While there is a growing body of research that explores the integration of music and coding in learning environments, much of this work has either emphasized the technical aspects of computer language design or music as a motivational context within which to learn computer science concepts. In this paper, we report on a study in which five undergraduate students with experience in both music and coding completed two creative musical tasks: one using conventional instruments and tools and one using Python code in an online music + coding environment. Inspired by the work of Christopher Small (1998. Musicking: The Meanings of Performing and Listening. University Press of New England), we describe music + coding as a set of interlocking processes which we call computational musicking and explore how practices from both domains are reimagined in this new hybrid context. We introduce semiotic theories of translation and transcription to make sense of the computational musicking process and describe strategies that participants devised in their creative process.
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Baby Shark to Barracuda: Analyzing Children’s Music Listening Behavior
Music is an important part of childhood development, with online music listening platforms being a significant channel by which children consume music. Children’s offline music listening behavior has been heavily researched, yet relatively few studies explore how their behavior manifests online. In this paper, we use data from LastFM 1 Billion and the Spotify API to explore online music listening behavior of children, ages 6–17, using education levels as lenses for our analysis. Understanding the music listening behavior of children can be used to inform the future design of recommender systems.
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- Award ID(s):
- 1751278
- PAR ID:
- 10316668
- Date Published:
- Journal Name:
- Fifteenth ACM Conference on Recommender Systems
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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