The Modern Grad Student Paradox

I was sitting in the audience during the discussion of the Hacking Ecology 2.0 Ignite session at ESA this year and Josie Simonis, who was on the panel, said something that really resonated with the grad students in the audience and on Twitter. They said that graduate students face a real paradox: grad students need to learn a lot of modern skills to succeed as scientists, but those who are their teachers (the faculty) don’t have the skills and knowledge to teach them.

What is the purpose of graduate school? It seems like a straightforward question at first, but for those pursuing graduate degrees in the sciences, at least, I think the answer is a lot more complex than it used to be. Because the words “school” and “student” are used, it’s reasonable to suppose the purpose of a graduate education is to learn. For a good chunk of an American PhD program – and for full programs in some other countries – that education doesn’t come in the form of classes, as it does for undergraduate education. Instead, the education is more of an apprenticeship – more like the residency that medical doctors undertake after all their classes are complete.

Let’s pretend that the sole purpose of graduate school in ecology is to create scientists who can fill the shoes of their advisors. [1] This is obviously false, as there are far more PhDs created than R1 faculty jobs that can absorb them. But for the purpose of the post, I want to focus on just the academic path. A newly minted assistant professor today probably spent about 10 years as a graduate student and postdoc. That puts the beginning of their professional training around 2005 (give or take), which is just shortly after the Internet took off as a ubiquitous agent of change. So only the very newest advisors came of (professional) age in what I’m going to call the modern research world. And all the rest – the great majority of tenured and tenure-track faculty – learned how to be scientists during a time when the Internet didn’t exist. (Think about that for a moment…)

I use the Internet as a yardstick, as well as an important driver of research culture. There are a lot of other technologies that have undergone enormous change in the past decade, too. Whatever your particular study system is, it’s likely that there are technological devices, tools, or machines that affect how research in that system has changed over the past decade. And even if your research is bare-bones basic – taxonomy, for example – you have still been affected. The plunging price of computer memory and processing power means that how scientific data is recorded, managed, curated, and accessed has changed.

What all this means is that today’s graduate students need to learn all sorts of things that their advisors can’t teach them. Most advisors haven’t had the time (and in some cases the inclination) to keep up with advances in hardware technology, data standards, software, statistics, and communication. I don’t see this as a shortcoming on the part of the advisors, by the way. Instead I see it as a manifestation of the 12 Hats Problem. But it is a very real conundrum for grad students.

What to do about this paradox? I think the first thing to do is to really assess whether graduate programs are meeting the needs of their students. [2] In this, of course, they need to consider not just those aiming for R1 faculty positions, but also students who will take other types of jobs. My whole time as a grad student – and ever since – I’ve heard a yearning from graduate students for more courses in coding and data management and ecologically relevant statistics. Even if there are teachers for these types of courses (and there often aren’t), there’s always the question of what part of the formal education to drop. My suggestion is to drop or condense requirements that focus on memorization. These days, with the Internet, one can look up a factual piece of information in moments. It simply isn’t worth it for most people to learn how many teeth different mammal skulls hold or to memorize plant families. [3] My emphasis here is on “most people.” There will always be niches of science in which it’s much more useful to have these facts in one’s head than at one’s fingertips. But those niches are quite small – not enough for entire courses. And those who need to memorize this information can do so in the apprentice part of the PhD, rather than the classroom part. I think objections to this come mostly from those who like teaching these sort of (sorry to say it) outdated courses.

One possible solution to the lack of teachers is peer training – that is, grad students (and others) training grad students. The Software Carpentry model is one to consider, in which grad students are trained as teachers and then team teach other students coding skills. Short courses and workshops also fill this gap, but have the downsides of typically being expensive to attend and requiring travel (which disenfranchises some groups of students). Another possibility is to leverage online cross-institution training. Perhaps, for example, there’s a faculty member who is perfect at teaching Needed Skill X. Instead of a class just for students at that teacher’s university, that teacher could open up the class online, allowing participation from students at multiple universities. [4] There exists technology to do this, but I’m relatively unfamiliar with it. For cross-university courses to catch on widely, such technology needs to be rather glitch-free and easy to use. Administrative matters, such as course credits and tuition, need to be addressed too. Perhaps one thing that departments should do is to assign faculty to learn specific skills so they can teach them to students subsequently. As in: “Hey, we’re eliminating your course load this year, but in exchange, we expect you to learn the latest in hierarchical Bayesian Statistics (or R coding or database creation and administration or…) and develop a graduate-level course (or workshop or whatever). You will be teaching it for the next five years, and you will be considered the department expert during that time.”

The apprenticeship part of the PhD still confers many important skills. Being able to read the literature critically, being able to ask a good research question, being able to think logically, being able to write – these are all timeless skills. But for the hard skills, we need a new paradigm – one that doesn’t leave graduate students flailing in a research environment that looks very different from the one their advisors grew up in.

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