Every semester I have about 22 brand new students in an intro course. I try pretty hard to learn their names quickly, without sacrificing significant class time to the endeavor. There are always some I get right away (the ones that are outspoken from the first lecture), and others that take weeks (usually the ones that tend to be quiet and/or absent).
Our classrooms are amazingly diverse, and I truly enjoy that. It means I personally have a hand in broadening participation in computing, to use the NSF phrase. Plus it’s just darned interesting to work with people with varied backgrounds and stories.
To learn names, I notice myself doing something that I believe is perfectly harmless, natural, and useful, but could seem somewhat dubious from the outside. That is, I cluster the names and faces by perceived ethnicity, race, and gender. It means I don’t really have to match 22 names to 22 faces; I just have to distinguish names within the perceived cluster. Not every face is an equally likely candidate for the name Mahesh or Mahmud, Matsumoto, Miguel, or… Alice.
The embarrassing consequence, however, is that as I’m still learning, my mistakes appear to be indicative of my hillbilly heritage, namely, “durr… all y’all [ETHNIC] folks look alike!” By week three, I’m specifically focusing on distinguishing between (for example) the three quiet Indian women or the two absent Turkish guys.
I don’t believe there are any clusters for which I’m particularly better or worse at learning names, though I could be wrong. There probably are clusters for which I can make finer distinctions than others: Bangladeshis vs. Pakistanis vs. Hindu South Indians; or Chinese, Japanese, Thai (certainly by name, sometimes by face or dress). But I’m useless at the varieties of Caribbean Islanders or Eastern Europeans.
Also, once in a while, my perception doesn’t match reality, such as when I guess that Bob Smith is Angelo Santos. But as a result of such a mistake, I manage to learn Bob’s and Angelo’s names indelibly! So perhaps intentionally making cross-ethnic mistakes would be an even better learning strategy.