Toward an Automatic Speech Classifier for the Teacher

Our system classifies audio from microphones worn by the teacher in order to determine (1) whether the teacher is addressing the whole class or talking to individuals or groups of students. In the latter case, it determines (2) whether the teacher is giving formative feedback, giving corrective feed...

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Autores principales: Shahrokhian Ghahfarokhi, Bahar, Sivaraman, Avinash, VanLehn, Kurt
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2020
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7334713/
http://dx.doi.org/10.1007/978-3-030-52240-7_51
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author Shahrokhian Ghahfarokhi, Bahar
Sivaraman, Avinash
VanLehn, Kurt
author_facet Shahrokhian Ghahfarokhi, Bahar
Sivaraman, Avinash
VanLehn, Kurt
author_sort Shahrokhian Ghahfarokhi, Bahar
collection PubMed
description Our system classifies audio from microphones worn by the teacher in order to determine (1) whether the teacher is addressing the whole class or talking to individuals or groups of students. In the latter case, it determines (2) whether the teacher is giving formative feedback, giving corrective feedback, chatting socially, or addressing administrative or workflow concerns. This paper reports the initial accuracy of this system against human coding of middle school math classroom behavior. We also compared audio collected through professional hardware versus more accessible alternatives.
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spelling pubmed-73347132020-07-06 Toward an Automatic Speech Classifier for the Teacher Shahrokhian Ghahfarokhi, Bahar Sivaraman, Avinash VanLehn, Kurt Artificial Intelligence in Education Article Our system classifies audio from microphones worn by the teacher in order to determine (1) whether the teacher is addressing the whole class or talking to individuals or groups of students. In the latter case, it determines (2) whether the teacher is giving formative feedback, giving corrective feedback, chatting socially, or addressing administrative or workflow concerns. This paper reports the initial accuracy of this system against human coding of middle school math classroom behavior. We also compared audio collected through professional hardware versus more accessible alternatives. 2020-06-10 /pmc/articles/PMC7334713/ http://dx.doi.org/10.1007/978-3-030-52240-7_51 Text en © Springer Nature Switzerland AG 2020 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Article
Shahrokhian Ghahfarokhi, Bahar
Sivaraman, Avinash
VanLehn, Kurt
Toward an Automatic Speech Classifier for the Teacher
title Toward an Automatic Speech Classifier for the Teacher
title_full Toward an Automatic Speech Classifier for the Teacher
title_fullStr Toward an Automatic Speech Classifier for the Teacher
title_full_unstemmed Toward an Automatic Speech Classifier for the Teacher
title_short Toward an Automatic Speech Classifier for the Teacher
title_sort toward an automatic speech classifier for the teacher
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7334713/
http://dx.doi.org/10.1007/978-3-030-52240-7_51
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