Computational thinking in music: A data-driven general education steam course

Abstract

This poster outlines the design and results of a course entitled Computational Thinking in Music. The course teaches computational thinking principles as a general education objective to undergraduate students, using data-driven investigation to inform musical composition. Students compose a song to imitate an artist of their choice by analyzing data extracted from a corpus of crowd-sourced pop song transcriptions. Students learn principles of abstraction, decomposition, and algorithmic thinking; no coding experience is required. Quantitative and qualitative results indicate that students are learning and applying computational thinking principles. Since the course is designed and taught by a musician and is run in the music department, students also learn a significant amount of music theory and composition, including harmonic structures and harmonization principles, melodic organization, consonance and dissonance, aural analysis of formal structures and meter, and influence of rhythm and timbre to create desired sounds.