융합정보논문지 (Journal of Convergence for Information Technology)

중소기업융합학회 (Convergence Society for SMB)
권호 표지
DOI QR코드

DOI QR Code

파충류와 양서류 분류를 위한 인공지능 교육 기반의 융합 교육 프로그램 개발

Development of Artificial Intelligence Education based Convergence Education Program for Classifying of Reptiles and Amphibians

  • 이소율 (한국교원대학교) ;
  • 이영준 (한국교원대학교 컴퓨터교육과)
  • Yi, Soyul (Korea National University of Education) ;
  • Lee, YoungJun (Dept. of Computer Education, Korea National University of Education)
  • 투고 : 2021.11.09
  • 심사 : 2021.12.10
  • 발행 : 2021.12.28
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

본 연구에서는 인공지능 교육을 활용하여 생물 교육의 파충류와 양서류를 분류에 대한 이해를 높이고, AI(Artificial Intelligence) 역량을 증대할 수 있도록 탈학문적(Transdisciplinary) 융합 교육 프로그램을 개발하였다. 중심 내용으로는 생물교육에서 오랫동안 다루어진 주제인 파충류와 양서류의 분류를 의사결정 트리 및 ML4K(Machine Learnig for Kids)를 활용하여 해결하는 것으로, 총 3차시 분량으로 설계하였다. 개발된 교육 프로그램에 대하여 전문가 검토를 실시하였고, 그 결과 I-CVI 값이 .88~1.00을 나타내어 내용 타당도를 확보하였다. 이 교육 프로그램은 학습자들에게 정보 교육의 인공지능에 관한 학습 내용과 생물 교육의 척추 동물의 분류에 관한 학습 내용에 대해 동시에 학습할 수 있다는 강점이 있다. 또한, 인공지능 활용 부분에서는 인지 부하를 최소로 하도록 구성되어 있기 때문에 모든 교사들이 쉽게 활용할 수 있다는 점이 특징이다.

In this study, a transdisciplinary convergence education program was developed to enhance the understanding for classification of reptiles and amphibians in biology education and also to increase AI (Artificial Intelligence) capability by using artificial intelligence education. The main content is to solve the classification of reptiles and amphibians that has been dealt with for a long time in biology education, using a decision tree and ML4K (Machine Learnig for Kids), it was designed for a total of 3 lessons. Experts review was conducted on the developed education program, as a result, the I-CVI(Item Content Validity Index) value was .88~1.00 so that can secure content validity. This education program has the advantage of being able to simultaneously learn about the learning contents of artificial intelligence in informatics and the classification of vertebrates in the biological education. In addition, since it is configured to minimize the cognitive load in the AI using part, it is characterized by the fact that all of any teachers can apply it their lesson easily.

키워드

참고문헌

  1. K. Schwab. (2016). The fourth industrial revolution. Geneva: World Economic Forum.
  2. Ministry of Education. (2020.11.10). Education Policy Direction and Core Tasks in the Age of Artificial Intelligence. (Online). https://www.korea.kr/archive/expDocView.do?docId=39237
  3. S. Russell & P. Norvig. (2021). Artificial Intelligence: A Modern Approach. Pearson, NJ.
  4. R. Socher, B. Huval, B. Bath, C. D. Manning & A, Ng. (2012). Convolutional-recursive deep learning for 3d object classification. Advances in neural information processing systems, 25, 656-664.
  5. Naver Knowledge Encyclopedia. (2021). AlphaGo vs. Lee Sedol. Current common sense dictionary(Online). https://terms.naver.com/entry.naver?docId=3408537&cid=43667&categoryId=43667
  6. S. Y. Yi, S. W. Kim & Y.J Lee. (2021). Development of Teaching Efficacy Belief Instrument about Artificial Intelligence for Pre-service Teachers. The Journal of The Korea Association of Computer Education, 24(1), 47-61. https://doi.org/10.32431/KACE.2021.24.1.006
  7. M. J. Sohn, J. S. Kim, S. G. Jeon, J. H. Park, J. S. Kim, Y. J. Heo & I. S. Kim. (2020). Trend Analysis of Artificial Intelligence Education in China. Economic, Humanities and Social Research Society Chinese Comprehensive Research Collaborative Research Series, 20-91-03.
  8. H. S. Kim, Y. H. Seo, J. H. Song & J. Y. Lee. (2020). Direction of K-12 SW.AI Education through Computing Curricula 2020. Software Policy & Research Institute Issue Report, IS-110.
  9. AI4K12. (2021). AI4K12-Sparking Curiosity in AI. AI4K12(Online). http://AI4K12.org
  10. Ministry of Education. (2020.03.08). 2020 Ministry of Education Business Report (Online). https : //www.moe.go.kr /boardCnt s /view.do?boardID=346&lev=0&boardSeq=79918
  11. Relevant Ministries Joint. (2020.08). A plan to spread AI.SW education to the whole people in Korea. Policy Information(Online). https://policy.nl.go.kr/search/searchDetail.do?rec_key=SH2_PLC20200254423
  12. Ministry of Education. (2020.09.11). Partial revision of the elementary and secondary education curriculum and special school curriculum (draft). Ministry of Education Notice No. 2020 - 294.
  13. H. W. Kim, K. R. Noh, S. J. Ahn & O. J. Kwon. (2021). Technology Convergence Map Creation and Country Profile Analysis in the Field of Artificial Intelligence. The Journal of the Korea institute of electronic communication sciences, 12(1), 139-146. DOI : 10.13067/JKIECS.2017.12.1.139
  14. E. K. Lee. (2020). A Comparative Analysis of Contents Related to Artificial Intelligence in National and International K-12 Curriculum. The Journal of The Korea Association of Computer Education, 23(1), 37-44. DOI : 10.32431/kace.2020.23.1.003
  15. B. Y. Park & H. N. Lee. (2014). Development and Application of Systems Thinking-based STEAM Education Program to Improve Secondary Science Gifted and Talented Students' Systems Thinking Skill. Journal of Gifted/Talented Education, 24(3), 421-444. DOI : 10.9722/JGTE.2014.24.3.421
  16. G. Yakman & J. Kim. (2007). STE@M: Integration aspects of go into the core curriculum. American Go Association, Retrieved from: http://www.usgo.org//teach/method.html
  17. S. M. Drake & R. C. Burns. (2004). Meeting standards through integrated curriculum. ASCD.
  18. W. S. Song. (2020). Devleopment of SW education class plan using artificial intelligence education platform : focusing on upper grade of elementary school. Journal of The Korean Association of Information Education, 24(5), 453-462. DOI : 10.14352/jkaie.2020.24.5.453
  19. Y. H. Lee. (2021). Development and effectiveness analysis of artificial intelligence STEAM education program. Journal of The Korean Association of Information Education, 25(1), 71-79. DOI : 10.14352/jkaie.2021.25.1.71
  20. Google AI Experiment. (2021). Information of Teachable Machine. Teachable Machine(Online). https://teachablemachine.withgoogle.com/
  21. Naver Connect Foundation. (2021). Entry - We can be everything. Entry(Online). http://playentry.org
  22. Dale Lane. (2021). Introduction of Machine Learning for Kids. Machine Learning for Kids(Online). https://machinelearningforkids.co.uk/
  23. J. Y. Park. (2004). Understanding Students' Conceptions in the Research on Conceptual Change in Science: from Misconception to Mental Model. Journal of the Korean Association for Science Education, 24(3). 621-637.
  24. E. M. Joe, S. I. Kim, J. S. Jeong & Y. J. Kwon. (2005). Development of the Thinking Process Model for the Generation of Biological Phylogenetic-Tree. Biology Education, 33(1), 13-22.
  25. W. H. Jeong, M. Heo & H. Y. Cha (1991). Animal Classification Concepts of Korean Elementary and Secondary Students. Biology Education, 19(2), 95-114.
  26. W. H. Gim, U. W. Hwang & Y. J. Kim. (2012). Analyses of Middle School Students' Thoughts Causing Common Mistakes on Animal Classification. Journal of Science Education, 36(1), 153-165. https://doi.org/10.21796/jse.2012.36.1.153
  27. D. R. Park, J. M. Ahn, J. H. Jang, W. J. Yoo, W. Y. Kim, Y. G. Bae & I. W. Yoo. (2020). The Development of Software Teaching-Learning Model based on Machine Learning Platform. Journal of The Korean Association of Information Education, 24(1), 49-57. DOI : 10.14352/jkaie.2020.24.1.49
  28. W. S. Shin. (2020). Exploring the Possibility of AI Convergence Science Education in Motion and Energy. Journal of The Korea Society of Energy and Climate Change Education, 10(1), 73-86.
  29. T. J. Seong. (2019). Understanding and Application of Modern Basic Statistics. Hakjisa, Pajoo.