推动中小学数学公平，助力信号处理与机器学习人才早期培养
Democratizing Signal Processing and Machine Learning: Math Learning Equity for Elementary and Middle School Students
- 通过大学支持的课外数学项目补强学生基础算术能力
- 实证显示项目可帮助学生在七年级前掌握代数核心知识
- 适合关注教育公平、高校社会责任与早期人才培养的读者
信号处理（SP）和机器学习（ML）依赖扎实的数学与编程基础，尤其是线性代数、概率、三角函数和复数等知识。这些知识又建立在小学阶段掌握的标量代数之上。而标量代数的掌握，取决于良好的基础算术能力。由于系统性障碍，许多学生在小学阶段无法建立坚实的算术基础，导致后续代数及更复杂内容学习困难。因数学学习具有累积性，这种差距随学年递增，难以在大学弥补。本文探讨了SP领域师生与专业人士如何参与或发起大学主导的校外数学支持项目，以补充学生学习。作者介绍了两个实际案例：爱荷华州立大学的CyMath项目和普渡大学的Algebra by 7th Grade（Ab7G）项目，以及精算基金会的支持计划。最后提出几项零成本建议，供公立学校采纳，可惠及远超校外项目覆盖范围的学生。
原文摘要 · Abstract (English)
Signal Processing (SP) and Machine Learning (ML) rely on good math and coding knowledge, in particular, linear algebra, probability, trigonometry, and complex numbers. A good grasp of these relies on scalar algebra learned in middle school. The ability to understand and use scalar algebra well, in turn, relies on a good foundation in basic arithmetic. Because of various systemic barriers, many students are not able to build a strong foundation in arithmetic in elementary school. This leads them to struggle with algebra and everything after that. Since math learning is cumulative, the gap between those without a strong early foundation and everyone else keeps increasing over the school years and becomes difficult to fill in college. In this article we discuss how SP faculty, students, and professionals can play an important role in starting, and participating in, university-run, or other, out-of-school math support programs to supplement students' learning. Two example programs run by the authors, CyMath at Iowa State and Algebra by 7th Grade (Ab7G) at Purdue, and one run by the Actuarial Foundation, are described. We conclude with providing some simple zero-cost suggestions for public schools that, if adopted, could benefit a much larger number of students than what out-of-school programs can reach.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。