Teaching

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Teaching philosophy

Free educational content is everywhere, so what a classroom adds is a learning community: structure, feedback, and peers working toward the same goal. My teaching rests on three practices.

Evidence-based, active learning. I trained as a graduate student in the University of Washington Department of Biology, a center for research on active-learning methods in large science courses. Students learn by engaging directly with ideas that challenge their prior thinking: working problems in small groups, confronting gaps in their understanding, and explaining their reasoning out loud. Meta-analyses show that active learning raises exam performance and lowers failure rates compared with traditional lecturing,¹ and that it narrows achievement gaps for students from groups underrepresented in STEM.² Techniques I draw on include peer instruction, cooperative groups, anonymous peer grading, and lecture-free class sessions, evaluated with quiz performance, attendance, grade distributions, and student feedback rather than assumptions such as “learning styles,” which the evidence does not support.³

Data literacy. Much of modern biology arrives as large datasets (sequencing, sensor networks, long-term monitoring), and students increasingly need to work with data to understand how knowledge is produced. I bring coding and data-science concepts into biology lessons, starting with tools that pair R with a point-and-click interface so students can clean, analyze, visualize, and report on a real dataset before they write code from scratch. My open lab notebook and how-to guides, such as ANOVA in R, grew out of the same goal.

Student-led research. The most effective active learning happens when students own a project. As an undergraduate, a course at Friday Harbor Laboratories, with live organisms and time in the field, is where I fell in love with research; I later returned there as a research technologist and instructor. I aim to give students the same experience, connecting their projects to partners in industry, agencies, and tribal natural-resource programs so that the work matters beyond the classroom and opens doors to internships and jobs.

Courses

Guest lectures

• Coastal Oceanography, University of Washington
• Bioinformatics for Environmental Sciences, University of Washington
• Marine Benthic Ecology, University of Washington
• Invertebrate Zoology (2 lectures), Friday Harbor Laboratories

Sample course: BIOL 427 Biomechanics

I was a teaching assistant for BIOL 427 at the University of Washington in autumn 2012 (instructors Tom Daniel and Michael Dickinson), leading the weekly discussion sections. The course takes an engineering view of how organisms are built and how they interact with their physical environment, covering fluid flow and boundary layers, the mechanical properties of biological materials, beam theory, locomotion, and life at high and low Reynolds numbers. The weekly problem sets tie the quantitative material to things students already care about. Two examples:

• Can Spider-Man stop a train? Using the measured stiffness, strength, and density of spider silk, students calculate the minimum thread diameter needed to stop the runaway train in Spider-Man 2, which introduces stress, strain, and energy absorption in biological materials.
• How do tuna breathe? Students apply Bernoulli’s principle to ram ventilation, calculating the pressure difference across the gills, the resulting flow rate, and the mouth opening a swimming tuna needs.

What students said

From anonymous end-of-quarter evaluations.

Laboratory sections

“He has a really good way of explaining things in their simplest term and cutting out complications that I often added to ideas.”

“He knew how to ask questions that forced me to think about different things that still related to the course.”

“The graphs and charts that Matt drew were very helpful. He also guided our experiment ideas without outright giving us answers.”

Lecture-based courses

“Matt always guided you to the answer but didn’t flat out tell you what it was so you could really learn it and come to it on your own.”

“[I] could tell Matt was very passionate about the class and the field.”

“Matt’s alternate explanations were helpful when the provided information was confusing.”

Student evaluations

Student evaluation ratings (University of Washington IASystem) for courses taught at the University of Washington and Friday Harbor Laboratories, which ranged from lecture-based to laboratory and field courses. Ratings are on a scale of 1 (very poor) to 5 (excellent). Each linked report opens the full PDF.

The 2022 FISH 441/541 evaluation uses a different form: overall combined median 4.4 of 5 (adjusted 4.5), 7 of 21 students responding (report).

References

1. Freeman S, Eddy SL, McDonough M, Smith MK, Okoroafor N, Jordt H, and Wenderoth MP (2014). Active learning increases student performance in science, engineering, and mathematics. Proceedings of the National Academy of Sciences 111(23):8410-8415. doi:10.1073/pnas.1319030111
2. Theobald EJ, Hill MJ, Tran E, et al. (2020). Active learning narrows achievement gaps for underrepresented students in undergraduate science, technology, engineering, and math. Proceedings of the National Academy of Sciences 117(12):6476-6483. doi:10.1073/pnas.1916903117
3. Newton PM and Miah M (2017). Evidence-based higher education: is the learning styles ‘myth’ important? Frontiers in Psychology 8:444. doi:10.3389/fpsyg.2017.00444