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Emergence of learning in computer-supported, large-scale collective dynamics: A research agenda
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Paper presented at the 7th International Conference on Computer Supported Collaborative Learning (CSCL), New Jersey, USA, 16 – 21 July 2007
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”.
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