  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.
