• Journal of Digital Convergence
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• v.19 no.12
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• pp.359-367
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• 2021

The purpose of this study is to analyze domestic patient safety culture research topics using text mining and CONCOR analysis. The research method was conducted in the stages of data collection, data preprocessing, text mining and social network analysis, and CONCOR analysis. A total of 136 articles were analyzed excluding papers that were not published. Data analysis was performed using Textom and UCINET programs. As a result of this study, TF (frequency) of patient safety culture-related studies showed that patient safety was the highest, and TF-IDF (importance in documents) was highest in nursing. As a result of the CONCOR analysis, a total of seven clusters were derived: knowledge and attitude, communication, medical service, team, work environment, structure, organization and management that constitute the patient safety culture. In the future, it is necessary to conduct research on the relationship between the establishment of a patient safety culture and patient outcomes.

• Smart Media Journal
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• v.11 no.10
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• pp.54-64
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• 2022

A big data-driven digital transformation is defined as a process that aims to innovate companies by triggering significant changes to their capabilities and designs through the use of big data and various technologies. For a successful big data-driven digital transformation, reviewing related literature, which enhances the understanding of research statuses and the identification of key research topics and relationships among key topics, is necessary. However, understanding and describing literature is challenging, considering its volume and variety. Establishing a common ground for central concepts is essential for science. To clarify key research topics on the big data-driven digital transformation, we carry out a comprehensive literature review by performing text mining of 439 articles. Text mining is applied to learn and identify specific topics, and the suggested key references are manually reviewed to develop a state-of-the-art overview. A total of 10 key research topics and relationships among the topics are identified. This study contributes to clarifying a systematized view of dispersed studies on big data-driven digital transformation across multiple disciplines and encourages further academic discussions and industrial transformation.

• Cross-Cultural Studies
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• v.49
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• pp.95-126
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• 2017

The purpose of this paper is to compare learners' academic texts with academic texts of native speakers and to examine the usage patterns of learners' objectification strategies in detail. In order to achieve objectivity as a discourse mechanism applied to describe the results of academic inquiry in a scientific way with universality and validity, we analyzed concepts and signs such as related intentionality, accuracy, and mitigation of the linguistic markers of objectification strategies. As a result of the comparison, it was analyzed that there are intersectional overlaps with the signs that reveal objectivity, signs indicating related mechanisms, and there is a different set that is differentiated. Objective markers can be broadly classified as emphasizing stativity of research results, separating research subjects from research results, and generalizing research contents. Sustainable expressions and noun phrases emphasize statehood, and non-inhabited expressions, passive expressions, and self-quotations are maintained in the distance between the claimant and the writer, and the pluralization through first-person pronouns and suffixes contributes to generalization. In the case of the learner, the non-inhuman expression of the quotation type appears to be very less compared to the maw speaker, which could be due to the lack of recognition of the citation method of the Korean academic text. Next, in the generalization of the research contents, the expression of 'we' was very less compared to the maw speakers.

• Proceedings of the Korean Society of Computer Information Conference
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• 2020.01a
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• pp.275-276
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• 2020

이 연구의 목적은 기호학적 관점에서 프로그래밍에서 발생하는 학습자의 사고 과정을 이해하기 위함이다. 그래서 프로그래밍의 표상과정을 이해하기 위한 기호작용 모형을 제안하였다. 이 연구의 결론은, 프로그래밍 교육에서 구성주의(constructivism) 학습 이론을 적용하기 위해서는 개인의 해석체와 프로그래밍 요소에서 인터텍스트(intertext) 속성을 파악하는 것이 선결과제인 것과 프로그래밍 맥락인 콘텍스트(context)의 중요함을 확인하였다. 후속 연구로 인지언어학적 방법으로 학습자가 프로그래밍에서 표상한 해석체(interpretant)와 콘텍스트(context), 인터텍스트(intertext) 등의 상호작용을 구체적으로 알아보는 연구를 진행하고자 한다.

• Annual Conference of KIPS
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• 2021.11a
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• pp.462-465
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• 2021

본 연구는 최근 ICT분야 R&D 동향을 파악하기 위하여 NTIS에서 제공하는 국가연구개발사업 과제정보를 텍스트 마이닝 기법을 통해 분석하였다. 2017년부터 2020까지의 과제 정보에서 키워드를 추출하고 연결 관계 마이닝을 통해 키워드 네트워크를 시각화하였다. 분석 결과는 다음과 같다. 첫째, 정보통신 각 분야에서 핵심 연구주제가 기술의 발전에 따라 변화하고 있음을 관찰하였다. 둘째, 키워드 네트워크 상에서 허브 역할을 하는 키워드를 통해 분야 간 융합의 매개 기술을 파악할 수 있었다. 마지막으로, 연도별 키워드 네트워크를 비교·분석함으로써 새롭게 등장하거나 연결 상태의 변화를 보이는 이머징(Emerging) 키워드를 통해 미래 유망 기술이나 최신 연구 방향성을 감지할 수 있음을 보였다.

• Korean Journal of Construction Engineering and Management
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• v.21 no.2
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• pp.15-21
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• 2020

The relationship between keywords, found in all productivity related papers published in the KSCE journal for last 15 years, were analyzed in order to reveal a research trend in the area using text mining and A-Priori algorithm. As the results, it is found that the word of 'productivity' is most closely related to the words of 'work' and 'labor'. Futhermore, the word is somewhat related to those of 'factor', 'model', simulation', and 'work time'. It is also revealed that, on the other hand, the words of 'machine' and 'equipment' have little relationships with the keyword. This research will be a great help for academia to understand a research trend in the area of construction productivity.

• Journal of Korean Society for Geospatial Information Science
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• v.22 no.3
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• pp.87-97
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• 2014

City is currently developing into intelligent city which adopts the ICT technology to resolve the problems and increase the competitiveness. This intelligent city is promoted under the name of U-City or Smart City, yet it is also criticized in the trend for what the differences between U-City and Smart City are. In this study, we draws the differences between U-City and Smart City from our distinctive research method, text mining which analyzes the trend of research papers, and contribute to direction of U-City study in the future. Through this analysis, the study results in that U-City focuses practical implementation in domestic cities while Smart City focuses technological development and provision of single service. However, this paper has a limitation as the subjective opinion was reflected to configure the sets of keywords, and only keywords and s were analyzed. Therefore, further studies are needed to confirm the differences between U-City and Smart City with related research papers and reports.

• Journal of the Korea Convergence Society
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• v.12 no.9
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• pp.295-305
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• 2021

The purpose of this study is to explore the trend of nursing professional research topics published domestically through quantitative content analysis. The research method performed procedures for collecting academic papers, refining and extracting words, and data analysis. A text network was developed by collecting 351 papers and extracting words from the abstract, and network analysis and topic modeling were performed. The core-topics were nurses, nursing professionalism, nursing students, nursing care, professional self-concept, health care professionals, satisfaction, clinical competence, and self-efficacy. Through topic modeling, topic groups of nurse's professionalism, nursing students' professionalism, nursing professional identity, and nursing competency were identified. Over time, core-topics remained unchanged, but topics such as role conflict and ethical values in the 1990s, self-leadership and socialization in the 2000s, and clinical practice stress and support systems in the 2010s have emerged. In conclusion, it is necessary to facilitate multidimensional interventional research to improve nursing professionalism of clinical nurses and nursing students.

• Informatization Policy
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• v.25 no.4
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• pp.84-107
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• 2018

The existing research on domestic e-government trends in Korea has weaknesses in that it depends only on qualitative research methods. Therefore, a quantitative analysis was conducted through this study as of September 2018 based on the data from 1996 to 2017. A total of seven research topics were derived from text mining, of which the network centrality of the framework and public policy effect were identified as highly significant. The results of this study provide academic and policy implications for the development of e-government. including that using a quantitative analysis method instead of a qualitative method contributes to ensuring relative objectivity and diversity of learning.

• Journal of Digital Convergence
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• v.16 no.12
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• pp.61-69
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• 2018

This study aims to understand and to infer the implications from the career preparation experiences of low-income university students in the context of high youth unemployment rate and the polarization of the social classes. For this purpose, we selected 13 university students who received scholarship from the S scholarship foundation and conducted analysis using text mining techniques based on the six-time interviews. According to the results, university students seem to be influenced by home environment and income level when recalling previous academic experience or designing career during the interview process. Also, these differences were found to have different characteristics according to gender and major. This study is meaningful in that the qualitative research data is analyzed by applying the text mining technique in a convergent way. As a result, the college life and career preparation of low-income university students were explored through the frequency and relation of words.