{"id":321,"date":"2016-09-07T06:00:29","date_gmt":"2016-09-07T10:00:29","guid":{"rendered":"http:\/\/ecologybits.com\/?p=321"},"modified":"2016-09-06T16:48:39","modified_gmt":"2016-09-06T20:48:39","slug":"the-modern-grad-student-paradox","status":"publish","type":"post","link":"http:\/\/ecologybits.com\/index.php\/2016\/09\/07\/the-modern-grad-student-paradox\/","title":{"rendered":"The Modern Grad Student Paradox"},"content":{"rendered":"

I was sitting in the audience during the discussion of the Hacking Ecology 2.0<\/em> Ignite session<\/a> at ESA this year and Josie Simonis<\/a>, 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\u00a0need to learn a lot of modern skills to succeed as scientists, but those who are their teachers (the faculty) don\u2019t have the skills and knowledge to teach them.<\/p>\n

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 \u201cschool\u201d and \u201cstudent\u201d are used, it\u2019s reasonable to suppose the purpose of a graduate education is to learn. For a good chunk of an American PhD program \u2013 and for full programs in some other countries \u2013 that education doesn\u2019t come in the form of classes, as it does for undergraduate education. Instead, the education is more of an apprenticeship \u2013 more like the residency that medical doctors undertake after all their classes are complete.<\/p>\n

Let\u2019s pretend that the sole purpose of graduate school in ecology is to create scientists who can fill the shoes of their advisors. [1]<\/span> 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.<\/span><\/span> 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\u2019m going to call the modern research world. And all the rest \u2013 the great majority of tenured and tenure-track faculty \u2013 learned how to be scientists during a time when the Internet didn\u2019t exist. (Think about that for a moment…)<\/p>\n

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\u2019s 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 \u2013 taxonomy, for example \u2013 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.<\/p>\n

What all this means is that today\u2019s graduate students need to learn all sorts of things that their advisors can\u2019t teach them. Most advisors haven\u2019t 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\u2019t 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<\/a>. But it is a very real conundrum for grad students.<\/p>\n

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]<\/span> 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.<\/span><\/span> My whole time as a grad student \u2013 and ever since \u2013 I\u2019ve heard a yearning\u00a0from 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\u2019t), there\u2019s 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\u2019t worth it for most people to learn how many teeth different mammal skulls hold or to memorize plant families. [3]<\/span> My emphasis here is on \u201cmost people.\u201d There will always be niches of science in which it\u2019s much more useful to have these facts in one\u2019s head than at one\u2019s fingertips. But those niches are quite small \u2013 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<\/em>\u00a0these sort of (sorry to say it) outdated courses.<\/span><\/span><\/p>\n

One possible solution to the lack of teachers is peer training \u2013 that is, grad students (and others) training grad students. The Software Carpentry<\/a> 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\u2019s a faculty member who is perfect at teaching Needed Skill X. Instead of a class just for students at that teacher\u2019s university, that teacher could open up the class online, allowing participation from students at multiple universities. [4]<\/span> There exists technology to do this, but I\u2019m 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.<\/span><\/span> Perhaps one thing that departments should do is to assign faculty to learn<\/em>\u00a0specific skills so they can teach them to students subsequently. As in: \u201cHey, we\u2019re 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\u2026) 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\u00a0during that time.\u201d<\/p>\n

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 \u2013 these are all timeless skills. But for the hard skills, we need a new paradigm \u2013 one that doesn\u2019t leave graduate students flailing in a research environment that looks very different from the one their\u00a0advisors grew up\u00a0in.<\/p>\n","protected":false},"excerpt":{"rendered":"

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\u00a0need to … <\/p>\n

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