Needed Math Project: September 2022 Newsletter

NEEDED MATH PROJECT

It's All about Math Scenarios, but ….

How will we know what mathematics today’s technicians use on the job? Why not just ask them? But can we craft questions that are likely to elicit the math concepts that underpin and inform what technicians do? That’s the problem we have been wrestling with. We have been working hard to develop a survey that will illuminate the math that’s needed in modern manufacturing jobs by linking it to the tasks that make up those jobs. The results, we hope, will help math and technical faculty at community colleges to teach math in a more realistic setting while also motivating the students who ask, “When am I ever going to use this?”

The 21st Century workplace calls for technicians who understand the math that lurks within the complex, often highly automated processes they are called to supervise. It is not enough for them to use a spreadsheet as a static look-up table, they must be conscious of the relationships between variables embodied in, but hidden by, the technology—a central feature of high school algebra that is easily overshadowed by the quest to “solve for x.” A curve of best fit involves more than “connecting the dots” and its relevance to completing a job or troubleshooting a process entails an understanding of mathematics.

After analyzing results from our survey we plan to develop a series of math-rich scenarios that we hope will spark a conversation about the math that’s needed and how to teach it. But before we get there, we welcome feedback from each of you. If you would like to respond to this article, please send a message to Lois.M.Miceli@hofstra.edu.

In addition, if you would like to participate in the pilot test or survey or know someone who would, please send an email to: Lois.M.Miceli@hofstra.edu.

September 2022

A Huge Thank-You to all Thinkaloud participants: Michael, Shera, Luis, Mark, Dianne, Eric, Amy, Ric, Sujithan, Wei-Kai, Marci, Natalia, Al, and Douglas!

Also, Thank-You to all the participating companies and universities: Monin, GenMet, Univ. of New Mexico, Hocking Community College, Central Community College (Nebraska), Central Virginia Community College

Neogen Corporation Site Visit A special thanks to Jerome, Mari-Sue, Chris, and Allison, for taking the time out from their busy days to host the site visit. We learned so much!

In August 2022, we had a virtual visit at Neogen Corporation in Lansing, Michigan. This plant provides test kits and relevant products to detect dangerous substances in food. The areas we toured were Quality Control, Chemistry Lab, and Packaging. The Quality Control area tests kits and determines the final release of these kits. They test for allergenic and mycotoxin residues as well as genetically modified organisms. Within the application suites, the machine operators were using lateral flow technology to develop kits. Nano droplets of fluid were dispensed out of a solenoid and sprayed onto a web. Operators monitor the quality of the spray using vision cameras.

During lamination, components are laminated onto an adhesive card and wicking pad in a controlled humidity and temperature environment. A cutting machine cuts the reel into 4.1 mm wide strips, accumulates them into a tube that will be ready for packaging and assembly.

Some Needed Math: In cutting and tubing, testing is done on one strip for every ten (sampling). This will be assessed for reproducibility through CV percentages that get entered into a chart.

Technicians look for any shift in the mean that might occur to determine if further assessment is needed. In addition, when testing quantitative lateral flows technicians have to set curves and do regression using Excel templates.

Project Director Contact Information: Michael Hacker, Ph.D. Michael.Hacker@hofstra.edu 518 915-1411 https://www.neededmath.org/

A Big Shout Out to Nicole, Ayin, Brooke, Tom, Virginia, Holley, Madison, Ted, Dan, and Courtney for helping us become more knowledgeable about Neogen’s production!

This material is based upon work supported by the National Science Foundation under grant # 2100062. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.