Seminar: Empirical Methods for Music Research🎼🔬

This was constructed as a two-semester course for undergraduate students majoring in Music Technology, who have basic knowledge in Python coding, audio data manipulation using digital audio workstation (DAW), and general music theory at the Karlsruhe University for Music [Hochschule für Musik Karlsruhe]. This was a part of the Music and Neuroscience Module organized by Prof. Marc Bangert. For the niche position of the major (Music Technology) and the course (Music [Neuro-]Science), the number of students was 2 to 6, which made one-on-one discussion on students’ progress feasible.

I taught the course for two years from 2022 to 2024, after Dr. Anna Czepiel who taught the course before me for also two years. The first semester [2022WS] was constructed as a typical seminar course where I gave lectures and students take turns presenting research papers and inviting others to discussions. Then I felt most of the information delivered in lectures was irrelevant to most of the six students. Thus, in the next semester [2023SS], I made it very relevant to students by setting a goal to design, implement, run, analyze, and write their own study. This change made four students withdraw from the course.

Learned from this year, and in the next year, I started early. In the first semester [2023WS], three students focused on developing study ideas, designing online experiments, and implementing them. Only two students were able to submit the term paper in the second semester [2024SS].

While my approach was definitely demanding and clearly not for everyone, I am happy that it seems that it encouraged some students to learn more. This, however, is extremely difficult to tell without a proper causal inference (did they stay because they had been already interested, or did my teaching somehow interested them?). What I can tell easily, though, is that I enjoyed the hard way.

Syllabi

Lecture Materials

Open Science Framework project