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Democratizing Signal Processing and Machine Learning: Math Learning Equity for Elementary and Middle School Students

Namrata Vaswani, Mohamed Y. Selim, Renee Serrell Gibert

TL;DR

The paper addresses inequitable early math preparation as a barrier to SP/ML readiness, arguing that mastering scalar algebra and linear-system intuition is foundational for later techniques such as $\\mathbf{A}\\mathbf{x}=\\mathbf{b}$ with $\\hat{\\mathbf{x}}=\\mathbf{A}^{\\dagger}\\mathbf{b}$ in least-squares settings. It combines theoretical framing with concrete out-of-school interventions, describing three university-led programs—CyMath, Algebra by Seventh Grade (Ab7G), and Math Motivators—and discusses their structures, practices, and early outcomes. The contributions include detailed program designs, cross-institutional collaboration insights, and scalable, low-cost recommendations for public schools to broaden access to SP/ML skills. Together, these elements offer a practical pathway to democratize SP/ML by strengthening foundational math and leveraging community partnerships.

Abstract

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.

Democratizing Signal Processing and Machine Learning: Math Learning Equity for Elementary and Middle School Students

TL;DR

The paper addresses inequitable early math preparation as a barrier to SP/ML readiness, arguing that mastering scalar algebra and linear-system intuition is foundational for later techniques such as with in least-squares settings. It combines theoretical framing with concrete out-of-school interventions, describing three university-led programs—CyMath, Algebra by Seventh Grade (Ab7G), and Math Motivators—and discusses their structures, practices, and early outcomes. The contributions include detailed program designs, cross-institutional collaboration insights, and scalable, low-cost recommendations for public schools to broaden access to SP/ML skills. Together, these elements offer a practical pathway to democratize SP/ML by strengthening foundational math and leveraging community partnerships.

Abstract

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.
Paper Structure (7 sections, 1 figure)

This paper contains 7 sections, 1 figure.

Table of Contents

  1. Why Early Math Education matters for Signal Processing (SP) and Machine Learning (ML)
  2. Systemic Barriers
  3. A Partial Solution: Out-of-school Math Tutoring or Mentoring Programs
  4. CyMath: run by Electrical and Computer Engineering faculty at Iowa State University
  5. Algebra by 7th Grade (Ab7G): run by Purdue University School of Engineering
  6. Math Motivators: run by the Actuarial Foundation
  7. Lessons learned and Conclusions

Figures (1)

  • Figure 1: Figure indicating that math learning is cumulative. This is taken from https://www.educationnext.org/grade-level-expectations-trap-how-lockstep-math-lessons-leave-students-behind/ which uses data from dougherty2012getting.