibliometrics was analyzed using the Bibliometrix package of the R program. As a result of the analysis, the average number of papers increased from 1993 to 2022, and academic journals in which related papers were published were evenly distributed locally. As a result of keyword analysis, papers with top citations, author collaboration networks, and literature co-citation networks, Aesthetic Science-Education could be classified as inducing aesthetic experience by integrating art in science education, and categories using 'formalist aesthetic' and 'emotional response'. The implications derived from this study are as follows: first, the aesthetic aspects of science should be actively studied to emphasize 'Agency' and 'Active Learning' in future science education. Second, it is necessary to develop a new approach to science education by further utilizing the 'formalist aesthetic' of science in science education. Third, the aesthetic aspect of science can change the perception of the Nature of Science of teachers, pre-service science teachers, and students. Fourth, it is necessary to discover implications for utilizing aesthetic aspects in science education through extensive research on the aesthetic aspects of science for teachers, students, and pre-service teachers. This study is meaningful because it provides an overview of the 'Aesthetic Science-Education' research to realize the above implications.

• 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.47 no.1
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• pp.217-242
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• 2016

To investigate the characteristics of Korean researchers who have published journal articles in the international Library and Information Science (LIS) journals, we analyzed the types of institutions of the authors and their disciplines by publication year and journal type. Specifically, we analyzed 819 pieces of author information from 384 articles published between 2005 and 2014 in 31 journals using the existing categories, allowing comparisons of research trends between past and current and between Korean and international researchers. In addition, we used a co-authorship credit allocation method to calculate the credits of individual authors in an accurate manner, and found that 342.6 of 384 papers were contributed by solely Korean researchers. We demonstrated that the internationality and quality of Korean LIS research has increased during this time. In particular, authors who were affiliated with universities took the lead in publishing papers, and we presented that the nature of research might be related to the type of author affiliation. Based on the disciplinary backgrounds of the researchers, we also suggest that LIS research is associated with such disciplines, computer science, management, and communications.

• 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 Korean Library and Information Science Society
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• v.53 no.4
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• pp.143-162
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• 2022

Altmetrics is an alternative impact evaluation index that evaluates the social interest in the research performance of individuals or institutions in universities, research institutions, and research fund support institutions. This study empirically analyzed what kind of attention a papers of domestic library and information science researchers published in an international academic journal was receiving in the international community using Altmetric explorer. As a result of the analysis, 230 papers were tracked. The average Altmetric Attention Score (AAS) was 6.63, but there were 2 papers that received overwhelming attention (over 170 points) as they were mentioned in news report and Twitter. Second, there was a tendency for high AAS to appear in cases where a domestic researcher participated as a co-author and the main author belonged to an overseas institution, and in the case where the research funds were supported by foreign government agencies. In addition to the field of the library information science or information system, the papers classified as the field of public health service and education showed high AAS, and it was confirmed that these papers were published in the journals of various fields such as life science. Finally, it was confirmed that there was a weak correlation of r =0.25 between the AAS and the number of citations of the analyzed paper, but a strong correlation of r =0.68 between the number of Mendeley readers and the number of citations.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.101-123
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• 2013

Recently due to the advancements of science and information technology, the socio-economic business areas are changing from the industrial economy to a knowledge economy. Furthermore, companies need to do creation of new value through continuous innovation, development of core competencies and technologies, and technological convergence. Therefore, the identification of major trends in technology research and the interdisciplinary knowledge-based prediction of integrated technologies and promising techniques are required for firms to gain and sustain competitive advantage and future growth engines. The aim of this paper is to understand the recent research trend in management of technology (MOT) and to foresee promising technologies with deep knowledge for both technology and business. Furthermore, this study intends to give a clear way to find new technical value for constant innovation and to capture core technology and technology convergence. Bibliometrics is a metrical analysis to understand literature's characteristics. Traditional bibliometrics has its limitation not to understand relationship between trend in technology management and technology itself, since it focuses on quantitative indices such as quotation frequency. To overcome this issue, the network focused bibliometrics has been used instead of traditional one. The network focused bibliometrics mainly uses "Co-citation" and "Co-word" analysis. In this study, a keywords network analysis, one of social network analysis, is performed to analyze recent research trend in MOT. For the analysis, we collected keywords from research papers published in international journals related MOT between 2002 and 2011, constructed a keyword network, and then conducted the keywords network analysis. Over the past 40 years, the studies in social network have attempted to understand the social interactions through the network structure represented by connection patterns. In other words, social network analysis has been used to explain the structures and behaviors of various social formations such as teams, organizations, and industries. In general, the social network analysis uses data as a form of matrix. In our context, the matrix depicts the relations between rows as papers and columns as keywords, where the relations are represented as binary. Even though there are no direct relations between papers who have been published, the relations between papers can be derived artificially as in the paper-keyword matrix, in which each cell has 1 for including or 0 for not including. For example, a keywords network can be configured in a way to connect the papers which have included one or more same keywords. After constructing a keywords network, we analyzed frequency of keywords, structural characteristics of keywords network, preferential attachment and growth of new keywords, component, and centrality. The results of this study are as follows. First, a paper has 4.574 keywords on the average. 90% of keywords were used three or less times for past 10 years and about 75% of keywords appeared only one time. Second, the keyword network in MOT is a small world network and a scale free network in which a small number of keywords have a tendency to become a monopoly. Third, the gap between the rich (with more edges) and the poor (with fewer edges) in the network is getting bigger as time goes on. Fourth, most of newly entering keywords become poor nodes within about 2~3 years. Finally, keywords with high degree centrality, betweenness centrality, and closeness centrality are "Innovation," "R&D," "Patent," "Forecast," "Technology transfer," "Technology," and "SME". The results of analysis will help researchers identify major trends in MOT research and then seek a new research topic. We hope that the result of the analysis will help researchers of MOT identify major trends in technology research, and utilize as useful reference information when they seek consilience with other fields of study and select a new research topic.