Matching data-driven models of group interactions to video analysis of collaborative problem solving on tablet computers

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.