Addressing Gender and Racial Biases in Teaching Evaluations
Law & Public Policy Major (O'Neill School of Public & Environmental Affairs)
Daniel McDonald (College of Arts & Sciences)
Student evaluations of faculty teaching are often found to display biases against female and minority faculty relative to white men, even when those instructors are teaching from the same material and have similar levels of teaching experience. This project will use data from a previous study to examine the extent of this phenomenon and whether other student factors may be involved. We will also perform a randomized experiment with a large statistics course taught at IU to see if earlier findings are replicable. The students involved will learn basic statistical techniques for causal inference and survey design; develop competencies in computer programming, workflow, Github, and visualization techniques in R; and undertake basic research methods.
Technology or Computational Component
At the most basic level, this project will involve reproducing the statistical analysis of an earlier published study. At a minimum, the student(s) will learn how to read in a public dataset, perform data cleaning, implement simple statistical methods (using R or Python), create reproducible workflows (with Jupyter Notebooks or Rmarkdown), and use version control (with git and Github). We will also try to reproduce the previous study with a Fall 2019 course at IU. This will likely involve working with UITS and the IRB to implement slight changes to online student course evaluations (at random). It will also create more complex issues for the student(s) in terms of how to merge data from disparate sources. The student(s) will be asked to create graphics and possibly create a web application for exploring and disseminating the results.