Please use this identifier to cite or link to this item: https://repository.isls.org/handle/1/446
Title: CSCL and Learning Analytics: Opportunities to Support Social Interaction, Self-Regulation and Socially Shared Regulation
Authors: Wise, Alyssa Friend
Azevedo, Roger
Stegmann, Karsten
Malmberg, Jonna
Rosé, Carolyn Penstein
Mudrick, Nicholas
Taub, Michelle
Martin, Seth A.
Farnsworth, Jesse
Mu, Jin
Järvenoja, Hanna
Järvelä, Sanna
Wen, Miaomiao
Yang, Diyi
Fischer, Frank
Issue Date: Jul-2015
Publisher: International Society of the Learning Sciences, Inc. [ISLS].
Citation: Wise, A. F., Azevedo, R., Stegmann, K., Malmberg, J., Rosé, C. P., Mudrick, N., Taub, M., Martin, S. A., Farnsworth, J., Mu, J., Järvenoja, H., Järvelä, S., Wen, M., Yang, D., & Fischer, F. (2015). CSCL and Learning Analytics: Opportunities to Support Social Interaction, Self-Regulation and Socially Shared Regulation In Lindwall, O., Häkkinen, P., Koschmann, T. Tchounikine, P. Ludvigsen, S. (Eds.) (2015). Exploring the Material Conditions of Learning: The Computer Supported Collaborative Learning (CSCL) Conference 2015, Volume 2. Gothenburg, Sweden: The International Society of the Learning Sciences.
Abstract: 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’ self- and socially shared- regulation 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.
URI: https://repository.isls.org/handle/1/446
https://doi.dx.org/10.22318/cscl2015.1107
Appears in Collections:CSCL 2015

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