Recognizing creative thinking in graphical e-discussions using artificial intelligence graph-matching techniques

TL;DR: The value of a dialogic analytic framework including creative emergence was tested through applying it to the coding and analysis of undergraduate synchronous e-discussions using a graphical interface within the EU funded project ARGUNAUT and found that critical reasoning functioned to 'deepen' the graph through unpacking assumptions whilst creative emergence of new perspectives produced 'widening' moves.

Abstract: Many approaches to analyzing online argumentation focus on explicit reasoning and overlook the creative emergence of new ideas. The value of a dialogic analytic framework including creative emergence was tested through applying it to the coding and analysis of undergraduate synchronous e-discussions using a graphical interface within the EU funded project ARGUNAUT. Qualitative analysis found that critical reasoning functioned to 'deepen' the graph through unpacking assumptions whilst creative emergence of new perspectives produced 'widening' moves. This distinction between deepening and widening was successfully used as the basis for an artificial intelligence (AI) graph-matching algorithm. Given examples of deepening and widening from real e-discussions, the AI algorithm was able to successfully find other occurrences of such moves within new e-discussions. This supports our claim to distinguish between these two aspects of shared thinking and has the potential to provide awareness indicators as a support for e-moderation.