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
This paper emphasizes the importance of early math education in elementary and middle school for equitable access to signal processing and machine learning, advocating for out-of-school support programs to bridge foundational gaps.
Contribution
It highlights systemic barriers to math learning, discusses successful out-of-school programs, and offers low-cost suggestions for public schools to improve early math foundations.
Findings
Out-of-school programs can significantly support math learning.
Early math foundation is crucial for advanced STEM fields.
Simple, low-cost interventions can benefit many students.
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…
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Taxonomy
TopicsCognitive and developmental aspects of mathematical skills
