Please use this identifier to cite or link to this item: https://repository.isls.org//handle/1/719
Title: Matching Data-Driven Models of Group Interactions to Video Analysis of Collaborative Problem Solving on Tablet Computers
Authors: Paquette, Luc
Bosch, Nigel
Mercier, Emma
Jung, Jiyoon
Shehab, Saadeddine
Tong, Yurui
Issue Date: Jul-2018
Publisher: International Society of the Learning Sciences, Inc. [ISLS].
Citation: Paquette, L., Bosch, N., Mercier, E., Jung, J., Shehab, S., & Tong, Y. (2018). Matching Data-Driven Models of Group Interactions to Video Analysis of Collaborative Problem Solving on Tablet Computers. In Kay, J. and Luckin, R. (Eds.) Rethinking Learning in the Digital Age: Making the Learning Sciences Count, 13th International Conference of the Learning Sciences (ICLS) 2018, Volume 1. London, UK: International Society of the Learning Sciences.
Abstract: Despite an increasing emphasis on the use of collaborative learning in classrooms, there is still much to be understood about how to successfully implement it. In particular, it is still unclear what the role of teachers should be during collaborative learning activities and how we can better support and guide teachers in their implementation of collaborative activities. In this study, we investigated how digital learning environments can be leveraged to support collaborative learning through data-driven models of students’ collaborative interactions by matching video and log data. The models successfully detected off-task behavior (43.2% above chance-level accuracy) and task-related talk (34.5% above chance) as students solved problems using a collaborative sketching tool. Future work will investigate how these models can be used to allow instructors to intervene effectively to support collaborative learning through the use of data-driven tools which will provide them with live information about the students’ behaviors.
URI: https://doi.dx.org/10.22318/cscl2018.312
https://repository.isls.org//handle/1/719
Appears in Collections:ICLS 2018

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