Nothing matters but straight As

Fifteen years ago, Princeton was famous (infamous, depending on your views) for being a leader among elite U.S. universities in the fight against inflating grades, imposing a quota on how many As professors are allowed to give out in any course, a policy described as “grade deflation.” The policy was scrapped in 2014, when the administration bowed to pressure from parents and lack of reciprocation from peer institutions.

It’s come to this. Fifteen years later, Princeton appears in the lead sentence of a Wall Street Journal article on the meaninglessness of GPAs and cum laude honors in the world of education in this millennium.

I have written a few posts about Princeton's losing fight against grade inflation (here, here, here). In 2014, I grudgingly understood the administration’s decision to abandon grade deflation as a bow to pragmatism, although I am not a fan of the policy reversal. So I won't repeat those arguments here.

But what about the journalism!

This is the entire first paragraph from WSJ:

Nearly half of students who graduated from Lehigh University, Princeton University and the University of Southern California this year did so with cum laude, magna cum laude or summa cum laude honors, or their equivalents. At Harvard and Johns Hopkins, more got the designations than didn’t.

So the paper decided to lead with Lehigh, Princeton and USC, where fewer than 50% of the graduates received honors, instead of Harvard and Johns Hopkins, where about 60% of graduates received honors. What motivates this choice?

The mention of Harvard merits further investigation because some years ago, it came out that almost everyone is an honors graduate, with only ~10% not given cum laude honors. See my previous post "Harvard gives new meaning to meritocracy." Did that change? Apparently so. According to the article, Harvard capped the honors class at 60%, starting in 2005. This Crimson article reported that the proportion of A grades did not decrease after the honors cap; and the college all but abandoned efforts to fight grade inflation.

A key sentence in the WSJ article is:

"A 4.0 does signal something significant, that that student is good," said Stuart Rojstaczer, a former professor at Duke University who studies grade inflation. "A 3.7, however, doesn’t. That’s just a run-of-the-mill student at any of these schools."

Note that if the school does not grant A+s, a GPA of 4.0 means straight As. He's saying anything but straight As is meaningless.

***

When we search for meaning for something like GPA 3.7, we are grasping for reference points. 3.7 at one school is different from 3.7 at another school; within a school, 3.7 in engineering is different from 3.7 in the humanities; 3.7 by one professor is different from 3.7 by another.

Understanding the distribution of grades at various levels is key to interpreting GPAs. However, few college transcripts are annotated with data about grade distributions.

Grade curving aids interpretation by re-scaling the data to well-defined reference points.

Comments

[John Hall]

If I were in charge of one of these institutions, I would fit a hierarchical IRT model to grades, with groups at least taking into account major and time period each class was taken. Could be used to evaluate the students.

[Kaiser]

JH: You raised another sore topic that professors are powerless to bring up: the number of As given out is strongly correlated with how students evaluate the course. I wonder if any institution has published such an analysis. It would be embarrassing to see.

The IRT model would be very useful to admissions staff at graduate schools!

[Dave]

Yes, I think the real problem is that graduate admissions are lazy (or maybe want rigid admissions rules to avoid lawsuits) and pretend that you can compare GPAs from competitive elite schools to those at less competitive ones. This puts pressure on the competitive schools to give more As so as to not disadvantage their students.

[Kaiser]

Dave: "Lazy" and "lawsuit avoiding" are part of it but when you are reading lots of applications, it's hard to have to look at each GPA, correlate with the school, and adjust the numbers on the fly. Plus, the grade distributions at colleges are just not common knowledge. That's why a model that spits out adjusted GPAs would be useful.