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Title: 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 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.
Authors:
; ;
Award ID(s):
1840051
Publication Date:
NSF-PAR ID:
10185621
Journal Name:
Artificial Intelligence in Education, AIED 2020
Page Range or eLocation-ID:
279-284
Sponsoring Org:
National Science Foundation
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