Toward an Automatic Speech Classifier for the Teacher

Bahar Shahrokhian Ghahfarokhi, Avinash Sivaraman, Kurt VanLehn

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Scopus citations


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.

Original languageEnglish (US)
Title of host publicationArtificial Intelligence in Education - 21st International Conference, AIED 2020, Proceedings
EditorsIg Ibert Bittencourt, Mutlu Cukurova, Rose Luckin, Kasia Muldner, Eva Millán
Number of pages6
ISBN (Print)9783030522391
StatePublished - 2020
Event21st International Conference on Artificial Intelligence in Education, AIED 2020 - Ifrane, Morocco
Duration: Jul 6 2020Jul 10 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12164 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference21st International Conference on Artificial Intelligence in Education, AIED 2020


  • Educational data mining
  • Intelligent tutoring system
  • Multimodal learning analytics

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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