The other the day the Chronicle posted research by Kristina M. Mitchell and Jonathan Martin, “Gender Bias in Student Evaluations,” published in PS: Political Science & Politics last week. Here is the abstract:
“Many universities use student evaluations of teachers (SETs) as part of consideration for tenure, compensation, and other employment decisions. However, in doing so, they may be engaging in discriminatory practices against female academics. This study further explores the relationship between gender and SETs described by MacNell, Driscoll, and Hunt (2015) by using both content analysis in student-evaluation comments and quantitative analysis of students’ ordinal scoring of their instructors. The authors show that the language students use in evaluations regarding male professors is significantly different than language used in evaluating female professors. They also show that a male instructor administering an identical online course as a female instructor receives higher ordinal scores in teaching evaluations, even when questions are not instructor-specific. Findings suggest that the relationship between gender and teaching evaluations may indicate that the use of evaluations in employment decisions is discriminatory against women.”
Mitchell published another version of this research in Slate: “Student Evaluations Can’t Be Used to Assess Professors” (March 19, 2018)
Also, many thanks to CSW member Yi-Ping Ong for sharing “Is Gender Bias an Intended Feature of Teaching Evaluations?” from February in Insider Higher Ed.
1 thought on “Student evaluations & potential employment discrimination”
Interesting article. It might be hard to study, but I also think an analysis of emails to faculty members comparing the times that they were addressed as Mr./Mrs./Ms. vs Dr. would also be interesting.
While the articles is interesting, it is short on solutions…Is the answer assessment of student learning? peer evaluations of a single class? keeping evaluations but remembering these concerns? Is there a way to do student evaluations to reduce gender bias?