Teaching Philosophy

What I have done

The full list is on About. The tutorial is the course I have designed from scratch: its stages, exercises and what I learned from teaching it are on Learning Python, and the ideas behind it on Python Philosophy. I planned the microteaching lesson, on permutations and combinations, by backward design (slides, PDF); the probability example under Lecturing comes from it. The instructor's written feedback praised:

• the opening problem, which "was a great hook, as it was shared prior knowledge and something concrete students could relate to";
• my "use of humor throughout";
• and how I "got students involved throughout the lesson."

One classmate wrote that, as someone who is "NOT a math person," they "walked away having learned what you were trying to teach us."

The feedback also showed me what to improve. I underestimated how hard the mathematics would be for classmates from other fields, the expert blind spot I describe under Feedback: I planned too many learning outcomes for 15 minutes, used terms they did not know, and put too much on each slide. I now plan fewer outcomes per class, define terms before using them, and keep each question in view while students answer it.

Why teach fundamental science

I think of physics as a fundamental science: the study of how nature works at the most basic level. But teaching it is more than teaching its theories and their applications. By studying physics, students learn to extract, condense, express and apply information about the world, both nature and human society. In an information age, where unreliable information is everywhere, that ability and critical thinking matter more than ever. So I aim to teach skills that transfer, to physics majors and others alike, including the mathematics and computing that physics needs.

I used to think teaching was about passing on knowledge. I now think it is about helping students construct their own, so my job is to make each student's learning as good as it can be, not to show what I know. Students will face selection soon enough; in my classes, my aim is to help every one of them go as far as they can.

Lecturing

Multimedia has given students many ways to learn, but I still see lecturing as indispensable, as long as students are not passive receivers. I plan each course by backward design: I set the course goals, break them into learning outcomes, and plan class activities to reach them. The subjects I teach usually move from the concrete to the abstract, and that transition is where independent thinking lies, so I often make it the learning outcome of a lesson. When I introduce probability, for example, I emphasize turning real-world problems into mathematical language. My microteaching lesson opened with one: four singers, four guitarists, four drummers and four bassists play a first set as four bands, then form new bands at random, one of each, for a second set. What is the chance that no one plays with a former bandmate? Counting the ways to form the bands turns the question into arithmetic: 24 of 13,824, about 0.17%.

In class, I often guide students with questions. In my Python course, I asked students to explain example code in their own words, then followed up with questions that helped them recall what they knew and find the right path. To swap the values of two variables, for example, I asked them how they would swap two objects held in their two hands, perhaps with a table nearby to put one of them down: the table plays the part of the temporary variable that a step-by-step swap needs. When they tackled the exercises on their own, they were never stuck at the start, and could always write down what they thought, which gave me something to respond to.

Feedback

As experts, we find it hard to imagine what novices do not know: the curse of knowledge, or expert blind spot. My remedy is feedback that is specific and constructive. When I graded graduate quantum mechanics during my PhD, I gave each student detailed comments, and after each assignment I wrote to the whole class about recurring errors and how to fix them.

Independent thinkers should also be able to judge their own progress. I will use frequent short assessments, so students can check how they are doing against my feedback. They learn not only the material but how to learn, which makes them more confident and more willing to take part in class.

Students come with very different backgrounds. I see the diversity as an asset, since every student brings something to the class. Most students in my Python tutorial were first-time programmers from the humanities and social sciences, so I revised the study guide from their feedback and let each choose a project of their own; in a course, I will meet the same diversity with short surveys at the start and support at several levels.

Mentoring

Mentoring research is part of my teaching too. Students new to research, undergraduates and new graduate students, need the most support. In research, everyone faces the unknown, and even the mentor does not have all the answers. So I explain what research is like and help students set reasonable expectations, because frustration is often the biggest barrier for beginners. And however busy I am, I reply to beginners' questions promptly. My aim is for students to move from running code that others wrote to writing tools of their own for their research.

My own path, from French literature through computer science to astrophysics, makes me especially glad to work with students whose backgrounds are not the usual ones.

If you are a student who would like to work with me, see Working with me; projects I offer are on my Research Directions.

Teaching in the AI era

A goal I find achievable for students: to be able to do something, with or without AI, that others cannot easily learn to do, with or without AI. People, not tools, are accountable for results, and students for what they submit. No assignment is AI-proof, so I discuss AI policies with students early, starting with why; used well, AI can be a patient tutor and a partner in class discussion. More on how I think about AI in teaching, and in my work generally, is on Working with AI.

Growing as a teacher

Higher education keeps changing with society and technology, and so does my teaching. I keep learning teaching methods, follow new educational technology, and ask colleagues for feedback. From time to time I sit in on classes in other disciplines, to remember what it is like to be a novice and to learn from other perspectives. A teacher is always a learner of teaching, and I try to keep a student's mindset. For my college teaching course, I interviewed the best college teacher I had. Through a university teaching initiative, they tried short, open-note assessments in class, and then adopted them. That kind of guided trial and error, shared with colleagues as in research, is how I want to grow as a teacher.

Books that shaped my teaching:

• Susan A. Ambrose et al., How Learning Works: Seven Research-Based Principles for Smart Teaching (2010)
• Ken Bain, What the Best College Teachers Do (2004)
• Grant Wiggins and Jay McTighe, Understanding by Design (2nd ed., 2005)
• James M. Lang, Cheating Lessons: Learning from Academic Dishonesty (2013)
• John C. Bean, Engaging Ideas (2nd ed., 2011)
• Stephen D. Brookfield, Becoming a Critically Reflective Teacher (2nd ed., 2017)

Courses I am prepared to teach

Undergraduate

• Introductory astronomy: stars, galaxies and the Universe, for majors or non-majors
• Core physics courses, such as mechanics, electromagnetism and quantum mechanics
• Computational physics: numerical methods and scientific programming in Python
• Data analysis and statistics for the physical sciences, including how to check results from machine learning and AI

Graduate

• Numerical methods for astrophysics
• Cosmology and large-scale structure, including weak gravitational lensing
• Stellar structure and evolution
• Astronomical images: sampling, point spread functions, noise and reconstruction (a special-topics course)
• Machine learning for physical scientists, with an emphasis on validation
• AI in science: using AI tools in research, and checking what they produce

Plans and outreach

At MIDAS, I hope to run short workshops for students and researchers on high-performance Python, Git workflows and good practice in machine learning, and to mentor students working where a domain science meets AI. I see well-documented open-source software as a kind of outreach, because it lets researchers anywhere take part in large missions.

Notes for students

• What I learned applying for postdocs