A proposal for restructuring tenure

An Op-Ed from the NY Times (warning: paywall) suggests creating research and teaching tenure tracks and hire people for one or the other. This is an interesting proposal, and while the author Adam Grant marshals empirical evidence showing that the two skills are largely uncorrelated, as well as research on designing incentives, it seems that the social and economic barriers to implementing such a scheme are quite high.

Firstly, the economic. Grant-funded research faculty bring in big bucks (sometimes more modest bucks for pen-and-paper types) to the university. They overheads (55% at Rutgers, I think) on those grants help keep the university afloat, especially at places which don’t have huge endowments. Research in technology areas can also generate patents, startups, and other vehicles that bring money to the university coffers. This is an incentive for the university to push the research agenda first. Grant funding may be drying up, but it’s still a big money maker.

On the social barriers, it’s simply true in the US that as a society we don’t value teaching very highly. Sure, we complain about the quality of education and its price and so on, but the taxpayers and politicians are not willing to put their money where their mouth is. We see this in the low pay for K-12 teachers and the rise of the $5k-per-class adjunct at the university level. If a university finds that it’s doing well on research but poorly on teaching, the solution-on-the-cheap is to hire more adjuncts.

Of course, the proposal also represents a change, and institutionalized professionals hate change. For what it’s worth, I think it’s a good idea to have more tenure-track teaching positions. However, forcing a choice — research or teaching — is a terrible idea. I do like research, but part of the reason I want to be at a university is to engage with students through the classroom. I may not be the best teacher now, but I want to get better. A better, and more feasible, short-term solution would be to create more opportunities and support for teacher development within the university. This would strengthen the correlation between research and teaching success.

Telecommunications acronyms

When I first got to campus, I arrived a bit earlier than most of the staff and I didn’t have the key to my office. So I went to the Rutgers Math Library to hunker down and read a bit. It was winter break and so the place was empty. The furniture in there is pretty retro — it kind of reminded me of the Urbana Free Library as a kid. While browsing the stacks, I came across this:

Book spine: Computer and Telecommunications Acronyms

A book I saw at the Rutgers Math Library

One of the most… annoying things about wireless communications is the proliferation of acronyms. The one most people haven’t heard of is UE for “User Equipment.” That is, the cellphone or mobile or tablet. The one that everyone has heard of is LTE, for “Long Term Evolution,” an acronym with nearly zero informational content (H(\mathsf{LTE}) \approx 0 for those in the information theory crowd). How “LTE” passed muster with the marketing folks is mysterious to me. Perhaps being more or less meaningless was a plus in their book.

In any case, go to any tutorial on actual wireless technologies (like the LTE tutorial I went to), and it quickly devolves into a soup of acronyms from which few travelers return unscathed. They may be great if you already know what they mean, but it’s a disaster for trying to teach people.

Climate Confidential and new journalism

There’s a lot of talk about how the journalism industry is suffering and soon we’re going to be piled under an avalanche of Buzzfeed lists, reblogs of reblogs, doges.

My friend Celeste LeCompte and her friends have started a new venture called Climate Confidential — they are a collective of journalists and writers who will focus on environmental issues. They’re running a crowdfunding campaign on Beacon, a writer-focused site, to get started. I heartily encourage you lurking blog readers out there to support them.

Non-tenure track faculty at Rutgers get a contract

At Rutgers the faculty are unionized. Recently, the union reached a tentative agreement with the University regarding non-tenure track (NTT) faculty. The full text of the agreement is available now.

In the sciences and engineering, especially at research-focused universities, one often thinks of adjunct faculty as industry folks who come in and teach a class a semester or year. This stands in stark contrast to most departments in the humanities, where adjunct positions are (often) a way to dramatically underpay PhDs by paying them a mere $5k per course without benefits or even office space, sometimes. In the Boston area, the SEIU estimate is that “67 percent of the teaching faculty are not on the tenure track”. I don’t know how they estimated that number, and obviously the SEIU is a bit biased, but the number is certainly large.

Given the way the whole tenure system is going, any steps to provide more stability to adjunct contracts should be welcome. I think the short-term goal is to create more full-time instructional positions with benefits but without tenure. This agreement does something to address that. From an email I received:

Non-grant-funded NTT faculty who are successfully reappointed after six years of full-time service will have appointments of at least two years’ duration thereafter. Departments and decanal units will be required to develop, promulgate and post on their web sites clear criteria for appointment, reappointment, and promotion, and will also be required to provide all non-tenure track faculty with regular performance review and feedback.

Essentially, adjunct contracts were a bit of no-rules scenario before, and this is definitely a better situation.

The other big thing in the contract is to make the job titles more in line with other institutions. There are now 5 classes of non-tenure track faculty: Teaching, Professional Practice, Librarian, Clinical and Research. The first three are new. I’m not sure how the NTT body as a whole feels about this, and in a sense this approach is a capitulation to the trend of having fewer tenure-track faculty, but I think it’s much better than what we have now.

Starting up, and some thoughts on admissions

It’s been a busy January — I finished up a family vacation, moved into a new apartment, helped run the MIT Mystery Hunt, started teaching at Rutgers, and had two conference deadlines back to back. One of my goals for the year is to blog a bit more regularly — I owe some follow-up to my discussion of the MAP perturbation work, which I will be talking about at ITA.

In the meantime, however, one of the big tasks in January is graduate admissions. I helped out with admissions at Berkeley for 4 years, so I’m familiar with reviewing the (mostly international) transcripts, but the level of detail in transcript reporting varies widely. The same is true for letters of recommendation. I’m sure this is culturally mediated, but some recommenders write 1-2 sentences, and some write paeans. This makes calibrating across institutions very difficult. While the tails of the distribution are easy to assess, decisions about the middle are a bit tougher.

Rutgers, like many engineering school across the country, has a large Masters program. Such programs serve as a gateway for foreign engineers to enter the US workforce — it’s much easier to get hired if you’re already here. It’s also makes money for the university, since most students pay their own way. In that regards, Rutgers is a pretty good deal, being a state school. However, it also means making admissions decisions about the middle of the distribution. What one wants is to estimate the probability an applicant will succeed in their Masters level classes.

It’s a challenging problem — without being able to get the same level of detail about the candidates, their schools, and how their recommenders feel about their chances, one is left with a kind of robust estimation problem with a woefully underspecified likelihood. I’ve heard some people (at other schools) discuss GPA cutoffs, but those aren’t calibrated either. More detail about a particular individual doesn’t really help. I think it’s a systemic problem with how graduate applications work in larger programs; our model now appears better suited to small departments with moderate cohort sizes.