This paper is from Session 6F: Novel Approaches to Teaching Probability
which comes under Topic 6: Innovations in teaching probability
Paper 6F3 (Thursday 12th, 11:00-12:30)
Integrating Computational Learning in Probability
Presenter
- Amy Wagaman (Amherst College, United States)
Abstract
The mathematical foundations of probability can be challenging for our students to learn, and our students tackle many problems for practice. While analytical solutions to some problems can be difficult, empirical simulations can give intuition and guidance to students, provided that the students are able to perform the simulations. We discuss integrating computational learning in a probability course with a goal to strengthen student knowledge of probability concepts, algorithmic thinking, and computational skill. These skills also assist with bridging the gap between statistical theory and practice. Specific computational skill goals include writing functions, writing simulations to verify analytical results (including communicating results), and using a reproducible workflow.