DOI QR코드

DOI QR Code

인공지능 사고 함양을 위한 인공지능 빅 아이디어 기반 초등학교 수학 융합 수업 사례연구

A case study of elementary school mathematics-integrated classes based on AI Big Ideas for fostering AI thinking

  • 투고 : 2024.03.19
  • 심사 : 2024.05.06
  • 발행 : 2024.05.31

초록

본 연구의 주요 목적은 인공지능 사고를 함양할 수 있는 수학 융합 수업을 설계하고 이를 적용함으로써 나타나는 초등학생들의 인공지능 사고를 분석하는 것이다. 이를 위해 미국의 AI4K12 Initiative가 개발한 인공지능 빅 아이디어의 학습목표(Learning Objective) 및 지속적 이해(Enduring Understanding)와 2015 개정 초등학교 수학과 교육과정 성취기준을 연계하여 인공지능 사고 함양을 위한 수학 융합 수업을 설계 및 실시하였다. 수학적 내용 수업 2개, 수학적 과정 수업 2개로, 수학적 내용 수업은 인공지능 빅 아이디어의 Perception-Processing, Learning-Nature of Learning과 연계하였으며 수학적 과정 수업은 Representation & Reasoning-Search, Representation & Reasoning-Reasoning과 연계하였다. 설계한 수업 중 Learning-Nature of Learning을 제외한 세 개의 수업을 대상 학년에 맞추어 K 초등학교 5학년 두 학급, 6학년 한 학급에 적용하였다. 수업 중 학생 담화 및 활동지, 수업 관찰 자료를 수집하였으며, 이를 컴퓨팅 사고 분류 체계를 기반으로 인공지능 사고 구성 요소를 추가하여 구성한 인공지능 사고 분석틀을 사용하여 분석하였다. 연구 결과, 인공지능 빅 아이디어가 인공지능 사고 함양을 위한 수학 융합 수업 설계 시 준거로서 기능할 수 있고 이를 통해 초등학생들에게도 인공지능 교육이 가능함을 확인할 수 있었다. 수학 융합 수업은 학생들의 다양한 인공지능 사고를 촉진할 수 있었는데, 구체적으로 수업 과정에서 데이터, 모델링과 시뮬레이션, 컴퓨팅 문제해결, 인공지능 사고 요소가 다양하게 나타난 것에 비해 시스템 사고 요소가 나타나는 빈도수는 상대적으로 적었다. 또한 입체도형 및 공간감각 등의 수학적 내용 요소와 수학 교과역량에 해당하는 수학적 과정 요소의 성취를 보여주었다. 요컨대 인공지능 빅 아이디어를 기반으로 한 수학 융합 수업은 초등학생들의 인공지능 개념 및 원리 이해와 수학적 내용 요소의 이해 및 과정 요소의 강화에 도움이 된다고 할 수 있다. 더욱이 학생들은 수업 중 기존 문제해결 방법의 구조적 일관성을 유지한 채 이를 새로운 문제해결로 확장하는 모습을 보여주었는데, 이러한 반응을 통해 인공지능 사고의 전이 가능성을 확인할 수 있었다. 본 연구 결과에 기초하여, 대상 학년과 빅 아이디어의 하위 요소를 확장함으로써 초등학생들의 다양한 인공지능 사고 요소를 함양하려는 수학 수업 설계를 통한 교수학적 노력 및 지속적인 연구가 필요하다.

This study aims to design mathematics-integrated classes that cultivate artificial intelligence (AI) thinking and to analyze students' AI thinking within these classes. To do this, four classes were designed through the integration of the AI4K12 Initiative's AI Big Ideas with the 2015 revised elementary mathematics curriculum. Implementation of three classes took place with 5th and 6th grade elementary school students. Leveraging the computational thinking taxonomy and the AI thinking components, a comprehensive framework for analyzing of AI thinking was established. Using this framework, analysis of students' AI thinking during these classes was conducted based on classroom discourse and supplementary worksheets. The results of the analysis were peer-reviewed by two researchers. The research findings affirm the potential of mathematics-integrated classes in nurturing students' AI thinking and underscore the viability of AI education for elementary school students. The classes, based on AI Big Ideas, facilitated elementary students' understanding of AI concepts and principles, enhanced their grasp of mathematical content elements, and reinforced mathematical process aspects. Furthermore, through activities that maintain structural consistency with previous problem-solving methods while applying them to new problems, the potential for the transfer of AI thinking was evidenced.

키워드

참고문헌

  1. AI4K12 Initiative (2020a, May 28). Big Idea 1 -Perception Progression Charts. AI4K12. https://ai4k12.org
  2. AI4K12 Initiative (2020b, November 19). Big Idea 3 -Learning Progression Charts. AI4K12. https://ai4k12.org
  3. AI4K12 Initiative (2021, June 28). Big Idea 2 -Representation & Reasoning Progression Charts. AI4K12. https://ai4k12.org
  4. Barr, V., & Stephenson, C. (2011). Bringing computational thinking to K-12: what is involved and what is the role of the computer science education community?. ACM Inroads, 2(1), 48-54. https://doi.org/10.1145/1929887.1929905
  5. Chang, H., & Nam, J. H. (2021). The use of artificial intelligence in elementary mathematics education -Focusing on the math class support system "Knock-knock! math expedition"-. The Journal of Korea Elementary Education, 31(Supplement), 105-123. https://doi.org/10.20972/kjee.31..202101.105
  6. Choe, H. J. (2021). Study of AI thinking education based on computational thinking. The Journal of Korean Association of Computer Education, 24(3), 57-65. https://doi.org/10.32431/kace.2021.24.3.006
  7. Choi, S. Y. (2022). Development of an instructional design model for elementary mathematics classes for elementary mathematics classes based on an artificial intelligence education system [Master's thesis, Seoul National University]. https://dcollection.snu.ac.kr/common/orgView/000000169790
  8. Choi, S. Y., & Chang, H. (2024). Development and application of artificial intelligence education program for mathematics convergence using robots. Education of Primary School Mathematics, 27(1), 19-38. https://doi.org/10.7468/jksmec.2024.27.1.19
  9. Computer Science Teachers Association (CSTA). (2017). CSTA K12 computer science standards, Revised 2017. CSTA. https://www.csteachers.org/standards
  10. Drake, S. M., & Burns, R. (2006). Integrated curriculum (Y. Park et al., Trans.). Wonmisa. (Original work published 2004).
  11. Gadanidis, G. (2017). Artificial intelligence, computational thinking, and mathematics education. The International Journal of Information and Learning Technology, 34(2), 133-139. https://doi.org/10.1108/IJILT-09-2016-0048
  12. Heintz, F. (2022). The computational thinking and artificial intelligence duality. In S. Kong & H. Aelson (Eds.), Computational thinking education in K-12 (pp. 143-151). The MIT Press. https://doi.org/10.7551/mitpress/13375.003.0012
  13. Jeong, G. (2023). Development of artificial intelligence convergence class based on computational thinking using technology tools in middle school mathematics subject [Master's thesis, Korea National University of Education].
  14. Jeong, S. G., & Park, M. G. (2023). Development of artificial intelligence mathematics convergence education program linked with elementary mathematics curriculum. Journal of Elementary Mathematics Education in Korea, 27(1), 87-108. https://doi.org/10.54340/kseme.2023.27.1.5
  15. Jung, W. S., Seo, S. H., & Hwang, J. H. (2023). CHOINSU-elementary artificial intelligence. Jigumoonwha.
  16. Kim, H. N., & Jeon, Y. J. (2023). Development of artificial intelligence convergence education program of elementary mathematics data and possibilities area based on UMC model. In Proceedings of the Korean Association for Computer Education Winter Conference (pp. 311-314). The Korean Association for Computer Education.
  17. Ministry of Education (2020). Practical arts (technology and home economics) informatics curriculum. Ministry of Education.
  18. Papert, S. (1980). Mindstorms. Basic Books, Inc.
  19. Papert, S. (1996). An exploration in the space of mathematics education. International Journal of Computers for Mathematical Learning, 1(1), 95-123. https://doi.org/10.1007/BF00191473
  20. Park, M. G., Kim, Y. H., Choi, H. J., Jeong, H. W., & Park, S. S. (2022). Learning AI through mathematics of textbooks. Junior Kimyoungsa.
  21. Rad, P., Roopaei, M., Beebe, N., Shadaram, M., & Au, Y. (2018). AI thinking for cloud education platform with personalized learning. In Proceedings of the 51st Hawaii International Conference on System Sciences (pp. 3-12). HICSS. https://doi.org/10.24251/HICSS.2018.003
  22. Sung, J. (2023). Analysis of functions and applications of intelligent tutoring system for personalized adaptive learning in mathematics. The Mathematical Education, 62(3), 303-326. https://doi.org/10.7468/mathedu.2023.62.3.303
  23. Touretzky, D., Gardner-McCune, C., Breazeal, C., Martin, F., & Seehorn, D. (2019a). A year in K-12 AI education. AI Magazine, 40(4), 88-90. https://doi.org/10.1609/aimag.v40i4.5289
  24. Touretzky, D., Gardner-McCune, C., Martin, F., & Seehorn, D. (2019b). Envisioning AI for K-12: What should every child know about AI? Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence (AAAI-19), 33(01), 9795-9799. https://doi.org/10.1609/aaai.v33i01.33019795
  25. Weintrop, D., Beheshti, E., Horn, M., Orton, K., Jona, K., Trouille, L., & Wilensky, U. (2016). Defining computational thinking for mathematics and science classrooms. Journal of Science Education and Technology, 25(1), 127-147. https://doi.org/10.1007/s10956-015-9581-5
  26. Wing, J. (2006). Computational thinking. Communications of the ACM, 49(3), 33-35. https://doi.org/10.1145/1118178.1118215
  27. Yadav, A., Mayfield, C., Zhou, N., Hambrusch, S., & Korb, J. T. (2014). Computational thinking in elementary and secondary teacher education. ACM Transactions on Computing Education, 14(1), 1-16. https://doi.org/10.1145/2576872
  28. Zeng, D. (2013). From computational thinking to AI thinking. IEEE Intelligent Systems, 28(6), 2-4. https://doi.org/10.1109/mis.2013.141
  29. Zerega, R., & Milrad, M. (2023, August 21-23). Computational thinking and AI: Two irreconcilable worlds? [Conference presentation abstract]. 2nd International Symposium on Digital Transformation. Linnaeus University, Vaxjo, Sweden. https://open.lnu.se/index.php/isdt/article/download/3780/3456