With rising interest in data science and programming, those of us teaching (with) R can now reach a larger audience than we ever thought possible. As educators, it is our responsibility to ensure that while we are building interesting and challenging curricula for these students, we also do it in a way that is attractive and engaging for a diverse audience (both in terms of computing background and in terms of demographics) as well as supportive enough to minimize the number of students who strunggle and fall behind and can't catch up. Adopting welcoming and inclusive practices can enable these students, whatever their background and circumstances, to achieve their potential and grow and engage with the larger R and data science community.
In this talk, we highlight a collection of pedagogical considerations, tips, and tricks for designing a welcoming and inclusive curriculum for teaching (with) R. In addition, we demonstrate tooling and infrastructure solutions for making it as straightforward and painless as possible to put these approaches into practice in the classroom. We also discuss how we put these tips and tools in practice in the newly launched Introduction to Data Science course at the University of Edinburgh.
While the talk is designed around teaching R, the pedagogical points apply to teaching programming in any computing language and many of the tips and tools we present can be used in teaching of a broad range of STEM and non-STEM disciplines with minor modifications (e.g. building a student dashboard based on data from a pre-course survey, datasets that don't encode gender as binary, tools livecoding, etc.).