TY - GEN
T1 - Speech analytics on individual and group audio data to understand collaboration
AU - Rajarathinam, Robin Jephthah
AU - D'Angelo, Cynthia M.
AU - Mercier, Emma
N1 - This material is based upon work supported by the Campus Research Board of the University of Illinois Urbana-Champaign and the National Science Foundation under Grant No.#1628976. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the University of Illinois Urbana-Champaign or the National Science Foundation.
PY - 2022
Y1 - 2022
N2 - Collaborative learning in classrooms require instructors to monitor student groups to ensure they make progress with the tasks. One way learning analytics has helped facilitating such classrooms is by providing speech-based solutions to help instructors monitor. In this poster, we investigate two different ways of collecting audio data from group work namely, group audio data and individual audio data and how voice activity detection (VAD) can be used to predict student collaboration. Both types of audio data were collected from classes focused on collaborative problem solving that were part of an introductory undergraduate engineering course. Preliminary analysis of 8 groups of audio data using VAD indicate that individual audio data could provide information regarding turn ending, turn overlap, and turn duration of individual students which can be critical in understanding the quality of collaboration of a group which cannot be obtained consistently using group audio data.
AB - Collaborative learning in classrooms require instructors to monitor student groups to ensure they make progress with the tasks. One way learning analytics has helped facilitating such classrooms is by providing speech-based solutions to help instructors monitor. In this poster, we investigate two different ways of collecting audio data from group work namely, group audio data and individual audio data and how voice activity detection (VAD) can be used to predict student collaboration. Both types of audio data were collected from classes focused on collaborative problem solving that were part of an introductory undergraduate engineering course. Preliminary analysis of 8 groups of audio data using VAD indicate that individual audio data could provide information regarding turn ending, turn overlap, and turn duration of individual students which can be critical in understanding the quality of collaboration of a group which cannot be obtained consistently using group audio data.
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U2 - 10.22318/cscl2022.599
DO - 10.22318/cscl2022.599
M3 - Conference contribution
AN - SCOPUS:85173579400
T3 - Computer-Supported Collaborative Learning Conference, CSCL
SP - 599
EP - 600
BT - Proceedings of the 15th International Conference on Computer-Supported Collaborative Learning - CSCL 2022
A2 - Weinberger, Armin
A2 - Chen, Wenli
A2 - Hernandez-Leo, Davinia
A2 - Chen, Bodong
PB - International Society of the Learning Sciences (ISLS)
T2 - 15th International Conference on Computer-Supported Collaborative Learning, CSCL 2022
Y2 - 6 June 2022 through 10 June 2022
ER -