CSCL and Learning Analytics: Opportunities to Support Social Interaction, Self-Regulation and Socially Shared Regulation

Research has generated deep insights into computer-supported collaborative learning (CSCL), but the cycle of impact on practice is relatively lengthy and slow. In contrast, work in learning analytics attempts to leverage the collection and analysis of data to improve learning processes and outcomes in-situ. Developing learning analytics to support CSCL thus offers the opportunity to make our research actionable in an immediate way by using data collected on collaborative processes in-progress to inform their future trajectories. Efforts in this direction are specifically promising in support of students’ selfand socially sharedregulation of their learning. Data on collaborative and metacognitive activities can inform collaborating groups and help them to improve future joint efforts. In this symposium we bring together a collection of five papers that are exploring the space of connection between CSCL, learning analytics and self-regulation to advance thinking around these issues.

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