TY - GEN
T1 - Mapping individual to group level collaboration indicators using speech data
AU - D’angelo, Cynthia M.
AU - Smith, Jennifer
AU - Alozie, Nonye
AU - Tsiartas, Andreas
AU - Richey, Colleen
AU - Bratt, Harry
N1 - Funding Information:
This material is based upon work supported by the National Science Foundation under Grant No. DRL-1432606. 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 National Science Foundation.
PY - 2019
Y1 - 2019
N2 - Automatic detection of collaboration quality from the students’ speech could support teachers in monitoring group dynamics, diagnosing issues, and developing pedagogical intervention plans. To address the challenge of mapping characteristics of individuals’ speech to information about the group, we coded behavioral and learning-related indicators of collaboration at the individual level. In this work, we investigate the feasibility of predicting the quality of collaboration among a group of students working together to solve a math problem from human-labelled collaboration indicators. We use a corpus of 6th, 7th, and 8th grade students working in groups of three to solve math problems collaboratively. Researchers labelled both the group-level collaboration quality during each problem and the student-level collaboration indicators. Results using random forests reveal that the individual indicators of collaboration aid in the prediction of group collaboration quality.
AB - Automatic detection of collaboration quality from the students’ speech could support teachers in monitoring group dynamics, diagnosing issues, and developing pedagogical intervention plans. To address the challenge of mapping characteristics of individuals’ speech to information about the group, we coded behavioral and learning-related indicators of collaboration at the individual level. In this work, we investigate the feasibility of predicting the quality of collaboration among a group of students working together to solve a math problem from human-labelled collaboration indicators. We use a corpus of 6th, 7th, and 8th grade students working in groups of three to solve math problems collaboratively. Researchers labelled both the group-level collaboration quality during each problem and the student-level collaboration indicators. Results using random forests reveal that the individual indicators of collaboration aid in the prediction of group collaboration quality.
UR - http://www.scopus.com/inward/record.url?scp=85073355852&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85073355852&partnerID=8YFLogxK
U2 - 10.22318/cscl2019.628
DO - 10.22318/cscl2019.628
M3 - Conference contribution
AN - SCOPUS:85073355852
T3 - Computer-Supported Collaborative Learning Conference, CSCL
BT - A Wide Lens
A2 - Lund, Kristine
A2 - Niccolai, Gerald P.
A2 - Lavoue, Elise
A2 - Hmelo-Silver, Cindy
A2 - Gweon, Gahgene
A2 - Baker, Michael
PB - International Society of the Learning Sciences (ISLS)
T2 - 13th International Conference on Computer Supported Collaborative Learning - A Wide Lens: Combining Embodied, Enactive, Extended, and Embedded Learning in Collaborative Settings, CSCL 2019
Y2 - 17 June 2019 through 21 June 2019
ER -