Over the next few weeks, students will get the chance to evaluate their professors and TAs. They're going to get it wrong. They'll be harder on women and people of color than on white men. Tenured white male faculty, in particular, should help their students understand this. 1/8
Several studies have revealed the limitations of student evaluations of teaching (SETs). Berkeley's Philip Stark, Statistician & Associate Dean of Mathematical and Physical Sciences, co-authored a major study on this question a couple of years ago. It found: 2/8
*SET are biased against female instructors by an amount that is large and statistically significant.
*The bias affects how students rate even putatively objective aspects of teaching, such as how promptly assignments are graded. 3/8 https://www.scienceopen.com/document_file/25ff22be-8a1b-4c97-9d88-084c8d98187a/ScienceOpen/3507_XE6680747344554310733.pdf …
*The bias varies by discipline and by student gender, among other things. *It is not possible to adjust for the bias, because it depends on so many factors. 4/8
*SET are more sensitive to students’ gender bias & grade expectations than they are to teaching effectiveness. [Read that again!]*Gender biases can be large enough to cause more effective instructors to get lower SET than less effective instructors. 5/8
"Instructor race is also associated with SET....minority instructors tend to receive significantly lower SET scores compared to white (male) instructors. Age, charisma, and physical attractiveness are also associated with SET.” 6/8 https://www.scienceopen.com/document_file/25ff22be-8a1b-4c97-9d88-084c8d98187a/ScienceOpen/3507_XE6680747344554310733.pdf …
I've often gotten valuable feedback in student evals, feedback that has improved my teaching. We have a lot to learn from our students, obviously. But given the well-documented shortcomings of SETs, we shouldn't be using them for hiring, tenure, or promotion decisions. 7/8
In the meantime, tenured faculty - especially tenured white men - should explain this stuff to our students before each evaluation season. Help them understand why evals matter to peoples' careers, & how implicit bias affects the results. They'll listen. 8/8 - end.
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