Gendered Language in Teacher Reviews

The New York Times' The Upshot recently reported on a new interactive created by Benjamin Schmidt, a Northeastern University history professor.  Using 14 million student reviews posted on the website RateMyProfessor.com (hereafter abbreviated as RMP), the interactive tool lets users explore the words used in the reviews to describe male and female teachers: enter any word or two-word phrase, and the chart displays how often it appeared in reviews and how it broke down by gender and department.

The scores are normalized by gender and field.  "The largest fields are about 750,000 reviews apiece for female English and male math professors. The smallest numbers on the chart, which you should trust the least, are about 25,000 reviews for female engineering and physics professors."

Search term: funny

Search term: nice

Search term: great professor

While not all search terms produce gendered results, many do.  For example:
"Men are more likely to be described as a star, knowledgeable, awesome or the best professor. Women are more likely to be described as bossy, disorganized, helpful, annoying or as playing favorites. Nice or rude are also more often used to describe women than men.
Men and women seemed equally likely to be thought of as tough or easy, lazy, distracted or inspiring."
This interactive tool uncovers unconscious biases, however it should not be considered a scientific study of gendered language in teacher reviews.  Here are some caveats to keep in mind:
  • Schmidt included every review he could find on the RMP website, and as such, "almost any queries that show visual results on the charts are 'true' as statements of the form 'women are described as x more than men are on rateMyProfessor.com.' But given the many, many peculiarities of that web site, there's no way to generalize from it to student evaluations as used inside universities."
  • Not all categories (gender/discipline) are equally represented.  For example, female engineering and physics professors have many fewer reviews than female English and male math professors. Users should  check the X axis to make sure their search words are used at a reasonable level.
  • Gender (of the professor) was auto-assigned; Schmidt estimates that one in sixty are tagged with the wrong gender because they're a man named "Ashley," for example.
  •  Results could be influenced by the gender of the reviewer, which is not known.

Read more:
http://www.nytimes.com/2015/02/07/upshot/is-the-professor-bossy-or-brilliant-much-depends-on-gender.html?hp&action=click&pgtype=Homepage&module=second-column-region&region=top-news&WT.nav=top-news&abt=0002&abg=1
http://benschmidt.org/2015/02/06/rate-my-professor/
http://benschmidt.org/profGender/#%7B%22database%22%3A%22RMP%22%2C%22plotType%22%3A%22pointchart%22%2C%22method%22%3A%22return_json%22%2C%22search_limits%22%3A%7B%22word%22%3A%5B%22knowledgeable%22%5D%2C%22department__id%22%3A%7B%22%24lte%22%3A25%7D%7D%2C%22aesthetic%22%3A%7B%22x%22%3A%22WordsPerMillion%22%2C%22y%22%3A%22department%22%2C%22color%22%3A%22gender%22%7D%2C%22counttype%22%3A%5B%22WordsPerMillion%22%5D%2C%22groups%22%3A%5B%22department%22%2C%22gender%22%5D%7D

TeachingwithData.org resources:
Gender in STEM Education: A Data-Driven Learning Guide (http://www.teachingwithdata.org/resource/3446)
Gender Inequality in the US (http://www.teachingwithdata.org/resource/3161)
Women's Education (http://www.teachingwithdata.org/resource/3104)
Occupational Sex Segregation (http://www.teachingwithdata.org/resource/3127)
Gender, Occupation, and Earnings (http://www.teachingwithdata.org/resource/3108)
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