@inproceedings{e8ba3c437011479aae48695f94d185aa,
title = "Monitoring the Learning Progress in Piano Playing with Hidden Markov Models",
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.",
keywords = "Hidden Markov Model, Human-in-the-loop, Intelligent Tutoring and Monitoring System, Knowledge Tracing, Piano Playing",
author = "Nina Ziegenbein and Jason Friedman and Alexandra Moringen",
note = "Publisher Copyright: {\textcopyright} 2022 ACM.; 30th ACM Conference on User Modeling, Adaptation and Personalization, UMAP2022 ; Conference date: 04-07-2022 Through 07-07-2022",
year = "2022",
month = jul,
day = "4",
doi = "https://doi.org/10.1145/3511047.3537666",
language = "الإنجليزيّة",
series = "UMAP2022 - Adjunct Proceedings of the 30th ACM Conference on User Modeling, Adaptation and Personalization",
pages = "335--341",
booktitle = "UMAP2022 - Adjunct Proceedings of the 30th ACM Conference on User Modeling, Adaptation and Personalization",
}