디지털융복합연구 (Journal of Digital Convergence)

  • 제19권1호
  • /
  • Pages.29-43
  • /
  • 2021
  • /
  • 2713-6434(pISSN)
  • /
  • 2713-6442(eISSN)
한국디지털정책학회 (The Society of Digital Policy and Management)
권호 표지
DOI QR코드

DOI QR Code

텍스트 마이닝을 활용한 온라인 교육에 대한 소비자 인식 변화 분석: COVID-19 전후를 중심으로

A Study on Consumer perception changes of online education before and after COVID-19 using text mining

  • 손민성 (고려사이버대학교 휴먼서비스학부 보건행정학과) ;
  • 임미자 (고려사이버대학교 경영학부 경영학과) ;
  • 박경환 (고려사이버대학교 경영학부 경영학과)
  • 투고 : 2020.10.29
  • 심사 : 2021.01.20
  • 발행 : 2021.01.28
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

COVID-19 이후 국내는 물론, 전 세계적으로 온라인 교육은 절대적으로 필요하며 대체 불가한 교육 형태가 되었다. 온라인 교육이 급부상 하면서 교육 형태에 대해 사람들이 가지는 인식은 어떠한지, 만약 변화가 있다면 어떻게 변화했는지는 매우 궁금증을 자아내는 질문이다. 본 연구는 온라인 교육에 대한 소비자 인식의 변화 추이를 빅데이터를 활용하여 조사하였다. 이를 위해 코로나 이전(2019년 11월-12월), 코로나 촉발 이후(2020년 1월-2월), 온라인 개강직후(2020년 3월-4월), 온라인 교육을 일정 정도 경험한 이후(2020년 5월-6월)의 4개의 구간으로 구분하고, 텍스트 마이닝 즉, 키워드 빈도분석, 워드클라우드 분석, 네트워크 분석, 감성 분석을 수행하였다. 시기별로 온라인 교육 관련 키워드의 출현빈도는 코로나 이전에는 학점은행제, 평생교육, 블로그 등에서 코로나 이후 학교 개강이 시작되면서 온라인 개학, 비대면 교육, 실시간, 콘텐츠 제작, 유튜브 등으로 변화하였다. 감성분석 결과, 코로나 사태 이전에는 공지안내, 정보교류 등의 중립글이 대부분이었으나, 코로나 발생을 계기로 온라인 교육에 대한 사람들의 인식과 평가에 대한 긍정 및 부정의 의견이 논의되기 시작하였다. 또한 미래 온라인 교육시장의 확산과 전망 등 방향성에 대해서도 관심이 증대되었다. 온라인 교육은 발전가능성이 높은 만큼 앞으로 개선해야 할 부분들이 많겠으나, 교육 정책입안자, 현장에서 일하는 교육자들에게 온라인 교육 품질 개선 및 향후 나아갈 방향 수립에 도움을 줄 수 있을 것이다.

Coinciding with the advent of COVID-19, online education is on the rise both domestically and globally, and has become an absolutely necessary and irreplaceable form of education. It is a very curious question what the perception of people about the suddenly growing form of education is, and how it has changed. This study investigated changes in consumers' perception of online education using big data. To this end, we divided the time into four stages: before COVID-19 (November to December 2019), after the triggering of COVID-19 (January to February 2020), right after the online classes started (March to April 2020), after experiencing some online education (May to June 2020). Then we conducted text mining, namely, keyword frequency analysis, network analysis, word cloud analysis, and sentiment analysis were performed. The implications derived as a result of the analysis can help education policy makers and educators working in the field to improve online education quality and establish its future directions.

키워드

참고문헌

  1. National IT Industry Promotion Agency. (2019). "2019 Survey of Korean E-Learning Industry", Ministry of Trade, Industry & Energy. https://www.iitp.kr/kr/1/knowledge/statisticsView.it?masterCode=publication&searClassCode=K_STAT_01&identifier=02-008-200603-000001
  2. M. K. Moon, S. H. Park. (2019). Meaning of Life Planning for Middle-aged Women: Utilizing Big Data, Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology, Vol.9, No.3, 643-651. DOI: 10.35873/ajmahs.2019.9.3.060.
  3. S. W. Kim, H. W. Ahn, Y. A. Jang, M. Y. Hong, M. J. Seo, S. T. Kim. 2019. A Big-data Study of YouTube Comments to Classify Customer Journey Decisions - Proposing a New Exploratory Analysis Method, Journal of Image and Cultural Contents 16, 57-89. DOI:: 10.24174/jicc.2019.02.16.57.
  4. Wilde, D. (2020). Coronavirus Lockdown Has Changed Somepeople's EV Attitudes. https://uk.motor1.com/news/408466/coronavirus-lockdown-changed-ev-attitudes/.
  5. SNUBIZ series, 2020. 08. J.Y. Song, "Perfect Change: Change Korean Corporate Strategic Paradigm in Post-Pandamic Paradigm Change Era". https://www.youtube.com/watch?v=TIpEflMRnQA
  6. Views Letter, 2020.08.06. Issue No. 83. https://mailchi.mp/themiilk/hc1m40u65m?fbclid=IwAR3JFht5bJlJcnhYta7cilRBBzj6slkXkp6ohYi7c-abc3y_bVJnELH3kyw
  7. I. J. Rha. (2010). Theories Related to Educational Technology. KyungKiDo : KYOYOOK BOOK. p. 136.
  8. R. M. Delling. (1987). Towards a Theory of Distance Education. ICDE Bulletin,13. January, 21-25. In L. Visser, Y. L. Visser, R. J. Amirault, & M. Simonson. (2012). Trends And Issues In Distance Education (An International Perspective) pp.23-54). Charlotte, North Carolina: IAP- Information Age Publishing, Inc.
  9. C. Wedemeyer. (1971). Independent Study. In R. Deighton, Encyclopedia of Education IV (pp. 548-557). New York: McMillan.
  10. M. G. Moore & W. G. Anderson. (2003). Handbook of Distance Education, Mahwah, NJ: Lawrence Erlbaum Associates.
  11. O. Peters. (2000). The Transformation of the University into an Institution of Independent Learning. In T. Evans, & D. Nation (Eds.), Changing University Teaching(pp. 10-23). Londeon: Kogan Page.
  12. B. Homberg. (1986). Growth and Structure of Distance Education. London: Croom Helm.
  13. I. S. Joung & I. J. Rha. (2004). Understanding Distance Learning. Soule: KYOYOOKBOOK.
  14. M. Simonson, S. Smaldino, M. Albright, & S. Zvacek. (2000). Teaching and Learning at a Distance: Foundations of Online Education. Upper Saddle River, NJ: Pearson.
  15. L. S. Jung & I. Rha. (2000). Effectiveness and Cost-effectiveness of Online Education: A Review of Literature. Education Technology, 40(4), 57-60. https://www.jstor.org/stable/44428629
  16. P. A. Kirschner, J. Strijbos, K. Kreijns, & P. J. Beers. (2004). Designing Electronic Collaborative Learning Environments. Educational Technology Research and Development, 52(3), 47-66. DOI: 10.1007.BF02504675 https://doi.org/10.1007.BF02504675
  17. Y. H. Cho, S. Y. Huh, H. S. Choi, J. Y. Kim, H. K. Lee. (2015). Research Trend of Online Learning in Higher Education: An Ecological Approach. Journal of Educational Technology, 31(4). 725-755. http://dx.doi.org/10.17232/KSET.31.4.725
  18. E. K. Lee. (2006). Instructors` Perception of Online Education and Their Instructional Strategies, Journal of Educational Information and Media, 12(1), 87-106. DOI: I410-ECN-0102-2009-330-000863873
  19. H. G. Jung. (2020). College Students' Satisfaction with the Overall Implementation of Online Classes and Testing During the Corona 19 Pandemic. Multimedia-Assisted Language Learning, 23(3), 392-412. DOI: 10.15702/mall.2020.23.3.392
  20. D. J. Lee, & M. S. Kim. (2020). University Students' Perceptions on the Practices of Online Learning in the COVID-19 Situation and Future Directions. Multimedia-Assisted Language Learning, 23(3), 359-377. DOI: 10.15702/mall.2020.23.3.359
  21. H. M. Che, D. K. Lee. (2020). Exploring Secondary Teacher's Experience of Distance Learning due to COVID-19. Journal of Learner-centered Curriculum and Instruction, 20(16), 1047-1071. http://dx.doi.org/10.22251/jlcci.2020.20.16.1047
  22. Etody. (2020.9.21). 79% of Teachers "The Learning Gap Has Widened In Remote Classes." Name of Web Site. https://www.etoday.co.kr/news/view/1943112
  23. Medical Research Information Center. (2020.4.22.). Analysis of Domestic Media Reports on Online Education Related to COVID-19. https://www.medric.or.kr/Controls/Sub.aspx?d=06&s=01&g=NOTICE&m=VIEW&i=1592
  24. S. M. Kim. (2020). Analysis of Press Articles in Korean Media on Online Education Related to COVID-19, Journal of Digital Contents Society, 21(6), 1091-1100. http://dx.doi.org/10.9728/dcs2020.21.6.1091
  25. Asia-tody. (2020.6.7). Gyeonggi Research Institute. Education of the Future is the 'Learning Age.' Name of Web Site. https://www.asiatoday.co.kr/view.php?key=20200607010004040
  26. Maekyung Meda Group. (2020.09.24). 83% of Parents "I Feel the Need for Private Education through Distance Education." Name of Web Site. https://news.v.daum.net/v/20200924114501935
  27. Gartner and others, http://www.datamation.com/big-data/big-data-analytics.html
  28. U. H. Lee, H. C. Park, H. W. Park. (2017). Examining Facebook Users in Hobby & Leisure Communities using Social Big Data in terms of their Reactions and Relations. The Journal of Society for Humanities Studies in East Asia, 41, 345-382.
  29. K. S. Noh. (2015). Educational Policy Proposals through Analysis of the Perception of Big data for University Students. Journal of Digital Convergence, 13(11), 25-33. DOI: 10.14400/JDC.2015.13.11.25.
  30. S. H. Whang, D. Y. Koo, J. G. Kim. 2019. Application of Social Big Data Analysis for CosMedical Cosmetics Marketing: H Company Case Study, Journal of Digital Convergence Vol. 17. No. 7, 35-41. DOI : 10.14400/JDC.2019.17.7.035.
  31. Korea Database Agency. 2014. The Guide for Advanced Data Analytics Professional. ISBN : 9788988474242.
  32. M. M. C. Han, Y. S. Kim & C. K. Lee. (2017). Analysis of News Regarding New Southeastern Airport Using Text Mining Techniques, Smart Media Journal, 6(1), 47-53. UCI : G704-SER000003489.2017.6.1.008.
  33. J. H. Ryu, Y. Yoo. (2018). The Fourth Industrial Revolution Core Technology Association Analysis Using Text Mining. Journal of Digital Convergence, Vol. 16(8), 129-136. DOI : 10.14400/JDC.2018.16.8.129.
  34. Y. H. Lim, H. B. Kim (2019), A study on the sentiment analysis using big data of hotels online review, KJHA Korean journal of hotel Administration, 28(7), 105-123. DOI : 10.14400/JDC.2018.16.8.129.
  35. S. M. Kim. (2020), Analysis of Press Articles in Korean Media on Online Education related to COVID-19. Journal of Digital Contents Society 21(6), 2020.6, 1091-1100. DOI : 10.9728/dcs.2020.21.6.1091.
  36. J. W. Do, S. J. Kim, J. W. Moon. (2020), Exploring Synchronous Online Course Cases on Secondary Schools via Semantic Network Analysis, Journal of Qualitative Inquiry, 6(3), 637-681. https://www.kci.go.kr/kciportal/ci/sereArticleSearch/ciSereArtiView.kci?sereArticleSearchBean.artiId=ART002634138
  37. S. Kyun, H. Kim, & S.Y. Lee. (2019), Analyzing the Keywords of Future Education using Text-mining Methodology, Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology,9(5), 11-23. DOI : 10.35873/ajmahs.2019.9.5.002.
  38. Park, M. C. (2020). COVID-19 Era Online Education Experience, https://www.jjan.kr/news/articleView.html?idxno=2081351
  39. National law information center. (2020). Enforcement Decree of the Elementary and Secondary Education Act, http://www.law.go.kr/