Optimizing piano practice with a utility-based scaffold
This work addresses the challenge of personalized piano learning without a human teacher, though it is incremental as it builds on existing optimization and modeling techniques.
The paper tackled the problem of optimizing piano practice by dynamically adapting practice tasks to individual learners, proposing a modeling framework that uses a Gaussian process-based utility model to select practice modes with the highest expected improvement in skill, demonstrated through simulations.
A typical part of learning to play the piano is the progression through a series of practice units that focus on individual dimensions of the skill, such as hand coordination, correct posture, or correct timing. Ideally, a focus on a particular practice method should be made in a way to maximize the learner's progress in learning to play the piano. Because we each learn differently, and because there are many choices for possible piano practice tasks and methods, the set of practice tasks should be dynamically adapted to the human learner. However, having a human teacher guide individual practice is not always feasible since it is time consuming, expensive, and not always available. Instead, we suggest to optimize in the space of practice methods, the so-called practice modes. The proposed optimization process takes into account the skills of the individual learner and their history of learning. In this work we present a modeling framework to guide the human learner through the learning process by choosing practice modes that have the highest expected utility (i.e., improvement in piano playing skill). To this end, we propose a human learner utility model based on a Gaussian process, and exemplify the model training and its application for practice scaffolding on an example of simulated human learners.