Audio Analysis of Teacher Interactions With Small Groups in Classrooms

Abstract

This paper presents exploratory work in combining computational methods with qualitative approaches in order to better understand teacher interactions with small groups of students. Classroom audio data is typically difficult to work with, due to background noise and challenging acoustics, and needs customization of algorithm parameters when using audio processing tools for speech detection. This secondary data analysis study looked at patterns in small group discussions of students over multiple class sessions and multiple teachers, especially focusing on times when teachers interacted with the groups. This type of approach can augment and extend the capabilities of qualitative researchers, who could use these computationally-derived analytics and patterns to aid them in better understanding teacher/student interactions and collaborative learning.