Collaboration in the Mathematical Sciences Community on Mathematical Modeling Across the Curriculum

12 As representatives of the Society for Industrial and Applied Mathematics (SIAM) who are engaged in education outreach, we are working to bring together the mathematical sciences communities to help facilitate mathematical modeling in the K–12 arena. This effort embraces all aspects of modeling: mathematical, statistical, computational, and data-based, as well as science-/physics-based. Indeed, for most interesting applications, these aspects interweave and no one sphere of expertise will have all the answers. We will use the term “modeling” as an umbrella for all of these. The emergence of data science—and data-enabled science—as a major area of research and study makes this blending of skills and understanding from the entire mathematical sciences spectrum critically important. When we say “mathematical sciences,” we include statistics, operations research, data science, and algorithmic approaches—pure, industrial, and applied mathematics. Certainly, modeling will be a key element of educational programs that prepare students for work in data-rich environments. Arguably, modeling is key to moving from data to information and making good decisions. Another major motivation for this article is the fact that applied mathematics and statistics share much of the raison d’être for K–12 education in the mathematical sciences in the first place. (See also the Usiskin article, “The Relationships Between Statistics and Other collaboration in the mathematical sciences community on mathematical modeling Across the curriculum