Mathematics and Statistics Across the Curriculum: Empowering Non-STEM Students to Appreciate and Use Quantitative Modeling

Texas Lutheran University, Seguin TX

Investigators

Abstract

With support from the NSF Improving Undergraduate STEM Education Program: Education and Human Resources, this Engaged Student Learning (Exploration and Design) project aims to serve the national interest in undergraduate mathematics education. It aims to do so by improving attitudes, awareness, and use of quantitative modeling by undergraduate students in STEM and non-STEM majors. This project will investigate how students outside of math-intensive majors respond to mathematical and statistical modules that are embedded within their courses and that focus on topics that are relevant to the course. The project design team, consisting of mathematics faculty paired with faculty from participating disciplines, will design technology-based modeling modules to incorporate in undergraduate non-mathematics courses. To ensure authenticity, modules from non-STEM disciplines will be selected by participating non-STEM faculty members and students. Enabling student experiences that address relevant problems with quantitative methods will have the potential to create awareness, appreciation, and use of these methods. As such, this project has potential to be a gateway for non-mathematics undergraduate students into quantitative applications in disparate areas of undergraduate study. The goals of this three-year project include to: (i) improve the attitudes of faculty and students toward use of quantitative methods in non-mathematics classes; (ii) increase use of modeling outside of mathematics courses; and (iii) develop a dynamic online archive of peer-reviewed modeling modules. The project will study the incorporation of quantitative literacy applications in a range of courses with non-mathematics undergraduate majors, including social and applied sciences, arts, humanities, and biology undergraduate courses. The research design of the project will include comparing class sections that do not incorporate quantitative modeling modules with those that do, using pre- and post-tests of students in these classes, who serve as their own controls. Data analysis will include use of repeated measures MANOVA, MANCOVA, regression, and non-parametric analyses. The project has the potential to support the broader national use of statistical and quantitative methods outside of STEM disciplines and enhance undergraduate students' critical thinking skills as well-prepared citizens. The NSF IUSE: EHR Program supports research and development projects to improve the effectiveness of STEM education for all students. Through the Engaged Student Learning track, the program supports the creation, exploration, and implementation of promising practices and tools. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.