From danceable audiobooks and “thorny opportunities

Project: Psychology meets computer science - from the collection to the analysis of complex data

Information about the Interview Series

Spotlight: Data Literacy Teaching Lab

In our series 'Spotlight: Data Literacy Teaching Lab' we talk to teachers whose teaching projects were funded by the Digital and Data Literacy in Teaching Lab (DDLitLab) at the University of Hamburg. What were the innovative ideas of the projects? What were the special didactic and content-related challenges, but also highlights? And are there perhaps concrete tips for other teachers who also want to start a new teaching project and are looking for experience reports? We clarify this and more in our look at and behind the scenes. Spotlight on!

Concept & Production: Julia Pawlowski, Sven Rehder, Simon Steinhauser, unterstützt von Laura Aguilera

How does one’s own spontaneous mental state influence music listening behavior? Students of computer science and psychology came together in this interdisciplinary project seminar to learn about data collection and execution from scratch using Python and machine learning via Spotify and smartwatches. The goal: to hack music taste for mental health! In the process, they not only gained valuable practical knowledge for their future careers, but also learned that setbacks and overcoming obstacles are simply part of working with complex data.

Dr. Larissa Gebken and Dr. Matthias Pillny, together with Habiba Schiller, Junbo Huang, and Tibebu Tesfaye Biru, developed the interdisciplinary teaching project “Psychology Meets Computer Science – From Collection to Analysis of Complex Data” and successfully implemented it in 2023 and 2024. The teaching project was funded by the Digital and Data Literacy in Teaching Lab (DDLitLab for short) and took place at both the Department of Computer Science and the Department of Psychology at the University of Hamburg.

The goal: to introduce psychology and computer science students to practical, realistic, and career-preparatory collection and analysis of complex data.

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