CoOrdinated Math-Physics Assessment as an Alternative Pathway in Early STEM

Abstract

Improving student success rates in introductory calculus and physics courses is critically important for our students’ path in STEM fields. Many of the students who have an intuitive understanding of physics fail calculus and are pushed to delay or drop their majors in technical fields. One way of addressing this issue is by adopting a program that is designed to identify students’ specific needs and provide direct assistance to help the students maximize their potential in STEM fields. At Clarkson University, a specific calculus section, COMPASS: CoOrdinated Math-Physics Assessment for Student Success, is designed to prepare students by introducing mathematical concepts using physical applications. The implementation of the COMPASS program at the institution for the academic years of 2015–2016 and 2016–2017 are described in this paper. This includes basic information on pre-tests, student identification tools, example lectures, and reports on the analysis of the students’ performance and assessment for two academic years. After 2 years of the COMPASS program, we recognize that students paths in STEM education need to be tailored to match their backgrounds and subjects of interest.

Keywords:

• Student success
• STEM
• calculus
• physics
• COMPASS

Additional information

Funding

This work is supported by grant NSF-DUE-1431882.

Notes on contributors

Guangming Yao

Guangming Yao is an associate professor in the mathematics department at Clarkson. She has an undergraduate degree and master’s degree in mathematics from Haerbin Normal University and a Ph.D. in computational mathematics from the University of Southern Mississippi. Her research concentrates on computational and applied mathematics, especially on radial basis functions methods for solving partial differential equations and mathematical modeling. She is passionate about mathematics education in colleges. She likes to spend time with her students, just as much as she likes to spend time on research and play with her little baby girl Audrey.

Kelly Black

Kelly Black is a numerical analyst with an interest in education. He likes to keep students busy with writing, on-line activities, and mathematical modeling. He is also curious about ways to better utilize webworks. His favorite classes to teach are the introductory courses in the students’ first 2 years. In his spare time he enjoys cycling and fly fishing.

Michael Ramsdell

Michael Ramsdell is an associate professor in the physics department at Clarkson University. He enjoys teaching introductory physics lectures and labs. His research has focused on the implementation and assessment of the Physics Team Design Program and COMPASS for the calculus-based introductory Mechanics and Electricity and Magnetism courses. He is interested in the area of Physics Education Research Laboratory, Curriculum Development and Design. All physics labs add unique competitive challenges where students utilize a mathematical model to predict physical outcomes. He is an active hockey player and coaches his son’s team.

Joseph Skufca

Joseph Skufca is chair of the department of mathematics at Clarkson University, Potsdam, New York. His dissertation research focused on application of algorithms for numerical exploration of unstable invariant sets to low dimensional systems. In particular, the effort was focused on transient chaos within finite dimensional Galerkin models of planar Couette flow, with a goal of increasing the understanding of the onset of turbulence within these systems. Additionally, he is interested in modeling of complex and evolving networks, to include how such systems may be analyzed if the network topology is not observable. His general area of concentration is non-linear dynamics and control of chaotic systems. He has done some research in stochastic processes and hope to expand that work. In the future, he hopes to explore the inter-relationships between noisy observations, adaptive control algorithms, and dynamic networks to provide more insightful models of conflict. Recent research has focused on application of data-mining tools to complex data sets, to include data mining within the context of geospatially related information.