Machine Learning Education for Artists, Musicians, and Other Creative Practitioners
- Submitting institution
-
Goldsmiths' College
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 2633
- Type
- D - Journal article
- DOI
-
10.1145/3294008
- Title of journal
- ACM Transactions on Computing Education
- Article number
- 31
- First page
- -
- Volume
- 19
- Issue
- 4
- ISSN
- 1531-4278
- Open access status
- Compliant
- Month of publication
- September
- Year of publication
- 2019
- URL
-
http://research.gold.ac.uk/id/eprint/25213/
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
0
- Research group(s)
-
-
- Citation count
- 2
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This is one of the first articles about machine learning pedagogy to appear in TOCE, and the first about creative ML pedagogy. It describes new learning objectives, teaching strategies, and scaffolding technologies for ML teaching, and examines how these impact student learning. These teaching innovations were used in the world’s first creative ML MOOC, with over 12,000 students. These tools and curricula have been used in workshop and classroom teaching around the world (e.g., Carnegie Mellon, Stanford, Copenhagen Institute of Interaction Design, Glasgow School of Art). Tools developed in this teaching were named as inspiration for Google’s Teachable Machine project.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -