. . . and Kaiser Fung is unhappy. In a post entitled, “Princeton’s loss of nerve,” Kaiser writes:
This development is highly regrettable, and a failure of leadership. (The new policy leaves it to individual departments to do whatever they want.)
The recent Alumni publication has two articles about this topic, one penned by President Eisgruber himself. I’m not impressed by the level of reasoning and logic displayed here.
Eisgruber’s piece is accompanied with a photo, captioned thus:
The goal of Princeton’s grading policy is to provide students with meaningful feedback on their performance in courses and independent work.
Such a goal [writes Kaiser] is far too vague to be practical. But let’s take this vague policy at face value. How “meaningful” is this feedback when 40% of grades handed out are As, and 80% of grades are either As or Bs? (At Stanford, Harvard, etc., the distributions are even more skewed.)
Here are some data:
My agreement with Kaiser
As a statistician, I agree with Kaiser that if you want grades to be informative, it makes sense to spread out the distribution. It’s an obvious point and it is indeed irritating when the president of Princeton denies or evades it.
I’d also say that “providing students with meaningful feedback” is one of the least important functions of course grades. What kind of “meaningful feedback” comes from a letter (or, for that matter, a number) assigned to an entire course? Comments on your homeworks, papers, and final exams: that can be meaningful feedback. Grades on individual assignments can be meaningful feedback, sure. But a grade for an entire course, not so much.
My impression is that the main functions of grades are to motivate students (equivalently, to deter them from not doing what it takes to get a high grade) and to provide information for future employers or graduate schools. For these functions, as well as for the direct feedback function, more information is better and it does not make sense to use a 5-point scale where 80% of the data are on two of the values.
One can look at this in various ways but the basic psychometric principle is clear. For more depth, go read statistician Val Johnson’s book on grade inflation.
OK, now for my disagreement or, maybe I should say, my discomfort with Kaiser’s argument.
Grad school grades.
In grad school we give almost all A’s. I’m teaching a course on statistical communication and graphics, and I love the students in my class, and I might well give all of them A’s.
In other grad classes I have lots of grading of homeworks and exams and I’ll give mostly A’s. I’ll give some B’s and sometimes students will complain about that, how it’s not fair that they have to compete with stat Ph.D. students, etc.
The point is, if I really believe Kaiser’s principles, I’d start giving a range of grades, maybe 20% A, 20% B, 20% C, 20% D, 20% F. But of course that wouldn’t work. Not at all. I can’t do it because other profs aren’t doing it. But even if all of Columbia were to do it . . . well, I have no idea, it’s obviously not gonna happen.
Anyway, my point is that Princeton’s motivation may well be the same as mine: yes, by giving all A’s we’re losing the ability to give feedback in this way and we’re losing the opportunity to provide a useful piece of information to potential employees.
But, ultimately, it’s not worth the trouble. Students get feedback within their classes, they have internal motivation to learn the material, and, at the other end, employers can rely on other information to evaluate job candidates.
From that perspective, if anyone has the motivation to insist on strict grading for college students, it’s employers and grad schools. They’re the ones losing out by not having this signal, and indirectly if students don’t learn the material well because they’re less well motivated in class.
In the meantime, it’s hard for me to get mad at Princeton for allowing grades to rise, considering that I pretty much give all A’s myself in graduate classes.
P.S. Kaiser concludes:
The final word appears to be a rejection of quantitative measurement. Here’s Eisgruber:
The committee wisely said: If it’s feedback that we care about, and differentiating between good and better and worse work, that’s what we should focus on, not on numbers.
The wisdom has eluded me.
Let me add my irritation at the implicit equating of “wisdom” with non-quantitative thinking.