Passionate about teaching
University teaching requires knowledge and knowhow and many more competences to meet the high quality standards and the students needs. Having received the Golden Owl for excellence in teaching in 2022, Roman Vetter in this interview shares his tipps, tricks and vision for teaching.
Roman, in 2022 you received the Golden Owl for excellence in teaching. What makes your teaching excellent..?
I feel very honoured to have received this award. It is a very nice recognition coming directly from the students. I am teaching “Introduction to scientific computing”, which is a new course that I developed three years ago. It is an advanced-level course, offered to CBB {Computational Biology and Bioinformatics} and Biotechnology Master’s students. What became clear in student evaluation and from student feedback is that there are basically three main aspects that allowed me to receive this nice recognition:
Firstly, and this is the most important aspect, I try to build a script, a set of lecture notes that is in as high quality as I could get it. I always like to think back about my own past as a student at ETH in Zurich at that time; I always enjoyed lectures best if they were accompanied by something that lasts, something that you could look up again later, something that is well-designed, something that gives the lectures good structure - such as colour codes that always mark the same topics, something that you could hold on to.
Secondly, I try to pick up the students where their interests and backgrounds really are. For example, I give them programming examples, pieces of scripts, in PYTHON, which is nowadays the most popular programming language. That is a compromise to really spark their interest in what I am teaching, and allows them to connect to what they already know about programming.
The third aspect is that I always try to take my time to explain the theoretical concepts, because that is the essence. In the end I want them to learn something, to take something home, and I can do that best if I illustrate theoretical concepts along the way, with examples on the same slide.
There are of course additional points such as that I am very fortunate to teach small classes, which is really beneficial to the students and to me, i.e. there are only about 10 students that attend in person. This makes the course very interactive. I let them interrupt me, they can discuss with me and it becomes more like a dialogue, which makes the class very engaging to them I hope. I try to be accessible, try to provide help and feedback via email whenever needed.
One last thing I would like to mention is that I have a hybrid approach to teaching: I give the lecture in person, at the same time I stream the lecture on Zoom and record it. This recorded video I upload to Moodle later on, making it available to all students. According to feedback, this approach is really appreciated by the students.
What is the relation between theory and practical part?
I try to keep it balanced. The course was initiated a few years ago partly based on feedback from students who said that they wish for a theoretical foundation for their computational work. This is what we try to provide with this course. The way the course is structured is the following: I teach theoretical concepts and this part is followed by a block of two weeks of homework assignments with practical hands-on programming tasks. This is important to me: I try to keep the balance and offer a practical part right after the theoretical part.
During Corona times - but also today - I use Zoom polls in my lectures, that directly ask for feedback how it went: if the theory or the practical exercises where too difficult or unbalanced. That is a very valuable tool I believe.
ETH Zurich values critical thinking and independency in education and research. How do you achieve this requirement?
This is a key ingredient of my lecture, at least I try to make it one: I want them to become a critical thinker and an independent researcher. And this makes up even half of the lecture: to understand program output, simulation output, and to question the validity and the confidence and the errors that could be contained in it. I also foster independency in the sense that students are given homework assignments that are small projects to be completed during the semester. Typically, these projects take two weeks. They present their results to their peers, allowing them to discuss and share their ideas. This is very beneficial for all, because they hopefully learn from each other.
You recently secured an Innovedum grant. What about future education, in what way will your way of lecturing evolve…?
Indeed, this Innovedum project is ongoing. It is a small project allowing me to hire one teaching assistant. The idea is to make the lecture more interactive. Currently, my lecturing is still pretty much unidirectional: I teach some theoretical concepts, which may trigger some discussions. But my goal is to make this part more interactive and allow the students to play around with some parameters and explore the consequences of those actions. ETH Zurich’s department for Educational Development and Technology is offering this Innovedum grant, in which we develop interactive tools to complement what I have statically in my lecture notes (plots, graphics, diagrams etc.) to make them more interactive. Each of these illustrations is turned into a PYTHON script hosted on JupyterHub, which allows the students to change parameters and observe what happens. This project will last until the end of next semester. Applying for an Innovedum grant is something I can highly recommend to all teachers at ETH Zürich: you simply hand in a short proposal for an Innovedum project, it is easy to do and allows you to improve your courses in various ways, from very small to medium or larger-sized projects.
Thank you very much, Roman, for these very inspirational insights into teaching and lecturing!
This interview was conducted as part of the Digital Campus held on 30 May.
Roman Vetter is Senior researcher in the Computational Biology group led by Dagmar Iber and lecturer in the Computational Biology and Bioinformatics Master's programme at D-BSSE.