Using Machine Learning to Support Pedagogy in the Arts

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Fiebrink, Rebecca. 2013. Using Machine Learning to Support Pedagogy in the Arts. Personal and Ubiquitous Computing, 17(8), pp. 1631-1635. ISSN 1617-4909 [Article]

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Official URL: https://link.springer.com/article/10.1007%2Fs00779...

Abstract or Description

Teaching artistic skills to children presents a unique challenge: High-level creative and social elements of an artistic discipline are often the most engaging and the most likely to sustain student enthusiasm, but these skills rely on low-level sensorimotor capabilities, and in some cases rote knowledge, which are often tedious to develop. We hypothesize that computer-based learning can play a critical role in connecting “bottom-up” (sensorimotor-first) learning in the arts to “top-down” (creativity-first) learning, by employing machine learning and artificial intelligence techniques that can play the role of the sensorimotor expert. This approach allows learners to experience components of higher-level creativity and social interaction even before developing the prerequisite sensorimotor skills or academic knowledge.

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Article

Identification Number (DOI):

https://doi.org/10.1007/s00779-012-0526-1