When the university starts sending out teaching evaluation reminders, I tell all my classes about bias in teaching evals, with links to the evidence. Here's a version of the email I send, in case anyone else wants to poach from it.

1/16

When I say "anyone": needless to say, the people who are benefitting from the bias (like me) are the ones who should helping to correct it. Men in math, this is your job! Of course, it should also be dealt with at the institutional level, not just ad hoc.
OK, on to my email:
2/16
"You may have received automated reminders about course evals this fall. I encourage you to fill the evals out. I'd be particularly grateful for written feedback about what worked for you in the class, what was difficult, & how you ultimately spent your time for this class.

3/16
However, I don't feel comfortable just sending you an email saying: "please take the time to evaluate me". I do think student evaluations of teachers can be valuable: I have made changes to my teaching style as a direct result of comments from student teaching evaluations.
4/16
But teaching evaluations have a weakness: they are not an unbiased estimator of teaching quality. There is strong evidence that teaching evals tend to favour men over women, and that teaching evals tend to favour white instructors over non-white instructors.
5/16
Here is some specific information which may be useful if you wish to learn more about the biases described above:
6/16
Mengel, Sauermann, Zölitz - Gender bias in teaching evaluations. This one directly addresses mathematics courses, which I think is useful. The short version: female instructors receive lower evals than they deserve, mostly because of male students.
https://t.co/znL2HgtTSz
7/16
Boring - Reducing discrimination through norms or information. This one is a study which basically says that sending a message like this is a good idea!

https://t.co/eK6oD3I7sI

The conclusions of the study are as follows:
8/16
a) Simply reminding people not to be biased when filling out their teaching evaluations seems not to have an effect.
b) If as well as the reminder, you inform people that people that bias really does exist, in their exact setting, then does help reduce the resulting bias.
9/16
Chávez and Mitchell - Exploring Bias in Student Evaluations: Gender, Race, and Ethnicity. An experiment in the setting of an online course, which made it possible to compare variables in a much more controlled way.
https://t.co/3XVaLofLoe
10/16
.@mcgillu's report on gender equity in course evaluations (not peer-reviewed) shows statistically significant differences in course evaluations depending on the instructor's gender.

https://t.co/TO2HCgv4bT
11/16
This message feels particularly important as none of this is mentioned in McGill's guidelines for interpreting course evaluations (https://t.co/WSmNX8jCBn)
12/16
I hope you will all take seriously both the existence of this problem, and the idea that by paying attention, and having information about where and when this happens, you can actually help improve the situation. "
End email.
13/16
Then I include a P.S. with a few other references for any of my students who want to learn more:

* Carolina Women's centre - Teaching evaluations and bias
https://t.co/fGK3jTZZNg
14/16
* Bias in Student Evaluations of Minority Faculty: A Selected Bibliography

https://t.co/cKvcgP4Vt0
15/16
* Huston - Race and Gender Bias in Higher Education: Could Faculty Course Evaluations Impede Further Progress Toward Parity?

https://t.co/Vb76Q7TTZl
16/16

More from Education

Time for some thoughts on schools given the revised SickKids document and the fact that ON decided to leave most schools closed. ON is not the only jurisdiction to do so, but important to note that many jurisdictions would not have done so -even with higher incidence rates.


As outlined in the tweet by @NishaOttawa yesterday, the situation is complex, and not a simple right or wrong https://t.co/DO0v3j9wzr. And no one needs to list all the potential risks and downsides of prolonged school closures.


On the other hand: while school closures do not directly protect our most vulnerable in long-term care at all, one cannot deny that any factor potentially increasing community transmission may have an indirect effect on the risk to these institutions, and on healthcare.

The question is: to what extend do schools contribute to transmission, and how to balance this against the risk of prolonged school closures. The leaked data from yesterday shows a mixed picture -schools are neither unicorns (ie COVID free) nor infernos.

Assuming this data is largely correct -while waiting for an official publication of the data, it shows first and foremost the known high case numbers at Thorncliff, while other schools had been doing very well -are safe- reiterating the impact of socioeconomics on the COVID risk.

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