Turn-taking analysis of small group collaboration in an engineering discussion classroom

Abstract

This preliminary study focuses on using voice activity detection (VAD) algorithms to extract turn information of small group work detected from recorded individual audio stream data from undergraduate engineering discussion sections. Video data along with audio were manually coded for collaborative behavior of students and teacher-student interaction. We found that individual audio data can be used to obtain features that can describe group work in noisy classrooms. We observed patterns in student turn taking and talk duration during various sections of the classroom which matched with the video coded data. Results show that high quality individual audio data can be effective in describing collaborative processes that occurs in the classroom. Future directions on using prosodic features and implications on how we can conceptualize collaborative group work using audio data are discussed.