[Professor Tracy Larrabee] uses a three-pronged approach to support underrepresented students in her class.
“The first is that we have had a very diverse teaching staff,” she said. “We have one professor, four TAs and four MSI tutors, and during this time it just happened that of those people, half were female, we always had at least one African American, one Latinx, and one non-gender conforming tutor so that everyone could feel a connection to someone on the teaching staff.”
“Another technique I use is to emphasize failure as the appropriate path to learning,” she said. “Engineering is hard; it’s good to fail the first time you attempt a problem. People who fail at a problem the first time tend to retain things better than those who luck into the right answer.”
Her final tactic is to explicitly discuss stereotype threat. This is the risk that someone (i.e., from an underrepresented minority) might take routine negative experiences as confirmation that they are fundamentally unsuited for something like higher education.
“One of my African American MSI tutors—who are extremely high achieving students selected to provide supplemental tutoring to others—told me it was like having a light bulb go off for him,” Larrabee said. ”Until I discussed the issue in class, he felt like he didn’t belong in this major, but after we talked about stereotypes, he realized it wasn’t that he was unsuited for the material. It was hard for everyone!”
I’ve linked to a number of articles about the misuse of p-values. Recently, I read a nice article in the October/November 2019 issue of MAA Focus summarizing a conversation between the Executive Directors of the Mathematical Association of America and the American Statistical Association about the ASA’s call to eliminate the use of p-values. Per copyright, I can’t copy the entire article here, but let me quote the lead paragraph: