Deutsche Vereinigung für Politikwissenschaft

Learning, Teaching, and Social Interactions in Times of the COVID-19 Pandemic – Adapting a First-Semester MA/PhD Course to the Online Semester

The course Quantitative Methods (official title: “Multivariate Analysis”) introduces graduate students to quantitative methods in political science research. The course is divided into a lecture (“Multivariate Analysis”) and a computer lab session (“Tutorial Multivariate Analysis”). It is addressed at first semester M.A. and PhD students of political science for whom attendance is mandatory. The course is optional for MMDS (Mannheim Master of Data Science) students. Additionally, students of MMBR (Mannheim Master in Business Research) and PhD Students of Sociology and Psychology, as well as doctoral students at the CDSB and CDSE regularly sign up to take the course on a voluntary basis. In 2020, a total of 49 students attended the course. For the computer lab sessions, students are split into two groups to attend one of two weekly lab sessions.

The main goals of the course are to develop sound critical judgment about quantitative studies of political problems, to interpret quantitative analyses in published work, to understand to logic of statistical inference and to recognize and understand basic regression models. On the practical side, it provides the skills necessary to conduct state-of-the-art quantitative analysis. To that end, the course teaches how to use R, RStudio, and GitHub.

Quantitative Methods 2020 (hereafter QM2020) was, for the first time, taught by a team exclusively composed of junior staff. Owing to the COVID-19 pandemic and the shift from on-site teaching to online teaching, our teaching trio faced considerable challenges with respect to learning, teaching, class organization, and the facilitation of virtual social interactions and mutual learning among the class participants, most of whom were first-year MA or PhD students without pre-existing social or professional networks in Mannheim. To tackle these challenges, we collaboratively adapted the existing class concept and teaching materials to forge a new teaching and learning experience. In doing so, we sought to accommodate the specific didactic, logistical, and social requirements of this unprecedented full-length online semester. In the following, we will therefore keep the discussion of the general class design of Quantitative Methods short. Instead, we will emphasize our specific concept for QM2020 under the banner “Learning, Teaching, and Social Interactions in times of the COVID-19 Pandemic”.

Diverse Teaching Formats

We decided to use a mix of learning platforms and teaching methods to provide for as much interaction as possible while breaking down learning phases into smaller portions, thus countering screen fatigue due to permanent “chalk and talk”-style teaching.

Lectures: The lectures, taught by Denis Cohen, were split into several components. Weekly pre-recorded lecture videos were posted one week in advance of the sessions on YouTube. The lecture recordings were broken down into smaller substantive sections of 10-20 minutes duration and collected in weekly playlists. Students could thus flexibly decide when to watch the lecture recordings and decide to watch them in smaller chunks. This benefitted international students who took the class in different time zones and students with restricted availabilities for learning at home, e.g. due to child care. Questions on, and exchange about, the weekly lecture recordings was enabled by weekly interactive Q&A sessions on Zoom. Student engagement was facilitated in two ways: First, students were asked to post their questions in advance on the ILIAS class forums. Secondly, each Q&A session started with a quiz on the lecture recordings, in which students could answer multiple choice questions via the interactive Mentimeter Live Polling Tool.

Labs: In the weekly lab sessions, Oliver Rittmann and Marcel Neunhoeffer first went through the script for the week for 60 minutes, highlighting the most important material of the week. Then the classroom was flipped for 20 minutes and students were randomly assigned to small groups to collaborate and talk through an applied exercise section every week. In the applied exercises students had to solve problems using the previously introduced concepts and code. These 20 minutes proved to be important and effective. Our goal was to get everyone actively involved and from our experience the breakout sessions achieved exactly that.

Instructor-and-student interactions: Feedback mechanisms and support infrastructure

From the start of the semester, we discussed with our students the unique challenges that came along with teaching and learning exclusively online. One major challenge was that online teaching made it harder for us as instructors to get to know and support our students.

Surveys: We asked students to fill out a short survey on ILIAS during the first week of the semester, in which we asked for some basic details about their current and previous degree programs, their main academic interests, and their familiarity with statistical concepts and software. This not only allowed us to get a first overview of our course participants but also served as the basis for a tool for students to get to know each other (more on this below). We followed up on this with a second ILIAS survey in the fifth week of the semester, in which students could anonymously disclose general struggles they were experiencing while studying from home, rate their satisfaction with our teaching methods and our class overall, and give specific feedback to us.

Virtual Office Hours: Additionally, each of the three instructors held weekly virtual office hours. We actively encouraged students to attend these, e.g. in response to intricate questions posted on ILIAS or in the context of the written feedback on the weekly lab exercises.

Student interactions: Mutual learning and social interactions

Perhaps the biggest challenge was that students could not freely engage with one another. This was not only a challenge for the didactic format of the course, which strongly relied on mutual learning through collaboratively working on weekly exercises. Given that the majority of our students were first-year MA/PhD students without pre-existing social networks in Mannheim, it also presented a significant limitation for students’ social integration into their new programs of study. This applied particular to international students who chose not to move to Mannheim due to the COVID-19 pandemic and were thus joining our online classes from different countries, continents, and time zones. We sought to tackle this challenge in a number of ways.

Collaborative Learning in Rotating Groups: First, we ensured that students would engage with one another in small groups on a weekly basis through mandatory weekly exercises that were to be completed collaboratively in groups of three. The exercises were designed in such a way that they could not easily be split into independent tasks, thus prompting students to actively collaborate. To ensure that students would not exclusively rely on pre-existing social networks and that students with different skill sets and academic backgrounds would support each other, we randomly allocated participants to groups and changed the composition of the groups every three weeks.

The QM2020 Interactive Student Collage: In the context of our short survey on ILIAS in the first week of the semester, we asked students for their consent that we use some basic information (name, current and previous programs of study, academic interests, and profile pictures) from the ILIAS survey to compose an interactive student collage and share it with the class. Specifically, we developed an interactive ShinyApp. In contrast to a static yearbook-style document, this tool allowed participants to get to know each other and to find mutual interests with their fellow students in a fun and interactive way by swiping through fellow students’ profiles.

Virtual Social Events: To also promote and facilitate non-work-related interactions among the students of our class, we held two Virtual Social Events on Zoom in Weeks 4 and 8 of the semester. These allowed students to mingle in an informal setting in Zoom Breakout Rooms.

Open science and service to the community: Public availability of our teaching infrastructure

As scholars who teach quantitative methods in Mannheim, we observe high levels of interest among colleagues from other universities in our expertise on teaching quantitative methods.  While teaching QM2020, we invested an extraordinary amount of effort into adapting our learning materials and teaching concept to the unique challenges of online teaching. As supporters of open science, we are convinced that sharing teaching experiences and expertise serves the scientific community and ultimately helps to improve scientific practice in the long run. We have therefore made our entire teaching materials and tools publicly available online.


  1. Lecture recordings:
  2. Lab materials
  3. ShinyApp QM2020 Interactive Student Collage: We have published the source code of the app in a GitHub repository, which contains extensive documentation, a template for the questionnaire for our ILIAS Survey, code for data processing as well as the source code of our app. Other instructors can use these materials to adapt and use this tool in the context of their classes during the upcoming Spring 2021 online semester. A demo of the App (with profiles of Denis, Marcel, and Oliver), can be found on
  4. Tutorial Teaching Quantitative Social Science in Times of COVID-19: How to Generate and Distribute Individualized Exams with R and RMarkdown at Methods Bites, Blog of the MZES Social Science Data Lab

Über die Autoren

Denis Cohen is a postdoctoral fellow at the Mannheim Centre for European Social Research (MZES), University of Mannheim, who specializes in spatial inequalities, political behavior, party competition and quantitative social science.

Marcel Neunhoeffer is a research associate at LMU Munich, working at the intersection of social science and computer science he is specifically interested in the application of deep learning algorithms to social science problems.

Oliver Rittmann is a Ph.D. Candidate at the Graduate School of Economic and Social Sciences at the University of Mannheim, working on the development and application of methods to automatically analyse video recordings in the context of political science.

Über die Reihe „Herausragende Lehre in der deutschen Politikwissenschaft“

Dieser Beitrag wurde für den Lehrpreis Politikwissenschaft 2021 eingereicht. Der gemeinsame Preis von DVPW und Schader-Stiftung wurde 2020 neu geschaffen, um die besondere Bedeutung der politikwissenschaftlichen Hochschullehre sichtbar zu machen und die Qualität der Lehre in der deutschen Politikwissenschaft zu stärken. Der erste Lehrpreis Politikwissenschaft wurde an Sebastian Möller für sein Forschungsseminar „Schlüssel zur Welt: Die Bremischen Häfen in der Globalen Politischen Ökonomie“ im Sommersemester 2020 an der Universität Bremen verliehen. Die Jury möchte mit dieser Blog-Reihe die Vielzahl der Einreichungen innovativer und didaktisch anspruchsvoller Lehrprojekte würdigen.

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