Generating Piano Practice Policy with a Gaussian Process
Track: Innovations in AI for Education (Day 1)
Paper Length: long-paper (6 pages + references)
Keywords: computational scaffolding, accelerating motor learning, learning to play piano, practice modes, computational architecture for learning
TL;DR: learning to play piano guided by a Gaussian Process Teacher
Abstract: A typical process of learning to play a piece on a piano consists of a progression through
a series of practice units that focus on individual dimensions of the skill, the so-called
practice modes. Practice modes in learning to play music comprise a particularly large set
of possibilities, such as hand coordination, posture, articulation, ability to read a music
score, correct timing or pitch, etc. Self-guided practice is known to be suboptimal, and a
model that schedules optimal practice to maximize a learner’s progress still does not exist.
Because we each learn differently and there are many choices for possible piano practice
tasks and methods, the set of practice modes should be dynamically adapted to the human
learner, a process typically guided by a teacher. However, having a human teacher guide
individual practice is not always feasible since it is time-consuming, expensive, and often
unavailable. In this work, we present a modeling framework to guide the human learner
through the learning process by choosing the practice modes generated by a policy model.
To this end, we present a computational architecture building on a Gaussian process that
incorporates 1) the learner state, 2) a policy that selects a suitable practice mode, 3)
performance evaluation, and 4) expert knowledge. The proposed policy model is trained to
approximate the expert-learner interaction during a practice session. In our future work,
we will test different Bayesian optimization techniques, e.g., different acquisition functions,
and evaluate their effect on the learning progress.
Cover Letter: pdf
Submission Number: 35
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