Hidden order: how adaptation builds complexity
Hidden order: how adaptation builds complexity
Growing artificial societies: social science from the bottom up
Growing artificial societies: social science from the bottom up
Dynamics of complex systems
Insights into the emergence of convergence in group discussions
ICLS '06 Proceedings of the 7th international conference on Learning sciences
ICLS '06 Proceedings of the 7th international conference on Learning sciences
Proceedings of the 2007 conference on Supporting Learning Flow through Integrative Technologies
CSCL'09 Proceedings of the 9th international conference on Computer supported collaborative learning - Volume 1
A context for collaboration: institutions and the infrastructure for learning
CSCL'09 Proceedings of the 9th international conference on Computer supported collaborative learning - Volume 1
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Seen through the lens of complexity theory, past CSCL research may largely be characterized as small-scale (i.e., small-group) collective dynamics. While this research tradition is substantive and meaningful in its own right, we propose a line of inquiry that seeks to understand computer-supported, large-scale collective dynamics: how large groups of interacting people leverage technology to create emergent organizations (knowledge, structures, norms, values, etc.) at the collective level that are not reducible to any individual, e.g., Wikipedia, online communities etc. How does learning emerge in such large-scale collectives? Understanding the interactional dynamics of large-scale collectives is a critical and an open research question especially in an increasingly participatory, inter-connected, media-convergent culture of today. Recent CSCL research has alluded to this; we, however, develop the case further in terms of what it means for how one conceives learning, as well as methodologies for seeking understandings of how learning emerges in these large-scale networks. In the final analysis, we leverage complexity theory to advance computational agent-based models (ABMs) as part of an integrated, iteratively-validated phenomenological-ABM inquiry cycle to understand emergent phenomenon from the "bottom up".