This repository is associated with the paper An Analysis of Sex Differences in Computing Teaching Evaluations, which will be published in the proceedings of the third workshop on Gender Equality, Diversity, and Inclusion in Software Engineering (GE@ICSE, 2022).
Anonymous student teacher evaluations are commonly used to evaluate the quality of computing instructors at the university level. However, such teaching evaluations are subject to gender and sexbased biases, calling into question their utility and scope. In this paper, we first use data from a large public American university to replicate previous findings showing that significant sex-related differences persist in computing teaching evaluations. Intriguingly, we find that the sex-differences in computing teaching evaluations are primarily driven by bias involving professors, while significant sex-based differences for student-instructors are not observed. Finally, we place the magnitude of the sex-based differences we observe into a broader engineering context.
- Our de-identified dataset, aggregated by department, sex, and, teaching title (professor v.s. student instructor)
- A copy of the paper's preprint
The aggregated version of our data is also available online on Google sheets here. If you would like to access the non-aggregated data for individual instructors or classes, please reach out to the research team (see their emails below)!
- Priscila Santiesteban, Computer Science Ph.D. Student, University of Michigan pasanti@umich.edu
- Madeline Endres, Computer Science Ph.D. Candidate, University of Michigan, endremad@umich.edu
- Westley Weimer, Computer Science Professor, University of Michigan weimerw@umich.edu