Please use this identifier to cite or link to this item: https://repository.isls.org//handle/1/3359
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dc.contributor.authorKapur, Manu
dc.contributor.authorHung, David
dc.contributor.authorJacobson, Michael
dc.contributor.authorVoiklis, John
dc.contributor.authorKinzer, Charles
dc.contributor.authorDer-Thanq, and Victor Chen
dc.date.accessioned2019-07-20T23:49:49Z
dc.date.accessioned2020-01-09T18:38:12Z-
dc.date.available2019-07-20T23:49:49Z
dc.date.available2020-01-09T18:38:12Z-
dc.date.issued2007-07
dc.identifier.citationKapur, M., Hung, D., Jacobson, M., Voiklis, J., Kinzer, C., & Der-Thanq, a. C. (2007). Emergence of Learning in Computer-Supported, Large-Scale Collective Dynamics: A Research Agenda. In Chinn, C. A., Erkens, G., & Puntambekar, S. (Eds.), The Computer Supported Collaborative Learning (CSCL) Conference 2007, Volume 8, Part 1 (pp. 323-332). New Brunswick, NJ, USA: International Society of the Learning Sciences.en_US
dc.identifier.urihttps://doi.dx.org/10.22318/cscl2007.323
dc.identifier.urihttps://repository.isls.org//handle/1/3359-
dc.description.abstractSeen 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".en_US
dc.language.isoenen_US
dc.publisherInternational Society of the Learning Sciences, Inc.en_US
dc.titleEmergence of Learning in Computer-Supported, Large-Scale Collective Dynamics: A Research Agendaen_US
dc.typePapersen_US
Appears in Collections:CSCL 2007

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