Optimizing piano practice with a utility-based scaffold

TL;DR

This paper introduces a framework for optimizing piano practice by dynamically selecting practice modes tailored to individual learners, using a utility model based on Gaussian processes to enhance skill acquisition.

Contribution

It presents a novel modeling framework that adapts practice modes to individual learners' skills and history, aiming to maximize learning efficiency.

Findings

Utility-based practice mode selection improves learning outcomes.

Gaussian process model effectively predicts learner progress.

Simulated experiments demonstrate the framework's potential.

Abstract

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.