Posts with tag Higher Education
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Blaming the humanities fields for their travails recently can seem as sensible as blaming polar bears for not cultivating new crops as the arctic warms. It’s not just that it places the blame for a crisis in the fundamentally wrong place; it’s that it
It’s coming up on a year since I last taught graduate students in the humanities.
Every year, I run the numbers to see how college degrees are changing. The Department of Education released this summer the figures for 2019; these and next year’s are probably the least important that we’ll ever see, since they capture the weird period as the 2008 recession’s shakeout was wrapping up but before COVID-19 upended everything once again. But for completism, it’s worth seeing how things changed.
Ranking Graduate Programs
While I was choosing graduate programs back in 2005, I decided to come up with my own ranking system. I had been reading about the Google PageRank algorithm, which essentially imagines the web as a bunch of random browsing sessions that rank pages based on the likelihood that you–after clicking around at random for a few years–will end up on any given page. It occurred to me that you could model graduate school rankings the same way. It’s essentially a four-step process:
Pick a random department in the United States.
Pick a random faculty member from that department.
Go to that faculty member’s graduate department.
90% of the time, return to step 2; 10% of the time, return to step 1.
At the end of each stage, you’ll be in a different department; but more prestigiously any given department’s faculty are placed, the more likely you are to be there.
Using transition matrices, these numbers converge after a relatively short period.
I ran it on history departments, but have never circulated the history scores. (Rankings make people mad, and the benefit seems worse than the cost.) But one of my roommates at the time, Matthew Chingos, was already moving towards working in higher education policy and grad school in political science, so we wrote up a paper applying it to Political Science departments and published it in PS in 2007. (Schmidt, B., & Chingos, M. (2007). Ranking Doctoral Programs by Placement: A New Method. PS: Political Science & Politics, 40(3), 523-529. doi:10.1017/S1049096507070771)
It’s a pretty simple method, but I still occasionally get questions about it, the data, and the underlying code. As I recall, the political science data was viewed as slightly sensitive, so the arrangement we made with the American Political Science Association was that they would handle requests for the data and we would only provide code.
This was in 2005, so reproducibility was not a worry–nowadays, you’d put all this stuff on github. In response to a recent request, I’ve just done that.
The core code was interesting to look it, because it’s stuff I wrote in R fifteen years ago. It basically seems to still work, but it has little in common with how I’d handle the problem nowadays.
Ranking Computer Science Programs as of 2015
Still, the proof is in the eating. So I went looking for some new data to try it on. On the theory that computer science faculty are too distracted by their overwhelming course sizes and endless parade of job searches to be bothered by this, I’ll do them.
Alexandra Papoutsaki et al. created a crowdsourced dataset of CS faculty that they expect to be “80% correct” at Brown. They seem to have updated a version that’s sitting inside a Github repository here, so that’s what I’ve used. I’m using placements that are from 2005-2015 here.
| school | p |
|---|---|
| University of California - Berkeley | 17.2835408 |
| Massachusetts Institute of Technology | 16.6558147 |
| Stanford University | 9.8659918 |
| Carnegie Mellon University | 7.9750700 |
| University of Washington | 4.5314467 |
| Cornell University | 3.4656622 |
| Princeton University | 2.9223387 |
| University of Texas - Austin | 2.5394603 |
| Columbia University | 2.3110282 |
| University of California - Santa Barbara | 2.0507537 |
| California Institute of Technology | 1.9028543 |
| Georgia Institute of Technology | 1.5902598 |
| University of Illinois at Urbana-Champaign | 1.5324409 |
| University of California - Los Angeles | 1.5238573 |
| University of California - San Diego | 1.2106396 |
| University of Maryland - College Park | 1.1716862 |
| University of Pennsylvania | 1.0691726 |
| Brown University | 1.0167585 |
| University of North Carolina - Chapel Hill | 0.9371394 |
| University of Michigan | 0.9263730 |
| University of Minnesota - Twin Cities | 0.7845679 |
| Harvard University | 0.7668788 |
| New York University | 0.7561730 |
| University of Wisconsin - Madison | 0.7021781 |
| University of Massachusetts - Amherst | 0.6569323 |
| Purdue University | 0.6213802 |
| University of Chicago | 0.6157431 |
| Rice University | 0.6154933 |
| Johns Hopkins University | 0.5860418 |
| University of Virginia | 0.5794159 |
There is nothing shocking, as an outsider, here, which is good. Technical schools are pretty high up, and my current employer is on the list and right next to Harvard. Nobody ever got in trouble for saying their school is as good as Harvard, even when Harvard is–as in CS–not so hot.