Home

Fall 2019 Computational Teaching Resources Computation (use of a computer to numerically solve, simulate, and visualize a physical problem) has revolutionized scientific research and engineering practice. However, STEM (physics, chemistry, mathematics, computer science, and engineering) majors enrolled in introductory physics are not adequately exposed to computational modeling and analysis. Students enrolled in a traditional introductory calculus based physics course (still widely implemented across the nation) have minimal or zero exposure to computational physics tools and ideas. Solving computational problems in physics is not just about physics knowledge but also about skills and knowledge associated with math, programming, and modeling. To meet this growing need I utilize Visual Python (VPython) in my introductory calculus based physics course to promote a problemsolving environment where students can numerically solve physics problems, create visual simulations, practice mathematical and physical modeling, and investigate physics phenomena, rather than just calculating an answer. VPython is an opensource, freely available environment accessible to users of all major computing platforms. Such an approach has already been pioneered by Chabay and Sherwood in their Matter & Interactions (M&I) curriculum, which introduces computational modeling as an integral part of the introductory physics course. For purposes of visualizing 2D scientific data we utilize Matplotlib. Useful Links: Visual Python Matplotlib Other courses taught: Physical science – physics for non science majors (PHSC1011), Trigonometry based physics for science majors (PHYS1111&1112), Honors physics, Calculus based physics (PHYS2211H&2212H), Thermal physics (PHYS4310), Classical Mechanics (PHYS3250), and Special Topics courses (PHYS 4950 – Advanced Statistical Mechanics, Solid State Physics, and Magnetic materials, Numerical Computing using Fortran 90, and Many Particle Physics) 
