Gender Bias in Teaching Evaluations

By Friederike Mengel, Jan Sauermann, Ulf Zölitz. Social Science Research Network, 2017

404 citations recorded by the source corpus.

This paper provides new evidence on gender bias in teaching evaluations. We exploit a quasi-experimental dataset of 19,952 student evaluations of university faculty in a context where students are randomly allocated to female or male instructors. Despite the fact that neither students' grades nor self-study hours are affected by the instructor's gender, we find that women receive systematically lower teaching evaluations than their male colleagues. This bias is driven by male students' evaluations, is larger for mathematical courses and particularly pronounced for junior women. The gender bias in teaching evaluations we document may have direct as well as indirect effects on the career progression of women by affecting junior women's confidence and through the reallocation of instructor resources away from research and towards teaching.

Subfields: Education, Gender Studies

View Paper: https://www.semanticscholar.org/paper/f3df2009cacce18fbf688ff4392855e616d9b294

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Education

1. Lauren A. Rivera
2. András Tilcsik
3. Anne Boring
4. Steve Stewart-Williams
5. L. Halsey
6. Wolfgang Stroebe

In Gender Studies

1. András Tilcsik
2. Lauren A. Rivera
3. Anne Boring
4. Steve Stewart-Williams
5. L. Halsey

Citations in the corpus

• Gender biases in student evaluations of teaching (2017, background): “18 Two other recent articles studying SET scores in the context of Dutch universities also find evidence of gender biases, see Mengel et al. (2016) and Wagner et al.”
• Men, women and STEM: Why the differences and what should be done? (2020, background): “• Women commonly get lower ratings than men in teaching evaluations (Rosen, 2018), even in experimental studies that equalize teaching quality (MacNell et al., 2015; Mengel et al., 2018).”
• Scaling Down Inequality: Rating Scales, Gender Bias, and the Architecture of Evaluation (2019, background): “For example, students give significantly higher ratings to textbooks and readings assigned by men than those assigned by women, even when all course materials are identical across instructors (Mengel et al. 2017).”