TY - GEN
T1 - Description of Instructor Intervention Using Individual Audio Data in Co-Located Collaboration
AU - Rajarathinam, Robin Jephthah
AU - D'Angelo, Cynthia M.
N1 - This paper is based upon work supported by the National Science Foundation under Grant No. 1628976 and the Campus Research Board of the University of Illinois Urbana-Champaign. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author and do not necessarily reflect the views of the National Science Foundation or the University of Illinois Urbana-Champaign.
PY - 2023
Y1 - 2023
N2 - Collaborative learning in face-to-face classroom settings requires instructors to monitor student groups and intervene with the group to ensure they make progress with the activity. One way learning analytics could help in facilitating such classrooms is by providing speech-based solutions to help instructors monitor this. In this paper, we investigate audio data collected from individuals in group work, processed using voice activity detection (VAD), used to describe student collaboration before, within, and after instructor intervention. Individual audio data were collected from classes focused on collaborative problem solving that were part of an introductory undergraduate engineering course. Analysis of 22 groups of individual audio data using VAD indicate that individual audio data could provide critical information on how students interact before, within, and after intervention from a facilitator using metrics like turn taking, turn overlap, and turn duration of individual students.
AB - Collaborative learning in face-to-face classroom settings requires instructors to monitor student groups and intervene with the group to ensure they make progress with the activity. One way learning analytics could help in facilitating such classrooms is by providing speech-based solutions to help instructors monitor this. In this paper, we investigate audio data collected from individuals in group work, processed using voice activity detection (VAD), used to describe student collaboration before, within, and after instructor intervention. Individual audio data were collected from classes focused on collaborative problem solving that were part of an introductory undergraduate engineering course. Analysis of 22 groups of individual audio data using VAD indicate that individual audio data could provide critical information on how students interact before, within, and after intervention from a facilitator using metrics like turn taking, turn overlap, and turn duration of individual students.
UR - http://www.scopus.com/inward/record.url?scp=85183891802&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85183891802&partnerID=8YFLogxK
U2 - 10.22318/cscl2023.638306
DO - 10.22318/cscl2023.638306
M3 - Conference contribution
AN - SCOPUS:85183891802
T3 - Computer-Supported Collaborative Learning Conference, CSCL
SP - 317
EP - 320
BT - ISLS Annual Meeting 2023
A2 - Damsa, Crina
A2 - Borge, Marcela
A2 - Koh, Elizabeth
A2 - Worsley, Marcelo
PB - International Society of the Learning Sciences (ISLS)
T2 - 16th International Conference on Computer-Supported Collaborative Learning, CSCL 2023
Y2 - 10 June 2023 through 15 June 2023
ER -