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Goldsmiths - University of London

Machine Learning Education for Artists, Musicians, and Other Creative Practitioners

Fiebrink, Rebecca. 2019. Machine Learning Education for Artists, Musicians, and Other Creative Practitioners. ACM Transactions on Computing Education, [Article] (Forthcoming)

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Abstract or Description

This article aims to lay a foundation for the research and practice of machine learning education for creative practitioners. It begins by arguing that it is important to teach machine learning to creative practitioners and to conduct research about this teaching, drawing on related work in creative machine learning, creative computing education, and machine learning education. It then draws on research about design processes in engineering and creative practice to motivate a set of learning objectives for students who wish to design new creative artifacts with machine learning. The article then draws on education research and knowledge of creative computing practices to propose a set of teaching strategies that can be used to support creative computing students in achieving these objectives. Explanations of these strategies are accompanied by concrete descriptions of how they have been employed to develop new lectures and activities, and to design new experiential learning and scaffolding technologies, for teaching some of the first courses in the world focused on teaching machine learning to creative practitioners. The article subsequently draws on data collected from these courses—an online course as well as undergraduate and masters-level courses taught at a university—to begin to understand how this curriculum supported student learning, to understand learners’ challenges and mistakes, and to inform future teaching and research.

Item Type:

Article

Identification Number (DOI):

https://doi.org/10.1145/3294008

Keywords:

Machine learning education, Creative computing, STEAM

Departments, Centres and Research Units:

Computing
Computing > Embodied AudioVisual Interaction Group (EAVI)

Dates:

DateEvent
12 November 2018Accepted
March 2019Published

Item ID:

25213

Date Deposited:

06 Dec 2018 16:21

Last Modified:

06 Dec 2018 16:21

Peer Reviewed:

Yes, this version has been peer-reviewed.

URI:

http://research.gold.ac.uk/id/eprint/25213

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