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Analysis of Research Trends in Elementary Information Education in Korea using Topic Modeling

토픽 모델링을 활용한 국내 초등 정보교육 연구동향 분석

  • 심재권 (고려대학교 영재교육원)
  • Received : 2021.01.18
  • Accepted : 2021.01.30
  • Published : 2021.04.30

Abstract

As interest in artificial intelligence education for elementary school students has recently increased, it is necessary to analyze the existing elementary information education research from a macroscopic point of view to understand the current situation and to provide implications for subsequent research. This study analyzed Journal of The Korean Association of Information Education for the purpose of looking at the research trend of elementary information education in Korea. For the data of the study, all papers published until 2020 in the first issue of the journal were selected, and 11 research topics were derived by modeling topics. As a result of the study, topic T1, the highest proportion, was analyzed to account for about 38%, and keywords such as education, research, analysis, elementary school, and information were derived according to the order of contribution to topic T1. As a result of regression analysis according to the year of the topic, it was found that the research trend is changing to computing thinking, software education, and artificial intelligence education. The significance of this study is that text data related to elementary information education is objectively clustered.

초등학생을 대상으로 인공지능교육에 대한 관심이 증대되면서 기존에 수행된 초등 정보교육 연구를 거시적인 관점에서 분석하여 현재의 상황을 이해하고 후속연구의 시사점을 제공하기 위한 노력이 필요한 시점이라 할 수 있다. 본 연구는 우리나라 초등 정보교육의 연구동향을 조망하고자 하는 목적으로 정보교육학회논문지를 분석하였다. 분석을 위한 데이터는 정보교육학회논문지의 창간호에서 2020년까지 출간된 논문을 모두 선정하였고, 토픽모델링하여 연구주제 11개를 도출하였다. 연구결과, 가장 높은 비중인 토픽 T1은 약 38%을 차지하는 것으로 분석되었고, 토픽 T1에 기여도 순에 따라 교육, 연구, 분석, 초등, 정보의 키워드가 도출었다. 토픽들의 연도별 회귀분석 결과, 연구의 트랜드가 컴퓨팅사고력, 소프트웨어교육, 인공지능교육 등으로 변화하고 있는 것으로 나타났다. 본 연구의 의의는 초등 정보교육과 관련된 텍스트 데이터를 객관적으로 클러스터링하였다는 점에서 의미가 있다고 할 수 있다.

Keywords

Acknowledgement

이 논문은 2020년도 정부(교육부)의 재원으로 한국연구재단의 지원을 받아 수행된 기초연구사업임(NRF-2020R1I1A1A01058353).

References

  1. D. Newman, J. H. Lau, K. Grieser, and T.Baldwin. (2010). Automatic Evaluation of Topic Coherence. Human Language Technologies. The 2010 Annual Conference of the North American Chapter of the ACL, 100-108.
  2. David M. Blei, Andrew Y. Ng, Michael I. Jordan. (2003). Latent Dirichlet Allocation. Journal of Machine Learning Research, 3, 993-1022.
  3. Jonathan Chang, Jordan Boyd-Graber, Sean Gerrish, Chong Wang, David M. Blei. (2009). Reading Tea Leaves: How Humans Interpret Topic Models. Advances in Neural Information P rocessing Systems, 22, 288-296.
  4. Kim, K.M., Kim, S.S., and Kim, S.S.(2014). Development of Assessment Tool about Professionalism of the Information Ethics Teachers. Journal of The Korean Association of information Education, 18(1), 1-11. https://doi.org/10.14352/jkaie.2014.18.1.1
  5. Kim, S.Y.(2020). Analysis of Research Trends in Journal of the Korean Society for Industrial and Applied Mathematics Using Topic Modeling and Implications for Industrial Mathematics Education. Secondary Education Research, 68(2), 267-293.
  6. Kim, T.K., Choi, H.R., and Lee H.C.(2016). A Study on the Research Trends in Fintech using Topic Modeling. Journal of the Korea Academia-Industrial cooperation Society, 17(11), 670-681. https://doi.org/10.5762/KAIS.2016.17.11.670
  7. Lee, C.H., Kim, U.J.(2020). A Research Trend Analysis of Computer Education Using Topic Modeling. The Journal of Korean Association of Computer Education, 23(6), 15-23. https://doi.org/10.32431/KACE.2020.23.6.002
  8. Ma, D.S et al(2008). A Study on the Computer Application Ability Gap of the Elementary Student. Journal of The Korean Association of information Education, 12(2), 163.171.
  9. Ministry of Government(2020). Education P olicy Direction and Core Tasks in the Age of Artificial Intelligence.
  10. Moon, W.S.(2017). A Study on the Trend of papers published by Korean Association of Information Education. Journal of The Korean Association of information Education, 22(6), 681-687. https://doi.org/10.14352/jkaie.2018.22.6.681
  11. Park, S.J.(2017). Analysis of Information Education Related Theses Using R Program. Journal of The Korean Association of information Education, 21(1), 57-66. https://doi.org/10.14352/jkaie.21.1.57
  12. Shin, M.S., Cho, K.W.(2019). Analysis on Topic Modeling and Trend of Journal of Speech-Language & Hearing Disorders using Text Mining: (2002-2018). Journal of speech-language & hearing disorders, 28(3), 81-91. https://doi.org/10.15724/jslhd.2019.28.3.081
  13. Shin. D.J.(2020). A comparative study of domestic and international research trends of mathematics education through topic modeling. The Mathematical Education, 59(1), 63-80. https://doi.org/10.7468/MATHEDU.2020.59.1.63
  14. T. McNerney(2004). From turtles to tangible programming bricks: Explorations in physical language design. Personal and Ubiquitous Computing, 8(5), 326-337. https://doi.org/10.1007/s00779-004-0295-6
  15. Woo. C.W., Lee J.Y.(2020). Investigation of Research Topic and Trends of National ICT Research-Development Using the LDA Model. Journal of the Korea Convergence Society, 11(7), 9-18. https://doi.org/10.15207/JKCS.2020.11.7.009
  16. Yang, C.M(2014). Meta-Analysis on the Effects of Programming Education using Educational Programming Languages. Journal of The Korean Association of information Education, 18(2), 317-324. https://doi.org/10.14352/jkaie.2014.18.2.317
  17. Yon, B.N.(2020). A Topic Modeling Analysis on the Policy Issues of Meister High School. Journal of Vocational Education & Training, 23(1), 39-67. https://doi.org/10.36907/KRIVET.2020.23.1.39