Emergence of Learning in Computer-Supported, Large-Scale Collective Dynamics: A Research Agenda

Abstract

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".