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

I held back from commenting overnight to chew it over, but I am still saddened by comments during a presentation I attended yesterday by Prof @trishgreenhalgh & @CIHR_IMHA.

The topic was “LongCovid, Myalgic Encephalomyelitis & More”.
I quote from memory.
1/n
#MECFS #LongCovid


The bulk of Prof @Trishgreenhalgh’s presentation was on the importance of recognising LongCovid patient’s symptoms, and pathways for patients which recognised their condition as real. So far so good.

She was asked about “Post Exertional Malaise”... 2/n

PEM has been reported by many patients, and is the hallmark symptom of ME/CFS, leading many to query whether LongCovid and ME/CFS are similar or have overlapping mechanisms.

@Trishgreenhalgh acknowledged the new @NiceComms advice for LongCovid was planned to complement... 3/n

the ME/CFS guidelines, acknowledging some similarities.

Then it all went wrong.
@TrishGreenhalgh noted the changes to the @NiceComms guidance for ME/CFS, removing support for Graded Exercise Therapy / Cognitive Behavioural Therapy. She noted there is a big debate about this. 4/n

That is correct: The BMJ published Prof Lynne Turner Stokes’ column criticising the change (Prof Turner-Stokes is a key proponent of GET/CBT, and I suspect is known to Prof @TrishGreenhalgh).

https://t.co/0enH8TFPoe

However Prof Greenhalgh then went off-piste.

5/n
Our preprint on the impact of reopening schools on reproduction number in England is now available online: https://t.co/CpfUGzAJ2S. With @Jarvis_Stats @amyg225 @kerrylmwong @KevinvZandvoort @sbfnk + John Edmunds. NOT YET PEER REVIEWED. 1/


We used contact survey data collected by CoMix (
https://t.co/ezbCIOgRa1) to quantify differences in contact patterns during November (Schools open) and January (Schools closed) 'Lockdown periods'. NOT YET PEER REVIEWED 2/

We combined this analysis with estimates of susceptibility and infectiousness of children relative to adults from literature. We also inferred relative susceptibility by fitting R estimates from CoMix to EpiForecasts estimates(https://t.co/6lUM2wK0bn). NOT YET PEER REVIEWED 3/


We estimated that reopening all schools would increase R by between 20% to 90% whereas reopening primary or secondary schools alone would increase R by 10% to 40%, depending on the infectiousness/susceptibility profile we used. NOT YET PEER REVIEWED 4/


Assuming a current R of 0.8 (in line with Govt. estimates: https://t.co/ZZhCe79zC4). Reopening all schools would increase R to between 1.0 and 1.5 and reopening either primary or secondary schools would increase R to between 0.9 and 1.2. NOT YET PEER REVIEWED 5/
OK I am going to be tackling this as surveillance/open source intel gathering exercise, because that is my background. I blew away 3 years of my life doing site acquisition/reconnaissance for a certain industry that shall remain unnamed and believe there is significant carryover.


This is NOT going to be zillow "here is how to google school districts and find walmart" we are not concerned with this malarkey, we are homeschooling and planting victory gardens and having gigantic happy families.

With that said, for my frog and frog-adjacent bros and sisters:

CHOICE SITES:

Zillow is obvious one, but there are many good sites like Billy Land, Classic Country Land, Landwatch, etc. and many of these specialize in owner financing (more on that later.) Do NOT treat these as authoritative sources - trust plat maps and parcel viewers.

TARGET IDENTIFICATION AND EVALUATION:

Okay, everyone knows how to google "raw land in x state" but there are other resources out there, including state Departments of Natural Resources, foreclosure auctions, etc. Finding the land you like is the easy part. Let's do a case study.

I'm going to target using an "off-grid but not" algorithm. This is a good piece in my book - middle of nowhere but still trekkable to civilization.

Note: visible power, power/fiber pedestal, utility corridor, nearby commercial enterprise(s), and utility pole shadows visible.

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I will keep sharing such learning thread 🧵 for you 🙏💞🙏

Keep learning / keep sharing 🙏
@AdityaTodmal