Monitoring the Learning Progress in Piano Playing with Hidden Markov Models

Nina Ziegenbein, Jason Friedman, Alexandra Moringen

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

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.

Original languageEnglish
Title of host publicationUMAP2022 - Adjunct Proceedings of the 30th ACM Conference on User Modeling, Adaptation and Personalization
PublisherAssociation for Computing Machinery, Inc
Pages335-341
Number of pages7
ISBN (Electronic)9781450392327
DOIs
StatePublished - 4 Jul 2022
Event30th ACM Conference on User Modeling, Adaptation and Personalization, UMAP2022 - Virtual, Online, Spain
Duration: 4 Jul 20227 Jul 2022

Publication series

NameUMAP2022 - Adjunct Proceedings of the 30th ACM Conference on User Modeling, Adaptation and Personalization

Conference

Conference30th ACM Conference on User Modeling, Adaptation and Personalization, UMAP2022
Country/TerritorySpain
CityVirtual, Online
Period4/07/227/07/22

Funding

FundersFunder number
German-Israeli Foundation for Scientific Research and Development

    Keywords

    • Hidden Markov Model
    • Human-in-the-loop
    • Intelligent Tutoring and Monitoring System
    • Knowledge Tracing
    • Piano Playing

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