Perspectives 6: Uniform surjective grading?

I’m somewhat disconcerted by this recent article that appeared in USA Today. I wouldn’t have noticed it, except for the fact that it was critiqued to some extent by Mark Chu-Carroll and various commenters over at Good Math, Bad Math. I might be somewhat happier if I hadn’t noticed it.

The proposal to impose a 50 point minimum grade for an assignment (on a 100 point scale) is problematic for a number of reasons. Some of these are practical, and can be seen clearly by considering some analogous scenarios. I’m fairly certain that it would be in some people’s interest to impose similar artificial bounds in other situations, but such a proposition would be a complete non-starter. Declaring an NHL player’s plus-minus rating to have a minimum value of 4 would clearly invalidate the descriptive value of the statistic. Declaring that employees can be given no worse than a “satisfactory” review would have interesting repercussions in the corporate environment, since it would effectively eliminate the ability to fire someone for non-performance of their job, or even incompetence.

Apparently, however, mathematical competence is not required in this arena. According to the article, the argument in favor of this artificial minimum is this:

Other letter grades — A, B, C and D — are broken down in increments of 10 from 60 to 100, but there is a 59-point spread between D and F, a gap that can often make it mathematically impossible for some failing students to ever catch up.

“It’s a classic mathematical dilemma: that the students have a six times greater chance of getting an F,” says Douglas Reeves, founder of The Leadership and Learning Center, a Colorado-based educational think tank who has written on the topic.

Last time I checked, the closed interval S := \left[0,100\right] in the integers contains 101 elements, and a standard 90-80-70-60 scale allocates them in such a way that there is a 60 point spread between the minimum scores meriting an F and a D. Feel free to check my arithmetic, but even a hand calculator can usually get 60 - 0 = 60 correct. Apparently correct arithmetic is not important in an argument supporting how grades should be computed.

I’m also astounded—perhaps I shouldn’t be, but I am—that the head of a think tank would propose that statement as a “classic mathematical dilemma”. It certainly appears to contain two classic mathematical errors, however: one being a fencepost error, and the other being an unsupported assumption that grades are uniformly distributed over S. If the grades obtained by students were distributed uniformly over S, then the expected value of student grades would be 50. So far as I am aware, neither high school graduation rates nor university retention and graduation rates support such a claim. Moreover, we should expect strictly more values corresponding to an A than to any of the grades B, C, or D on a given assignment or exam, since it contains one more element of S than the others do. Would anyone care to provide the empirical evidence justifying such a result?

As unsettling as these errors are, I find the sidebar to be more disquieting. The examples it provides for calculating grades do not correspond to the general assumption in the article that a letter grade of F is recorded, and then considered to be 0 at some later point. Rather, it shows what happens when we have recorded the scores on a 100-point scale, and then compute the mean with the actual score or with a false minimum of 50. In these comparisons, the recorded failing grade is not in general a zero.

Let’s take a slightly closer look at what is happening here. There are two grading scales in use here: the closed interval S=\left[0,100\right] of integer scores, and the set L=\left\{\mathrm{A,B,C,D,F}\right\} of letter grades. The 90-80-70-60 convention establishes a surjective mapping \varphi: S\to L that preserves the standard order on each of these sets. However, \varphi is not injective, so we cannot define a consistent arithmetic on L in terms of \varphi^{-1}. The argument provided in support of this 50-minimum grading scale is, in effect, an argument about how a representative of the preimage \varphi^{-1}\left(x\right) should be selected for each x\in L.

Unfortunately, this argument does not go through if we already have scores recorded. We are not free to select any element of the preimage. At least in the sciences, there’s a term for such an operation: it’s called falsification of data. Viewed this way, the implications of the grading policies being adopted by some school districts are, at best, disturbing.

Copyright © 2008 Michael L. McCliment.

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