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[ Article ] | |
Journal of Educational Research in MathematicsVol. 31, No. 1, pp.109-130 | |
Abbreviation: JERM | |
ISSN: 2288-7733 (Print) 2288-8357 (Online) | |
Print publication date 28 Feb 2021 | |
Received 10 Jan 2021 Revised 03 Feb 2021 Accepted 05 Feb 2021 | |
DOI: https://doi.org/10.29275/jerm.2021.02.31.1.109 | |
Coding Environment and Exploration Curriculum for Max-Min Optimizations with an Evolution Strategy | |
*Graduate Student, Seoul National University, South Korea (starlit2148@snu.ac.kr) | |
**Teacher, Goyang Global High School, South Korea (iamisook20765@snu.ac.kr) | |
***Professor, Seoul National University, South Korea (hancho@snu.ac.kr) | |
Correspondence to : †Professor, Seoul National University, South Korea, hancho@snu.ac.kr | |
The purpose of this study is to develop a curriculum of school mathematics and artificial intelligence regarding max-min problems. We also developed a coding environment for a designed curriculum that can be accessed and utilized at codingmath.org without any additional installation. The curriculum uses an evolution strategy-based gradient descent method founded on a school probability and statistics curriculum with an experiment using dice; this curriculum is designed to work when the function is not differentiable and is ultimately linked to a calculus-based gradient descent method. The coding environment, in which even middle school students can visualize the min-max problem of f(x, y), uses a three-dimensional function graph with minimal coding inputs and explores the gradient descent process using a two-dimensional Python animation. This study, which is an attempt to combine mathematical problem solving and computational thinking in the context of max-min problems, further discusses ideas regarding the ways to integrate AI and coding in mathematics education.
Keywords: artificial intelligence(AI), evolution strategy, gradient descent, Python coding, 3D visualization, storytelling, maximum-minimum problem |
1. | Ackermann, E. (2001). Piaget’s constructivism, papert’s constructionism: what’s the difference. Future of learning group publication, 5(3), 438. |
2. | Cho. H., H. (2003). Computer and mathematics education. The Journal of Educational Research in Mathematics, 42(2), 177-191. |
3. | Cho, H., H. & Song, M., H. (2014). On the smart storytelling mathematics education based on executable expression. The Journal of Educational Research in Mathematics, 24(2), 269-283. |
4. | Choi, I., Y. (2020). A study on the pattern generalization process through the figural pattern learning environment - focusing on eye-tracking analysis and a microworld -. Doctoral dissertation, Seoul National University. |
5. | Choi, J., S., Lee, K., H., & Kim, S., R. (2010). Exploring a hypothetical learning trajectory of linear programming by the didactical analysis. The Journal of Educational Research in Mathematics, 20(1), 85-102. |
6. | Chung, I., W., & Cho, H., H. (2020), Fostering mathematical creativity through the various mathematical expressions in the 3D coordinate system based coding environment : Focusing on designing coding tasks and analyzing code expressions. School Mathematics, 22(1), 161-181.![]() |
7. | Chung, I., W. (2020). A study on mathematical design activities using 3D coordinate system- based coding environment: Focusing on mathematical learning by different code expressions. Master’s Thesis, Seoul National University. |
8. | Clements, D., H., & Sarama, J. (2002). The role of technology in early childhood learning. Teaching children mathematics, 8(6), 340-343.![]() |
9. | Frank, S., Christian, O., Thomas, R., Alex, G., Jan, P., & Jürgen, S. (2010). Parameter- exploring policy gradients. Neural Networks, 23(4), 551-559.![]() |
10. | Healy, L., & Kynigos, C. (2010). Charting the microworld territory over time: design and construction in mathematics education. ZDM, 42(1), 63-76.![]() |
11. | Heo, N., G. (2020). Analysis of secondary mathematics knowledge for AI learning through the AI related R&E program. Journal of Learner- Centered Curriculum and Instruction, 20(16), 673-689.![]() |
12. | Hoyles, C., Noss, R., & Adamson, R. (2002). Rethinking the microworld idea. Journal of educational computing research, 27(1), 29-53.![]() |
13. | Hwang, H., S. (2002). Evolution calculation and evolution design, 61~73. Seoul: NAEHA corp. |
14. | Jeong, H., R., Lee, S., J., & Cho, H., H. (2016). Educational application of turtle representation system for linking cube mathematics class. School Mathematics, 18(2), 323-348. |
15. | Jeong, J., H., & Cho, H., H. (2020). Mathematising of coding education command: focusing on algebra education. Journal of Educational Research in Mathematics, 30(1), 131-151.![]() |
16. | Kim, M., J. (2004). A mathematics educational study on dynamic geometry system(DGS) - focused on the viewpoint of microworld -. Master’s Thesis, Seoul National University. |
17. | Kim, N., R., Seo, Y., H., & Cho, H., H. (2018). Coding mathematics contents and environment design - focusing on mathematization and computational thinking -. Journal of Learner- Centered Curriculum and Instruction, 18(4), 647-673.![]() |
18. | Ko, H., K. (2020). A study on development of school mathematics contents for artificial intelligence(AI) capability. Journal of the Korean School Mathematics Society, 23(2), 223-237. |
19. | Lee, C., H. (2016). The effect of learning 3D turtle expression-based coding on spatial ability. Master’s Thesis, Seoul National University. |
20. | Lee, E., K. (2020). A comparative analysis of contents related to artificial intelligence in national and international K-12 curriculum. Journal of Korean Association of Computer Education, 23(1), 37-44. |
21. | Lee, J., H., & Huh, N. (2018). A study on the relationship between artificial intelligence and change in mathematics education. Communications of Mathematical Education, 32(1), 23-36. |
22. | Lee, S., G., Lee, J., H., & Ham, Y., M. (2020). Artificial intelligence and college mathematics education. Communications of Mathematical Education, 34(1), 1-15.![]() |
23. | Lee, S., H., Lee, J., H., & Kim, W., K. (2012). The effects of using geogebra on the mathematical thinking in the optimization problems of regional inequalities - focus on level curve -. Korean Journal of Teacher Education, 28(4), 1-44.![]() |
24. | Ministry of Education (2020a). A comprehensive plan for mathematics education that grows together with thinking power and leads the future[2020~2024]. |
25. | Ministry of Education (2020b). AI, into school! Introduced as artificial intelligence (AI), elementary mathematics study assistant, as a high school career optional course. Press release of MOE (2020-09-14) |
26. | Ministry of Science and ICT(2018). In the age of artificial intelligence, what is our R&D strategy?. Press release of Ministry of Science and ICT(2018-06). |
27. | Papert, S. (1972). Teaching children to be mathematicians versus teaching about mathematics. International journal of mathematical education in science and technology, 3(3), 249-262.![]() |
28. | Papert, S. (1980). Mindstorms: Children, computers, and powerful ideas. Basic Books, Inc.. |
29. | Papert, S. & Harel, I. (1991). Situating constructionism. Constructionism, 36(2), 1-11. |
30. | Park, M., G. (2020). The trends of using artificial intelligence in mathematics education. Journal of Korea Elementary Education, 31(supplement), 91-102. |
31. | Rechenberg, I., & Eigen, M. (1973). Evolutionsstrategie: optimierung technischer systeme nach prinzipien der biologischen evolution. Frommann- Holzboog Stuttgart. |
32. | Rosenbrock, H., H. (1960). An automatic method for finding the greatest or least value of a function. The Computer Journal, 3(3), 175-184.![]() |
33. | Salimans, T., Ho, J., Chen, X., & Sutskever, I. (2017). Evolution Strategies as a Scalable Alternative to Reinforcement Learning. ArXiv e-prints. |
34. | Sehnke, F., Osendorfer, C., Rückstieß, T., Graves, A., Peters, J., & Schmidhuber, J. (2010). Parameter-exploring policy gradients. Neural Networks, 23(4), 551-559.![]() |
35. | Schwefel, H., P. (1977). Numerische optimierung von computer-modellen mittels der evolutionsstrategie.![]() |
36. | Shin, D., J. (2020). Artificial intelligence in primary secondary education: A systematic Review. Journal of Educational Research in Mathematics , 30(3), 531~552.![]() |
37. | Shin, S., K. (2019). Designing the Instructional Framework and Cognitive Learning Environment for Artificial Intelligence Education through Computational Thinking. Journal of The Korean Association of Information Education, 23(6), 639-653.![]() |
38. | 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.![]() |