Mapping Individual to Group Level Collaboration Indicators Using Speech Data

, , ,

Citation

D’Angelo, C., Smith, J., Alozie, N., Tsiartas, A., Richey, C., & Bratt, H. (2019). Mapping Individual to Group Level Collaboration Indicators Using Speech Data. In Lund, K., Niccolai, G. P., LavoueĢ, E., Hmelo-Silver, C., Gweon, G., & Baker, M. (Eds.), A Wide Lens: Combining Embodied, Enactive, Extended, and Embedded Learning in Collaborative Settings, 13th International Conference on Computer Supported Collaborative Learning (CSCL) 2019, Volume 2 (pp. 628-631). Lyon, France: International Society of the Learning Sciences.

Abstract

Automatic detection of collaboration quality from the studentsā€™ speech could support teachers in monitoring group dynamics, diagnosing issues, and developing pedagogical intervention plans. To address the challenge of mapping characteristics of individualsā€™ 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.


Read more from SRI