I was asked to remind my students to fill out their course evaluations, so I made a few slides with the help of 's RMP data ().
Students writing evaluations of your instructors: Don't act like an anonymous internet troll. Also, be mindful of your raced/gendered expectations of faculty of color & women.
How do student evaluations describe men and women differently? Test your theories here:
It's course evaluation time; kicking off my classes this week with a discussion of gender bias in course evals, using 's app as an example:
Men are more often described as knowledgeable, intellectual, funny, and outstanding, on RateMyProfessor.
on the penultimate day of class I showed my students their own bias from RateMyProfessor compiled by
2) - is crazy sexist. Check out this tool for looking at the prevalence of gendered language in Rate My Prof reviews. You might like to try words like: genius, intelligent, smart, knowledgable or mean, rude, incompetent.
All students, profs, & university admins need to check out this interactive graphic of gendered language bias in teaching evaluations compiled by from data. H/t for bringing this to my attention.
Gendered language in teaching evaluations. You can enter any word and check for yourself here: ht
this visualization of the effect of gender on how students describe their professors is maybe relevant
a) The correlation between RMP and SET is 0.7. You would know that if you read the piece. b) It's funny how people like you celebrated this work when it purported to show what you want it to show: It's ok to have ideological biases. But be aware of them.
More gender differences in teaching evaluations: This is consistent with 's recent findings on gender bias in teaching evaluations:
A prior analysis on the language used in *the same* student evaluations (RateMyProfessor) shows clear gender differences (men more likely to be described as "brilliant", women as "caring") - try this fantastic interactive demo (via ) 4/n
It also comes with this cool interactive tool.
Check out this website which elegantly displays the gender disparity in words used in online faculty evaluations: #dataviz