With Thomas Fisher, Michael Hughes, & Xin Wang (Miami University)
A course originally designed for those needing material beyond introductory statistics has been rebranded at Miami University as both a service course for those majors requiring more applied statistics and a gateway to our majors. The curriculum meshes introductory experimental design, multiple regression, and an introduction to generalized linear models with the fundamentals of data science, including code repositories, reproducibility, readability, processing data from multiple sources and predictive modeling. The course serves a broad audience of students majoring in the biological and social sciences, computer science and software engineering, as well as majors in mathematics, analytics, data science and statistics. Enrollment in the course has grown from about 100 students per year in the 2016-2017 academic year to over 300 in the current academic year.
In this session we will mimic a (virtual) classroom experience on the topic of multiple regression while overviewing the course, its curriculum, evolution, course materials and pseudo-inverted classroom implementation. Session attendees are expected to have a rudimentary background in both RStudio and RMarkdown, and will have the opportunity to play the role of the student while the presenters step through a typical “in-class” activity. The presenters have broad backgrounds in statistical methods, but are fairly new to the inverted classroom paradigm and do not traditionally work on statistics education research. Attendees are encouraged to actively engage with the presenters on topics such as best practices and modes of delivery. Materials for the session will be available at the presenter’s github site: https://github.com/tjfisher19/uscots2021_presentation.