Monitoring the Learning Progress in Piano Playing with Hidden Markov Models
Pages 335 - 341
Abstract
Monitoring a learner’s performance during practice plays an important role in scaffolding. It helps with scheduling suitable practice exercises and, by doing so, sustains learner motivation and steady learning progress while moving through the curriculum. In this paper we present our approach for monitoring the learning progress of students learning to play piano with Hidden Markov Models. First, we present and implement the so-called practice modes, practice units that are derived from the original task by reducing its complexity and focusing on one or several relevant task dimensions. Second, for each practice mode, a Hidden Markov Model is trained to predict whether the player is in the Mastered or NonMastered latent state regarding the current task and practice mode.
Supplementary Material
Monitoring a learner?s performance during practice plays an impor- tant role in scaffolding. It helps with scheduling suitable practice exercises and, by doing so, sustains learner motivation and steady learning progress while moving through the curriculum. In this pa- per we present our approach for monitoring the learning progress of students learning to play piano with Hidden Markov Models. First, we present and implement the so-called practice modes, prac- tice units that are derived from the original task by reducing its complexity and focusing on one or several relevant task dimensions. Second, for each practice mode, a Hidden Markov Model is trained to predict whether the player is in the Mastered or NonMastered latent state regarding the current task and practice mode.
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July 2022
409 pages
ISBN:9781450392327
DOI:10.1145/3511047
- General Chairs:
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Published: 04 July 2022
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UMAP '22: 30th ACM Conference on User Modeling, Adaptation and Personalization
July 4 - 7, 2022
Barcelona, Spain
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