Using machine learning to support pedagogy in the arts
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ORIGINAL ARTICLE
Using machine learning to support pedagogy in the arts Dan Morris • Rebecca Fiebrink
Received: 20 October 2011 / Accepted: 17 November 2011 Springer-Verlag London Limited 2012
Abstract 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. Keywords
Machine learning Education Creativity
1 Introduction Artists—from hobbyists to professionals, in virtually all artistic disciplines—employ both high-level creative and lower-level sensorimotor skills in their work. Most artists will report that they derive their excitement from high-level creative thinking and that this is the level on which artists collaborate and converse with other artists. However, these
D. Morris (&) Microsoft Research, Redmond, WA, USA e-mail: [email protected] R. Fiebrink Princeton University, Princeton, NJ, USA e-mail: [email protected]
skills depend on a base of sensorimotor skills, and in some cases rote knowledge, that often fade into subconscious thinking as an artist progresses. This presents a unique challenge for education in the arts: A guitarist generally needs to learn basic fingering patterns, which is often tedious and frustrating, before she can even engage in the truly creative or social aspects of musicianship. This challenge is magnified when the student is a child and may be less easily motivated by long-term goals or by friends or colleagues who have developed their skills to the point of long-term value and enjoyment. This challenge is exemplified by a common trend within music education: Many children abandon instrumental education even after years of formal training in scales and technique, before the connections to creativity, social interaction, and ‘‘fun’’ are ever drawn. 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. For example, a painting module might allow a student to explore scene
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