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Social Perception of the Invention Education Center as seen in Big Data

빅데이터 분석을 통한 발명 교육 센터에 대한 사회적 인식

  • Lee, Eun-Sang (Technology and Home Economics Education, Kongju National University)
  • 이은상 (공주대학교 기술가정교육과)
  • Received : 2021.11.25
  • Accepted : 2022.01.20
  • Published : 2022.01.28

Abstract

The purpose of this study is to analyze the social perception of invention education center using big data analysis method. For this purpose, data from January 2014 to September 2021 were collected using the Textom website as a keyword searched for 'invention+education+center' in blogs, cafes, and news channels of NAVER and DAUM website. The collected data was refined using the Textom website, and text mining analysis and semantic network analysis were performed by the Textom website, Ucinet 6, and Netdraw programs. The collected data were subjected to a primary and secondary refinement process and 60 keywords were selected based on the word frequency. The selected key words were converted into matrix data and analyzed by semantic network analysis. As a result of text mining analysis, it was confirmed that 'student', 'operation', 'Korea Invention Promotion Association', and 'Korean Intellectual Property Office' were the meaningful keywords. As a result of semantic network analysis, five clusters could be identified: 'educational operation', 'invention contest', 'education process and progress', 'recruitment and support for business', and 'supervision and selection institution'. Through this study, it was possible to confirm various meaningful social perceptions of the general public in relation to invention education center on the internet. The results of this study will be used as basic data that provides meaningful implications for researchers and policy makers studying for invention education.

이 연구의 목적은 빅데이터 분석 방법을 이용하여 발명 교육 센터에 대한 사회적 인식을 확인해 보는 데 있다. 이를 위해 TEXTOM 사이트를 이용하여 네이버와 다음 사이트의 블로그, 카페, 뉴스 채널에서 '발명+교육+센터'를 검색 키워드로 2014년 1월부터 2021년 9월까지의 데이터를 수집하였다. 수집된 데이터는 TEXTOM 사이트에서 정제하였으며, 텍스트 마이닝 분석과 의미 연결망 분석을 위해 TEXTOM 사이트, Ucinet 6, Netdraw 프로그램을 이용하였다. 수집된 데이터는 1차와 2차의 정제 과정을 거쳐 단어빈도를 바탕으로 주요 키워드 60개를 선정하였으며, 선정된 주요 키워드는 매트릭스 데이터로 변환하여 의미 연결망 분석을 실시하였다. 이 연구의 텍스트 마이닝 분석 결과 '학생', '운영', '한국발명진흥회', '특허청' 등이 의미 있는 키워드임을 확인하였다. 의미 연결망 분석 결과 발명 교육 센터와 관련된 '교육 운영', '발명 대회', '교육 과정 및 진행', '사업 모집 및 지원', '주관 및 선정 기관' 등 5개의 군집을 확인할 수 있었다. 이 연구의 결과는 발명 교육 센터에 대한 연구를 수행하는 연구자나 정책 입안자의 학술 연구에 활용될 수 있을 것이다.

Keywords

Acknowledgement

This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT) (No. 2021R1F1A104755011).

References

  1. S. Ryu. (2020). Perception about Invention of Elementary School Students. The Journal of Korea Elementary Education, 31(3), 89-100.
  2. E. Lee. (2015). Research Trends and Issues of Invention Education in Early Childhood, Elementary and Secondary Education : Focussing on Journals in Korea. Korean Journal of Teacher Education, 31(3), 333-356. https://doi.org/10.14333/KJTE.2015.31.3.333
  3. G. Lee & I. Park. (2014). An Analysis into the Actual State of Operation for Invention Classes Using the Invention Education Activation Index. The Journal of Korea Elementary Education, 25(2), 179-190. https://doi.org/10.26844/ksepe.2020.25.4.179
  4. Y. Son, D. Jeong, D. Lee, Y. Lim, J. Yoon, K. Lee, et al. (2017). The Analysis of Invention Education Center Teachers' Perceptions on the Operation of the Invention Education Center. The Korean Journal of Technology Education, 17(3), 23-44.
  5. H. Lee. (2017). Analysis of Invention Education Center's Students Selection System in Gyeonggi-do. Seoul National University of Education Master's thesis.
  6. Y. Koo. (2020). Trend Analysis on Clothing Care System of Consumer from Big Data. Fashion & Textile Research Journal, 22(5), 639-649. https://doi.org/10.5805/SFTI.2020.22.5.639
  7. C. Oh. (2017). Analysis of Meaning of Social Conflict Discussion in Korea: Focusing on Key Word Network in Major Portals. Journal of Political Communication, 45, 37-67. https://doi.org/10.35731/kpca.2017..45.002
  8. T. Kim & S.-W. Kim. (2019). Social Tendency and Network Analysis of High School Credit System. Journal of Educational Innovation Research, 29(2), 225-242. https://doi.org/10.21024/pnuedi.29.2.201906.225
  9. J. Choi & S. Park. (2020). A study on perception of golf lesson using big data analysis. Journal of Golf Studies, 14(1), 151-163. https://doi.org/10.34283/ksgs.2020.14.1.13
  10. S. Kwak & H. Kim. (2019). Keywords and Topic Analysis of Social Issues on Twitter Based on Text Mining and Topic Modeling. KIPS Transactions on Software and Data Engineering, 8(1), 13-18. https://doi.org/10.3745/KTSDE.2019.8.1.13
  11. Y. Moon. (2020). An Analysis of Trends in Researches on the Open Recruitment System for Principals based on Topic Modeling and Keyword Network Analysis. Journal of Education & Culture, 26(1), 217-242. https://doi.org/10.24159/JOEC.2020.26.1.217
  12. J. Kang & Y. Lee. (2019). A Big Data Analysis of 'Youth Counseling 1388' Utilizing Text Mining: Focused on NAVER Knowledge iN, 2011-2018. The Korea Journal of Youth Counseling, 27(2), 127-147. https://doi.org/10.35151/KYCI.2019.27.2.006
  13. S. Lee. (2013) Network Analysis Methodology. Seoul: Nonhyeong.
  14. J. Park. (2020). A Study on Social Recognition of the Collaborative Curriculum between Schools Using Big Data Analysis. Journal of Education & Culture, 26(5), 85-104. https://doi.org/10.24159/JOEC.2020.26.5.85
  15. Invention Education Portal Site. (2021). Introduction to the Invention Education Portal Site. Invention Education Portal Site. https://www.ip-edu.net.
  16. Korea Invention Promotion Association. (2021). Introduction to the Korea Invention Promotion. Korea Invention Promotion Association. Association. https://www.kipa.org/.
  17. Korean Intellectual Property Office. (2021). Introduction to the Korean Intellectual Property Office. Korean Intellectual Property Office.https://www.kipo.go.kr/.