DOI QR코드

DOI QR Code

An Analysis of Research Trends in Computational Thinking using Text Mining Technique

텍스트 마이닝 기법을 활용한 컴퓨팅 사고력 연구 동향 분석

  • Lee, Jaeho (Dept. of Computer Education, Gyeongin Nat'l University of Education) ;
  • Jang, Junhyung (Oma Elementary School)
  • Received : 2019.11.05
  • Accepted : 2019.12.17
  • Published : 2019.12.31

Abstract

In 2006, Janet Wing defined computational thinking and operated SW education as a formal curriculum in the UK in 2013. This study collected related research papers by using computational thinking, which has recently increased in importance, and analyzed it using text mining. In the first, CONCOR analysis was conducted with the keyword of computational thinking. In the second, text mining of the components of computational thinking was selected by the repr23esentative academic journals at domestic and foreign. As a result of the two-time analysis, first, abstraction, algorithm, data processing, problem decomposition, and pattern recognition were the core of the study of computational thinking component. Second, research on convergence education centered on computational thinking and science and mathematics subjects was actively conducted. Third, research on computational thinking has been expanding since 2010. Research and development of the classification and definition of computational thinking and components and applying them to education sites should be conducted steadily.

컴퓨팅 사고력에 대한 연구는 2006년 자넷 윙이 이를 정의하고 2014년 영국에서 SW교육을 필수교과로 운영하게 되면서 관련 연구가 본격화 되었다. 본 연구는 최근 중요도가 높아가는 컴퓨팅 사고력을 키워드로 관련 연구논문을 수집하여 텍스트 마이닝 기법으로 분석하였다. 1차는 컴퓨팅 사고력을 키워드로 CONCOR 분석을 하였으며 2차는 국내외 대표 학술지를 선정하여 컴퓨팅 사고력의 구성요소를 텍스트 마이닝 기법으로 분석하였다. 2회에 걸친 분석결과 도출된 시사점은 다음과 같다. 첫째, 추상화, 알고리즘, 데이터처리, 문제분해, 패턴인식은 컴퓨팅 사고력 구성요소에 대한 연구의 핵심을 이루고 있었다. 둘째, 컴퓨팅 사고력과 과학, 수학 교과 중심의 융합 교육에 대한 연구가 활발히 진행되고 있음을 확인하였다. 셋째, 컴퓨팅 사고력에 대한 연구가 2010년 이후 확대되고 있었다. 향후 컴퓨팅 사고력과 구성요소에 대한 분류와 정의를 정립하여 이를 교육현장에 적용하는 연구가 꾸준히 진행되어야 할 필요가 있다.

Keywords

References

  1. Wing. J. M(2006). Computational thinking. Communications of the ACM. 17(3).33-35. https://doi.org/10.1145/1118178.1118215
  2. Korea Ministry of Education(2015). Commentary on the Revised Curriculum for 2015. http://www.edunet.net/nedu/ncicsvc/listSub2015Form.do?menu_id=623.
  3. ISTE(International Society for Technology in Education) (2019). ISTE STANDARDS FOR STUDENTS. https://www.iste.org/standards/forstudents.
  4. Computer Science Teachers Association (2017). CSTA K-12 Computer Science Standards, Revised2017. https://www.csteachers.org/page/standards.
  5. Jaeho Lee and Junhyung Jang (2018). Explore for developing computing thinking tools. Journal of Creative Information Culture, 4(3), 273-283. https://doi.org/10.32823/jcic.4.3.201812.273
  6. MinJeong Kim, WonGyu Lee, and JaMee Kim (2017). Presenting the Development DirectionThrough the Analysis of Tool used to MeasureComputational Thinking. The Journal of Korean association of computer education, 20(6), 17-25. https://doi.org/10.32431/KACE.2017.20.6.002
  7. Japan Ministry of Education (2019). About notebook about informatization of education. http://www.mext.go.jp/a_menu/shotou/zyouhou/1259413.htm.
  8. Korea Beaver Challenge (2019). Beaver Challenge introduction. https://www.bebras.kr/introduce.
  9. British Broadcasting Corporation (2019). Introduction to computational thinking. https://www.bbc.co.uk/bitesize/guides/zp92mp3/revision/1.
  10. ISTE(International Society for Technology in Education). ISTE STANDARDS FOR EDUCATORS. https://www.iste.org/standards/for-educators
  11. RAY. MARK (2019). Learning about Computational Thinking. Teacher Librarian 46(4), pp 8-12.
  12. Prakken, L. W (1942). The Education Digest (Vol. 16). Education Digest.
  13. TEXTOM. http://www.textom.co.kr/home/main/main.php
  14. Salton, G., and Yang, C. S (1973). On the specification of term values in automatic indexing. Journal of documentation, 29(4), 351-372. https://doi.org/10.1108/eb026562
  15. Zhang, W., Yoshida, T. and Tang, X (2011). A comparative study of TF* IDF, LSI and multi-words for text classification. Expert Systems with Applications, 38(3), 2758-2765. https://doi.org/10.1016/j.eswa.2010.08.066
  16. Ji-suk Hong and Ick-keun Oh (2016). Image difference of before and after an incident using social big data analysis. International Journal of Tourism and Hospitality Research, 30(6), 119-133.
  17. Yonghak Kim and Yeongjin Kim (2016). Social Network Analysis: 4th edition. Seoul: Bakyeongsa.

Cited by

  1. 텍스트 마이닝을 활용한 윤리적 패션 연구동향: 2009-2019 연구 네트워크 분석 vol.22, pp.2, 2019, https://doi.org/10.5805/sfti.2020.22.2.181
  2. 언어 네트워크를 이용한 야외지질답사 관련 연구 동향 분석: 최근 21년(2000~2020년)을 중심으로 vol.14, pp.2, 2019, https://doi.org/10.15523/jksese.2021.14.2.173