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|Title:||Effective Regulation in Collaborative Learning: An Attempt to Determine the Fit of Regulation Challenges and Strategies|
|Publisher:||International Society of the Learning Sciences (ISLS)|
|Citation:||Melzner, N., Greisel, M., Dresel, M., & Kollar, I. (2019). Effective Regulation in Collaborative Learning: An Attempt to Determine the Fit of Regulation Challenges and Strategies. In Lund, K., Niccolai, G. P., Lavoué, 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 1 (pp. 312-319). Lyon, France: International Society of the Learning Sciences.|
|Abstract:||University students often self-organize in groups for collaborative exam preparation. To learn effectively, they need to regulate emerging learning challenges through the choice of fitting strategies. So far, little is known about what strategies fit which challenges. We present a theoretical account for the definition of fit between regulation challenges and strategies in collaborative learning, thereby differentiating between direct and indirect strategies, and test its validity in an empirical study. We asked 163 university students in 90 groups to rate the challenges they encountered in their self-organized group meetings, and to report strategies by aid of which they regulated their biggest challenge. Answers were coded into 26 strategy types. We found that students mostly used strategy types that directly address their biggest challenges. Multilevel-modelling indicated that direct strategies are associated with higher satisfaction for coordination- and comprehension-related challenges only, but not for motivational challenges.|
|Appears in Collections:||CSCL 2019|
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