Journal of The Korean Association of Information Education (정보교육학회논문지)

Korea Association of Information Education (한국정보교육학회)
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Analysis on Trend of Study Related to Computational Thinking Using Topic Modeling

토픽 모델링을 이용한 컴퓨팅 사고력 관련 연구 동향 분석

  • Moon, Seong-Yun (Dept. of Computer Education, Korea National University of Education) ;
  • Song, Ki-Sang (Dept. of Computer Education, Korea National University of Education)
  • 문성윤 (한국교원대학교 컴퓨터교육과) ;
  • 송기상 (한국교원대학교 컴퓨터교육과)
  • Received : 2019.11.27
  • Accepted : 2019.12.14
  • Published : 2019.12.31
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Abstract

As software education was introduced through the 2015 revised curriculum, various research activities have been carried out to improve the computational thinking of learners beyond the existing ICT literacy and software utilization education. With this change, the purpose of this study is to examine the research trends of various research activities related to computational thinking which is emphasized in software education. To this end, we extracted the key words from 190 papers related to computational thinking subject published from January 2014 to September 2019, and conducted frequency analysis, word cloud, connection centrality, and topic modeling analysis on the words. As a result of the topical modeling analysis, we found that the main studies so far have included studies on 'computational thinking education program', 'computational thinking education for pre-service teacher education', 'robot utilization education for computational thinking', 'assessment of computational thinking', and 'computational thinking connected education'. Through this research method, it was possible to grasp the research trend related to computational thinking that has been conducted mainly up to now, and it is possible to know which part of computational thinking education is more important to researchers.

2015개정 교육과정을 통해 소프트웨어 교육이 도입되면서 기존의 ICT 소양 및 응용 소프트웨어 활용 교육에서 벗어나 학습자의 컴퓨팅 사고력을 향상시키기 위한 다양한 연구 활동이 이루어져 왔다. 이와 같은 변화에 따라 본 연구에서는 소프트웨어 교육에서 강조되고 있는 컴퓨팅 사고력과 관련된 다양한 연구 활동에 대한 연구 동향을 살피는데 그 목적이 있다. 이를 위해 2014년 1월부터 2019년 9월까지 출판된 컴퓨팅 사고력과 관련된 190편의 논문을 대상으로 주제어를 추출하여 그 단어들을 대상으로 빈도분석, 워드 클라우드, 연결 중심성, 토픽 모델링분석을 실시하였다. 토픽 모델링 분석 결과 지금까지의 주된 연구에는 '컴퓨터 사고력 교육 프로그램', '컴퓨팅 사고력 예비교사 교육', '컴퓨팅 사고력 로봇 활용 교육', '컴퓨팅 사고력 평가', '컴퓨팅 사고력 교과 연계 교육'에 관한 연구들이 진행되고 있음을 확인할 수 있었다. 본 연구 방법을 통해 현재까지 주로 진행되고 있는 컴퓨팅 사고력 관련 연구 동향을 파악할 수 있었고, 이는 컴퓨팅 사고력 교육의 어떤 부분이 연구자들에게 더 중요하게 인식되고 있는지를 알 수 있게 해 준다.

Keywords

References

  1. JiWon Lee, JeongBeom Kim, JungBog Kim (2018). Effects of the Experience in Developing Physics Teaching Materials Based on Computational Thinking for Improvement of Science Teachers' and Pre-service Teachers' Technological Pedagogical and Content Knowledge(TPACK). New Physics: Sae Mulli, 68(2), 1-15. https://doi.org/10.3938/NPSM.68.1
  2. HyungWook Kim, SeongYun Mun, SoRi Jeong, SoJean Jeong (2018). The Effect of Making My Own Game using ‘Entry and Arduino' on Elementary Students Creative Problem Solving Ability and Interpersonal Relationship Ability. Journal of Learner-Centered Curriculum and Instruction, 18(1), 487-507. https://doi.org/10.22251/jlcci.2018.18.9.487
  3. JungSook Sung, HyeonCheol Kim (2015). Analysis on the International Comparison of Computer Education in Schools. Journal of The Korean Association of Information Education, 20(6), 543-552.
  4. JaeHwi Kim, DongHo Kim (2016). Development of Physical Computing Curriculum in Elementary Schools for Computational Thinking. Journal of The Korean Association of Information Education, 20(1), 69-82. https://doi.org/10.14352/jkaie.2016.20.1.69
  5. EunJi Seong (2014). A Study on Computer Programming Education for Elementary, Master's Thesis, Seoul National University of Education.
  6. DongMan Kim, TaeWuk Lee (2018). A Meta-Analysis on the Effects of Software Education on Computational Thinking. Journal of The Korea Society of Computer and Information, 23(11), 239-246. https://doi.org/10.9708/JKSCI.2018.23.11.239
  7. Hyun Joo, DongSik Kim, JinJu Lee, ChungSoo Na (2018). Inducing Computational Thinking in Korean SW Education: Synthesizing Standardized Mean Changes through Meta-analysis. Journal of Educational Technology, 34(3), 775-815. https://doi.org/10.17232/KSET.34.3.775
  8. SangTae Na, JaHee Kim, MinHo Jung, JooEon Ahn (2016). Trend Analysis using Topic Modeling for Simulation Studies. Journal of the Korea Society for Simulation, 25(32), 107-116. https://doi.org/10.9709/JKSS.2016.25.3.107
  9. JunSeok Oh (2015). Identifying Research Opportunities in the Convergence of Transportation and ICT Using Text Mining Techniques. Journal of Transport Research, 22(4), 93-110. https://doi.org/10.34143/jtr.2015.22.4.93
  10. Wing, J. M. (2008). Computational thinking and thinking about computing. Philosophical transactions of the royal society of London A: mathematical, physical and engineering sciences. 366(1881), 3717-3725. https://doi.org/10.1098/rsta.2008.0118
  11. MOE (2015). Software Education operating instructions.
  12. ISTE & CSTA (2011). Operational Definition of Computational Thinking for K-12 Education. https://id.iste.org/docs/ct-documents/computational-thinking-operational-definition-flyer.pdf.
  13. SungHoon Seo, HakYeon Lee (2015). Fintech trend analysis using topic modeling of BM patents. The Korean Institute of Industrial Engineers fall conference, 471-480.
  14. David M. Blei, Andrew Y. Ng, Michael I. Jordan (2003). Latent Dirichlet Allocation. Journal of Machine Learning Research, 3(JAN), 993-1022.
  15. JuSeop Park, SoonGoo Hong, JongWeon Kim (2017). A Study on Science Technology Trend and Prediction Using Topic Modeling. Journal of the Korea Industrial Information Systems Research, 22(4), 19-28. https://doi.org/10.9723/jksiis.2017.22.4.019
  16. SunJu Park (2015). A Topic Analysis of SW Education Textdata Using R. Journal of The Korean Assocaition of Information Education, 19(4), 517-524. https://doi.org/10.14352/jkaie.2015.19.4.517
  17. HyeYoung Han (2019). A study of research trends in nurses turnover using Topic modeling and Keyword Network Analysis. Master's Thesis, Korea University.
  18. David M. Blei (2012). Probabilistic Topic Models, Communications of the ACM, 55(4), 77-84. https://doi.org/10.1145/2133806.2133826
  19. JaeWon Choi, Ho Lee, JungMin Kim, JuHo Song (2017). A Comparative Analysis of Curriculums for Software-related Departments based on Topic Modeling. Journal of Society for e-Business Studies, 22(4), 193-214. https://doi.org/10.7838/jsebs.2017.22.4.193
  20. MinChae Kim, YoungHwan Kim (2018). Analysis of Research Trends on Digital Textbook: Based on Text Network Analysis. Journal of Educational Information and Media, 24(2), 387-413.
  21. DooBong Kang (2019). Comparison of Unplugged Activities at Home and Abroad using Semantic Network Analysis. Journal of Korean association of computer education, 22(4), 21-34. https://doi.org/10.32431/KACE.2019.22.4.003
  22. Gyun Heo (2016). A Study on the Research Trends to Flipped Learning through Keyword Network Analysis. Journal of fisheries and marine sciences education, 28(3), 872-880. https://doi.org/10.13000/JFMSE.2016.28.3.872
  23. YoungChoo Choi, SuJung Park (2011). Analyzing Trends in the Study of Public Administration: Application of the Network Text Analysis Method. KOREAN REPUBLIC ADMINISTRATION REVIEW, 45(1), 123-139.
  24. JaeChang Kho, KuenTae Cho, YoonHo Cho (2013). A Study on Recent Research Trend in Management of Technology Using Keywords Network Analysis. Journal of intelligence and information systems, 19(2), 101-123. https://doi.org/10.13088/jiis.2013.19.2.101
  25. JuYeon Lee, YooHyun Park (2016). Social Network Analysis of author's interest area in Journals about Computer. Journal of the Korea Institute of Information and Communication Engineering, 20(1), 193-199. https://doi.org/10.6109/jkiice.2016.20.1.193
  26. SooSang Lee (2016). A Study on the Application of Topic Modeling for the Book Report Text. Journal of Korean Library and Information Science Society, 47(4), 1-18. https://doi.org/10.16981/kliss.47.4.201612.1
  27. Marwa Naili, Anja Chaibi, Henda Ghezala. (2017). Arabic topic identification based on empirical studies of topic models. Revue Africaine de la Recherche en Informatique et Mathematiques Appliquees, 27. 45-49.
  28. Kyrola, Aapo. 10-702 Project Report: Parallel LDA, Truth or Dare?.
  29. SeungKi Shin, YoungKwon Bae (2014). Analysis and Implication about Elementary Computer Education in India. Journal of The Korean Association of Information Education, 18(4), 585-594. https://doi.org/10.14352/jkaie.2014.18.4.585
  30. Meng-Leong How, Wei Loong David Hung (2019). Educing AI-Thinking in Science, Technology, Engineering, Arts, and Mathematics (STEAM) Education. Education Sciences, 9(3), 184-225. https://doi.org/10.3390/educsci9030184
  31. Rad, Paul, Mehdi Roopaei, Nicole Beebe, Mehdi Shadaram, Yoris Au (2018). AI Thinking for Cloud Education Platform with Personalized Learning. In Proceedings of the 51st Hawaii International Conference on System Sciences.
  32. Neller, Todd W. (2017). AI education: Machine learning resources. AI Matters, 3(2), 12-15. https://doi.org/10.1145/3054837.3054841

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