Teaching

Module Directorships

In the academic year 25/26 I am directing the following modules:

   

Seminar Companions

I am teaching all of my modules that contain a quantitative component with an online seminar companion. This way, all materials are presented in order and in context. This approach is also environmentally friendlier than weekly paper handouts and allows students to utilise the code I provide more easily.

Please note that these are password-protected and only available to students registered on the respective module.

   

Flashcards

Learning R is a lot like picking up a new language — you have to build up your vocabulary and keep practicing it. To this end, I have created Flashcards which contain the functions covered on each of my modules, divided into weekly content.

   

Shiny Apps

Both for my day-to-day teaching and for my textbook, I have written a few R Shiny applications1 with the following topics:

Please note that these are web-based applications and might therefore take a moment to load.

   

Online Tutorials

Together with a former student and research assistant, Piotr Bogdański, I have written two online tutorials. The first, “Academic Writing with LaTeX”, provides students who are interested in presenting their writing in a professional manner with an introduction to LaTeX. The second, “Analysing Quantitative Data with R”, is an attempt to help students who do not wish to take a formal module, or wish to brush up their skills with an opportunity to learn working with R.

   

Teaching Philosophy

I love teaching methods, research design, and comparative politics. Whilst methods often come with the reputation of being dry or intimidating, I see them as the foundation of political science. They not only allow us to evaluate and critique existing work, but also give us the tools to carry out excellent research ourselves. Without methods, a large part of the comparative politics literature – and indeed much of the discipline – would remain inaccessible. I like to think of methods as opening the door to knowledge. Once that door is open, students are free to explore, evaluate, and ultimately contribute to the debates that matter. This is not only true in the context of university degrees, but also in the world beyond. Methods, and especially the quantitative variety, represent transferable skills which make those who possess them a rare and much sought-after commodity on the labour market.

As a guide for my teaching, I am a big fan of this quote from Albert Einstein: “Everything should be made as simple as possible. But not simpler.” In practice, this means I do not want my students to simply learn how to apply a technique in R or any other software. Instead, I want them to look “under the hood” and understand how the method works. This deeper understanding is what allows them to judge whether the results produced by a statistical program are sensible, and what empowers them to become independent researchers. My ultimate goal is for students to be producers of knowledge, not just consumers. At the same time, I remind them that mastery requires patience. Becoming comfortable with methods takes time and effort, and the learning curve can sometimes feel steep.

I know that many students approach methods with a sense of trepidation, often because of the maths these inevitably involve. To help reduce that anxiety, I aim to provide as many supportive resources as possible. These include videos that students can watch at their own pace, flashcards for key terms and concepts, and detailed glossaries that they can return to throughout the module. But I also believe strongly that teaching is more than just providing materials: it is about building dialogue. In lectures, I involve students by asking questions and encouraging participation. In seminars, I see myself more as a facilitator than a lecturer, creating the space for students to come prepared, drive discussions, and take ownership of their learning. The dialogue continues in office hours where I take the time to work through difficult topics and ensure that each student feels supported and heard.

I also view teaching as a process of constant reflection and adaptation. Many of the features I now use in my teaching – such as flashcards, full glossaries, or interactive activities – have come directly from student feedback. I take seriously what students tell me about what helps them learn and try to adapt accordingly. Teaching, for me, is not static but dynamic. Just as I expect students to develop their skills over time, I also strive to keep improving my teaching practice.

Ultimately, I want my students to leave my classes not only with stronger technical skills, but also with confidence. I want them to see methods not as an obstacle, but as a set of tools that enable them to ask better questions, evaluate evidence critically, and contribute their own ideas to the field. If they leave my courses feeling more curious, more capable, and more empowered to engage with research, then I have done my job.

Footnotes

  1. All apps were written with the assistance of ChatGPT.↩︎