As a postdoc, I taught a summer undergraduate course in evolutionary biology, a required course for biology majors. I have also been a graduate teaching assistant for undergraduate evolution and a guest lecturer for a graduate seminar on host/parasite evolution, where I gave an introduction to mathematical models of pathogen epidemiology. In addition to undergraduate evolution, I can also teach graduate or advanced undergraduate courses in the ecology and evolution of infectious disease, social evolution, and microbial evolution.
A major component of my teaching philosophy is to emphasize processes and general principles over collections of facts. In my evolution course, I tried convey how evolution is a set of physical, understandable processes and not the semi-mystical force of popular conception that somehow makes things better. When we covered genetic drift, for example, I doubted undergrads had much first-hand experience with sampling error, so I developed an in-class activity in which students repeatedly sampled and propagated small populations of pennies and dimes. Students divided up into small groups and collected their own data in class. When groups raised their hand to say they only have one type of coin now, and what should they do, it became a moment to discuss the fixation of alleles. When I had the groups enter their data into a spreadsheet and display the results in class, one student actually looked at the plots and exclaimed “Huh”. I believe having students perform the sampling manually on their own, rather than having a computer do it for them, makes the underlying process more real, more immediate, more engaging, and easier to understand.
When covering general principles and processes, I make sure to include in the discussion real biological examples and real empirical data. As much as possible, I try include examples drawn from infectious disease, human biology, and conservation. This helps students see how ecology and evolution are relevant, important, and contemporary. It’s especially motivating for the pre-medical students that make up a large fraction of biology majors at many universities. Even rather abstract discussions, like how natural selection only requires heritable variation in survival or reproduction and not actual DNA, become more compelling when it’s part of a discussion on how selection acts on prion strains.
Mathematics has played an important role in the development of ecology, evolution, and population genetics, yet the undergraduate biology curriculum has traditionally required very little math of students. I myself only acquired a working proficiency with math accidentally, because I was a physics major. As systems biology, genomics, and other high-throughput methods become important tools in the study of biology, so too do the statistical, analytical, and computational methods necessary to make sense of the data. I believe we have reached the point where not only should biology majors be required to learn mathematics beyond calculus, but most biology courses should also have a significant mathematical or computational component.
I also believe we should teach science in science class, not just the knowledge science has created. I try to explicitly show the ways in which science works—discussing, for example, hypothesis testing versus discovery-based inquiry, the advantages and limitations of different kinds of data (experimental, comparative, observational), and the relationship of data to inference. The undergraduates in my evolution course found this material surprisingly challenging, so there appears to be plenty of room for us to improve how we convey the nature and practice of science.
In the future, I would also like to include in my teaching more of the methods recommended by evidence-based pedagogy. Empirical studies of the effectiveness of different teaching methods, primarily in undergraduate physics classes, have shown that seemingly small changes in teaching technique can have surprisingly large effect on student comprehension and retention—breaking up lectures, for example, with short, nongraded, in-class multiple choice quizzes in which the wrong answers are common misconceptions.