Speaker
Description
Many mathematical problems that students encounter in typical undergraduate mathematics courses are designed so that an exact solution can be found without using a computational tool. While this is beneficial for learning fundamental mathematics, large-scale “real-world” applications that students may encounter after college, especially in the life sciences, frequently require complex and sophisticated computational algorithms. In undergraduate Numerical Methods, students learn how to build – and when to implement – algorithms to approximate solutions to common mathematical problems. This course therefore provides a critical foundation for students as they translate mathematical theory into future STEM careers.
An undergraduate numerical methods course is uniquely challenging because it requires treatment of concepts from both theoretical and practical perspectives, and a solid knowledge of several prequisites. In addition, many textbooks and programming languages used in this course are costly, further impacting its accessibility. To address these challenges, we worked to create a set of open-access ancillary materials to supplement free textbooks and coordinate with the introduction of Python as the primary programming language. These included computer programming activities with built-in scaffolding, a theoretical problem bank, a standard set of course notes, a review packet for prerequisite material, and a curated library of external multimedia resources. The work of creating these materials also led to increased conversations among the primary faculty and improved pedagogy. In this talk we will share some early results from development and implementation of these materials over the previous academic year.
