We argue that fixing the early math skills of school students is critical for maximizing the likelihood of their future success in signal processing, or in fact, in any STEM profession, if they choose to pursue it. The reason is that math learning is cumulative (or causal in Signal Processing language): Signal Processing (SP) relies on good linear algebra and statistics, which rely on good basic scalar algebra, which cannot be understood without a strong grasp of basic arithmetic (fractions, decimals, ratios).
We discuss three ways in which Signal Processing (or any STEM) educators and professionals can help:
- Tutoring and encouraging math practice at home (use of one of many existing AI-enabled math learning applications can make this task easier and more reliable);
- Raising awareness of the need/resources for building good early math skills via social or news media;
- Advocating for better K-12 math teaching at local schools.
In the US context, we argue that many current US education policies are based on short-term research and should be critically re-examined from a long-term college STEM student success perspective.
This talk is based on Vaswani’s 20 years of experience teaching and researching signal processing and five years of experience directing a K-12 math tutoring program called CyMath at Iowa State University, which has had enormous success with tutoring initiatives while being extremely low-cost and easy to run. A novel aspect of CyMath is that it partially relies on international and domestic SP or other STEM graduate students and faculty volunteering an hour a week to tutor young students, while also improving their own communication and teaching skills and well-being.
