One of the upsides of teaching is that you get a fresh new start every term. Who doesn’t like a clean slate? Especially now that 2020 is in the rear view.
Over the past several months, higher education has been a theater of the pragmatic and the absurd. At this writing, most colleges and universities in the US are planning to return students to campus and hold classes in person, with some kind of fig leaf precautions. At least, that what they’re saying they’re going to do. Looking at the landscape of the COVID infection rate, this makes absolutely no sense.
In sizing up the pandemic plans of most universities, I have no idea how to identify the boundary between denial and deceit.
Bringing people together on campuses is a recipe for spreading the disease. It doesn’t have to do with the dorms, or frat parties, or any of that. It’s just that teaching in classrooms will circulate the virus. This is known.
When we made the switch to online because of the pandemic, I imagine we all were asking ourselves: “How can students learn under these circumstances, and how can I possibly teach well?” Now that we’ve adjusted somewhat, I think now is the time for us to consider another consequential question: Which technological tools might be harming the educational environment of our virtual classroom?” In particular, is it a good idea to implement automated electronic surveillance of our students in this time of crisis?
While I’ve mentioned it briefly in the past, now I’m ready for the full announcement: my book is good to go and is available for pre-order!
One of my goals with this blog is to make evidence-based teaching practices more accessible to scientists who aren’t prepared for a deep dive into educational jargon and theory. I sometimes have been asked to recommend a book that does this, and I couldn’t find one. They say that you should write the book that you think the world needs, so that’s what I did. It’s an outgrowth of Small Pond Science, but it’s all new material.
Some of us have already stopped holding classes in person. It looks like a lot more of us will be making the shift online very soon, as the COVID-19 outbreak will continue to expand in the United States.
We have a couple months left in the semester. I don’t think anybody knows whether campuses that go to online teaching will switch back to campus before the semester is over? It looks like we need to be prepared to stay online through the end of the spring.
This post is for advice.
I taught biostatistics for several years. You know what was one of bigger challenges of teaching that class? Finding articles to use in class that had straightforward application of the statistical principles that we were learning in class.
Let’s fix that! How about we crowdsource a list of articles that have great examples of common statistical concepts for us to use in teaching? I’ve created a google spreadsheet for this, please feel free to add to it! I’ve gotten it off a start with two papers that I’ve used a lot. (They date to the mid 2000s, because, well, that’s when I started teaching biostats, but they’re still great examples.) Please check out, and add to, this spreadsheet!
Here are a couple figures from one of the papers I added to the spreadsheet (Frederickson and Gordon 2007):
Hark, what is that I see? A straightforward example of an ANOVA with a Tukey post-hoc, in the wild? Can it be?
We contain multitudes. Our courses should reflect this.
We contain multitudes. Like an ecological niche, a person’s identity is composed of infinite dimensions that make up a person or group’s collective identity space (Figure 1). However, in science – a discipline that has historically valued objective and unbiased contributors – students and researchers often find it difficult to freely express their identities. Being open and valued because of our identities enhances social justice, makes us more productive, and leads to innovation. Yet, because science is embedded in a biased society, our scientific community is often unwelcoming to people from many backgrounds. Women, people of color, the LGBTQIA+ community, and likely many other groups (that we lack data for) are marginalized or underrepresented relative to their global populations.
Figure 1: A person’s identity, like an ecological niche, is comprised of infinite dimensions, some of which are included in this depiction of “identity space”
Who is doing science goes on to influence the research questions that are pursued and how results are framed. This affects whether marginalized and underrepresented students find science relevant to themselves, which also influences recruitment and retention. For example, biology has been weaponized against marginalized groups throughout history and, in many cases, still is today. Students that see these harmful biases may be alienated from pursuing a career in biology or doing research that is inclusive to their identity. This perpetuates the stereotype of who scientists are and what kind of work they can do, thus contributing to a cycle of exclusion (Figure 2).
Figure 2: Explicit and implicit biases act as a selective force against students from underrepresented groups (akin to stabilizing selection). The low diversity of scientist role models has created the scientist stereotype which further fuels the selective force against students from underrepresented/marginalized backgrounds through mechanisms such as stereotype threat. Made with figures modified from Western Michigan University, Fermi Lab, and Your Article Library.
We need change.
Some folks are surprised to learn that cheating is extremely common. I mean, it’s the norm.
On the other hand, even though our allegedly expect or require us to report all incidents of academic misconduct, faculty generally aren’t doing this. What’s up with that?
Instead of writing about it here, I wrote about it for the Chronicle of Higher Education. If that link gives you a paywall, then this one with Chronicle Vitae should work for you.
You may or may not have heard of this weekend’s debacle from the Proceedings of the National Academy of Sciences (They promoted a paper using images of women appearing to have an orgasm. Though the paper was an ovulation experiment in rabbits. Do they have any women involved in the social media process over there? Yikes.) I hold the new EIC in high regard, and I imagine she’ll get to the bottom of this. But it reminds me of a thing I’ve been meaning to address here for a while.
I suspect a lot of us are teaching animal behavior and behavioral ecology very badly, whenever it comes to our own species.
Well, of course, major research institutions (R1s) expect more research to come out of their labs than primarily undergraduate institutions (PUIs). But, after you take into account the circumstances of each kind of position, who experiences a higher relative demand for research productivity? At which kind of institution is it harder to meet the scholarship criteria for tenure?
Well, let’s compare various factors related to research productivity at these kinds of institutions*.
Science moves faster than curriculum. This means we are leaving some of our students behind.
Even you might not think of your college students as adults, it would help if you treated them as if they were.
I don’t know about you, but I’m used to hearing academics talking about how some people are just inherently brilliant. That there are people with oodles of raw talent, that just needs to be molded, and it’s our job as academia to find them and raise them up.
The entire point of this post is in the title. This idea crossed my path yesterday, and I’d like to share it as widely as possible:
Student evaluations are here to stay. And that’s the way it should be. I think universities owe it to students to provide a structured opportunity to provide feedback on classroom experiences. It’s not a matter of “customer service,” but instead, of respecting students and hearing what they have to say. But the way evaluations are typically structured, they facilitate inappropriate application and interpretation, and they don’t ask what we should be asking.
I’ve heard about some folks who are planning to give extra credit to their students for providing evidence that they voted.
Please don’t do this.
I had some unanticipated teaching challenges last spring, when I was teaching a couple sections of an intro-level organismal biology lab. I was befuddled, because on the lab reports, students were getting some straightforward questions wrong. Remarkably wrong in an unexpected manner, nearly all with the same wrong answer.
I want to share a quick story about something slightly stupid that I did some years ago, while teaching.
If your teaching is at least modestly informed by the scholarship of teaching and learning (and, I dare suggest, it should be), then you are probably aware that frequent assessments are a good thing. Students learn better when they have more opportunities to find out if they’re learning what is being taught.
But — as Meg Duffy pointed out last week — some teaching practices are effective but may not be sustainable because they might just require so much work from professors. This resonated with a lot of people. A lot of us apparently feel a genuine tradeoff between our capacity to teach effectively and the amount of time that we are expected to invest into teaching each of our courses.
Do you love it when students waste office hours with questions that don’t help them learn? Do you want to cultivate anxious emails from students sent at 3 in the morning? Do you want your students to wager their grades by guessing what you think is the most important material?
Then don’t tell your students what is going to be on the exam.
People often ask me what they might to read to get started with teaching science at the college level — or they ask for concrete suggestions about how to do active learning efficiently.
So, here are some book suggestions.
I admit it, I don’t like using the LMS. (The LMS is “learning management system” — the software that universities use for the online component of courses.) My campus is a Blackboard campus. I’m not a fan. Maybe that’s because I haven’t used it a lot.
I’m writing this entire blog post to share one cool tip:
“I like teaching, and I didn’t want the same stress-packed life as the professors in my PhD program, so a faculty position at a teaching-focused university is a good fit for me.”
I’ve heard something like this more times than I can possibly count from grad students, postdocs, and professors. It’s something that I used to say myself. But now I think this statement is built on two big fallacies.
Some folks want to ban laptops from their classrooms, and others are okay with laptops.
This is a perennially annoying discussion in higher ed today. But I think it’s an important issue because it has the potential to really affect learning.
What do I do? Here’s the language in my syllabus for this semester:
I read an interesting piece from a computer science professor at Bucknell, who documented his path to discovering universities “in the middle” — where both research and teaching are valued.
The times have changed, and our curriculum is not keeping up.
In the various majors offered by our Department of Biology, I’m convinced we’re not providing our students the most useful set of quantitative skills. After browsing the catalogs of a variety of other universities, I think we’re not alone.
Over the holidays, I taught my niece how to throw a frisbee with a forehand. It took five minutes, and she totally picked it up. It was awesome. And then we just played catch for a good long while. There may not be a more pleasant thing than throwing a frisbee on warm afternoon in the park with good company*.
When I was a senior in college, I was in a seminar dedicated to a new book, written by a US senator who had just been elected Vice President. The book was Earth in the Balance. It explained the science of carbon pollution, the greenhouse effect, and global climate change. To me, it was a revelation. I was aware of the greenhouse effect, but I didn’t appreciate the magnitude of the problem and the massive global effort it would require, until Gore explained it.
Students learn better when their professors are demanding and have high standards.
People learn even better when these professors are supportive, encouraging, and have confidence in their students.