Our semesters end the week before the Memorial Day holiday, which in practice means I don’t really get out from under the end-of-semester pileup and start my summer until that holiday weekend. Which is sort of nice for me, personally, because my birthday usually lands somewhere in or adjacent to the last weekend of May.
This year it’s a big(ger) round number, and there were any number of moments that seem nicely timed to commemorate it: I joined the University’s first in-person commencement since 2019 as a faculty “marshal”, and the passel of students from my spring botany class who were participating actually tracked me down to pose for photos. I introduced the first in-person thesis defense by a Master’s student from my lab, and she gave a fantastic, polished presentation of work that her committee declared outstanding almost the moment she stepped out of the room to let us confer. Next weekend (since I’ve now widened this moment to roughly a fortnight) I’ll run and probably survive my twelfth marathon. And, after I’d stopped checking email before the holiday weekend, I got the letter from the Provost conferring tenure and promotion, a year ahead of schedule.
It’s all nice and gratifying in different ways: good impressions made, mentorship successful, hard work paying off, employment and a raise secured. It’s really shockingly well in line with what I’d have wanted to have happen by this particular round-number birthday, if you’d asked me a decade ago when postdoctoral research was still fresh and exciting and radically more adult than grad school. (I’m in the midst of hiring the lab’s first postdoc right now, yikes.) However, right now my mind is more on the sheer number of things I’m still planning to accomplish: the big plans for the summer, grant and paper writing and course planning, and the speed with which making those plans chopped that vast expanse of teaching-free time into not enough. I’ve gotten a lot done in the last five years, and I’ve got so much more to come.
In their new book Making Scientists: Six Principles for Effective College Teaching, (Harvard University Press, $24.95) Light and Micari argue that undergraduate education in the sciences should go beyond imparting a basic set of knowledge, and make learning science more like the experience of doing scientific research.
If teaching science to undergraduates is also a thing you do, may I suggest you go read the whole thing?◼
But so now that it’s all over, how’d it go? Pretty well, on the overall. As much as Citizen Science is meant to be a crash course in scientific reasoning for Bard’s first-year students, it’s also a crash course in teaching for folks like me, who come to the job with experience as teaching assistants, but not in planning or executing a whole course. And judged solely on that level, Citizen Science is amazing.
Let me run through the numbers again: 12 four-and-a-half-hour days with the same 20 first-year students. I spent a fair bit of my Christmas holiday preparing lesson plans, and ended up reworking almost all of that planning in the last three days before class started. From there on, the average workday was something like:
0700-0800h: Wake, shower, breakfast at cafeteria.
0800-0900h: Last-minute lesson prep; classroom set-up, maybe some frantic final copy-making.
0900-1130h: Morning class period. Ideally, no more than one hour of this is PowerPoint presentations and/or videos of TED talks.
1130-1200h: Clean up, collect oneself, wait for the crush of students to move through the cafeteria.
1200-1300h: Lunch at the cafeteria.
1300-1500h: Afternoon class period. Only start this with a video if you want everyone to immediately fall asleep. Class debates are good in this time slot. Assign homework for the next day.
1500-1600h: Clean up, collect oneself, adjust tomorrow’s plans based on what you covered today.
1600-1730h: Exercise. (There’s a respectable campus gym, or nice trails if the weather’s not terrible.)
1730-1900h: Dinner at the cafeteria.
1900-whenever it’s done. Lesson planning and prep; printing and copying of handouts.
2300h: Bedtime, one hopes.
With variations for a four-day rotation in the wet lab and another in the computer lab, plus a “civic engagement” day in which the first-year students go to a local public school to guest-teach science classes for half a day, that’s pretty much the shape of the course. It was exhausting. Boot camp for college teaching. Learning to swim by jumping into the middle of the Hudson River in January.
But that schedule leaves out a multitude of support. First and foremost, Citizen Science faculty have no other personal responsibility than the teaching. Meals are in the campus cafeteria, which provides just fine. Housing is on campus—yes, my dorm room was tiny and ill-equipped, but it was also right around the corner from my classrooms, the communal faculty workspace, the cafeteria, and the gym. So: no cooking, no commute.
Also, it must be said, the Bard student body is pretty great. There were the inevitable exceptions, but most of my class section were smart, friendly, and willing to at least try to tackle any topic I threw at them. Sometimes they were alarmingly informal, and I had to bend a little to accomodate the local concept of punctuality, but if a classroom full of unknown students is a cliff from which a rookie prof dives, these students were also the trampoline at the bottom.
But most importantly, Citizen Science teaching is collaborative. Intensely collaborative. From the moment I arrived on campus, most of my conversations with other faculty members were about lesson plans: what had worked last year, what spurred an amazing class discussion earlier today, what part of the lab procedure left every student confused and irritated. We all started with a six-inch-thick binder of readings, case studies, and worksheets, and then added our own ideas—and swapped, reworked, cut, and rejiggered each other’s ideas.
For me, the flagship example of this was the computer lab. The resource binder had some material on SIR models of disease spread in a population; I wanted to try and teach my students some of the programming language R. So why not build SIR simulations in R?
One faculty member had already developed a nifty interactive model of disease spread in a simulated social network, which included many of the basic concepts necessary to understand more general models, so I started the computer section with that. Next up was an intro-to-R worksheet I’d banged out over the holidays, which covered exactly the programming concepts necessary to code the model, and nothing more. A couple of other faculty members test-drove that worksheet in their own class sections, which had the computer lab earlier in the schedule than mine.
One night’s reading assignment was Anderson and May (1979) [PDF], the original SIR paper; the next day we walked through the math in class. Then I gave my students a worksheet covering some of the graphing capabilities of R, which another of the R-using faculty had developed as followup to my introduction worksheet. And finally, I walked them through the coding necessary to create a simple SIR recursion simulation, complete with a plot of populaiton dynamics over time.
The result wasn’t unqualified success, by a long shot. Some students bogged down in the programming; many glazed over when I started writing equations on the whiteboard. Almost everyone seemed to like drawing graphs in R, though a lot of folks got frustrated by the technicalities of programming syntax even in that context. In the end, most students were able to at least follow me through coding the SIR model, but that was all we had time to do. Given another go-around, I’d provide more structure in the final stretch, with a worksheet that walks through the model coding and how to use the finished model to test specific hypotheses about epidemic dynamics. Also, I’d probably lead with the graph-making, which was more engaging than just pushing variables around on the command line.
But on the whole, I think it worked. My students coded SIR simulations in R, which actually responded to parameter changes the way they were supposed to, and generated pretty graphs in the process. Several students even told me, afterward, that they’ll use R for graphing in the future.
That outcome was really only possible because there were other faculty working on similar ideas, testing things out for me, sharing their own experience and materials. From what I hear, that’s a resource I can’t expect to have when I start teaching my own “real” courses as a full-fledged faculty member. And yet it’s the biggest reason why Citizen Science left me feeling like, actually, I might be able to pull off this whole professor-ing thing after all.◼
Which is to say, it’s about what you’d expect for a Northeastern liberal arts college which has (1) been around awhile, and (2) has money for Frank Gehry. It’s a nice place to walk around in the winter sunshine, after a morning working in this building:
Reem-Kayden Center for Science and Computation. Photo by jby.
Two days after ringing in the New Year, I had to wake up early to catch an eastbound plane. I’m starting out 2013 not by plunging back into the lab-greenhouse-office rotutine, but with a 3-week guest teaching gig at Bard College in upstate New York, as one of the faculty for Bard’s winter-term course Citizen Science.
Citizen Science is part of the Bard freshman seminar, and it’s primarily meant to help bring students up to a basic level of understanding how science and scientific reasoning work. Since the entire freshman class takes it, Bard brings on about two dozen temporary faculty to teach Citizen Science—and, while there are some elements of the course that are in place before we arrive, each faculty member builds his or her own curriculum.
That makes this my very first effort at building and teaching a course from (more or less) scratch. There’s a lot of starting material to work from, provided by the Bard faculty running the program, and by other CS faculty—course development is highly collaborative. But ultimately, what my students do for the next three weeks is entirely up to me—I have to pick readings, plan four and a half hours of in-class acitivites a day, and figure out appropriate homework assignments.
I spent most of my holiday vacation sketching out plans for the course, but I’ve still been scrambling to pull things together in the three days I’ve been at Bard. CS starts on Monday, but there’s an introduction/opening event this afternoon, at which I’ll meet my students and give them their first assignment, Robert Fisher’s essay “Mathematics of a lady tasting tea.” My class roster shows only three science majors out of 20 students—this will be one long exercise in talking about science with educated people who, after this month, may never set foot in a wet lab again.
I just built my first course webpage, for the Mammalogy lab I’ll be leading for this semester’s teaching assistantship. It pulls together a bunch of resources I developed for the same lab last year—photos of lab specimens taken by students (thanks to a little extra credit for inducement) and Anki decks. Now I need to get started on the slides for my first week’s lecture …
Following up on my previous post about Genius, an electronic flashcard program I was thinking about using as a resource for my students in this semester’s mammalogy lab. (There’s a double benefit here — I’m no mammalogist, so I’m really creating study materials for myself, but it’s nice if I can pass them on to the students.) Anyway, I think I’ve found something better than Genius: Anki.
Anki is basically the same thing as Genius, but with cross-platform compatibility where Genius is Mac-only, and with a utility to find and upload decks of virtual flashcards from a server full of shared user-created material. So I can put the cards online, and any student who installs Anki just has to type “Mammalian” into the search function to use them.
Anki also makes use of “spaced repetition” to schedule individual cards during study sessions; it’s less clear to me how useful that will be. To plan spaced repetitions, Anki doesn’t ask you to type in answers as Genius does, but to recall an answer, reveal the correct one, and rate how easy the recall was. That seems less helpful, but we’ll see how it goes.