The Korea Society Of Educational Studies In Mathematics

Current Issue

Journal of Educational Research in Mathematics - Vol. 31 , No. 1

[ 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
Kang, Hanbyeol* ; Lee, Misook** ; Cho, Hanhyuk***,
*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


Abstract

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

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