Data-driven analysis of cooperative activity using a topic model

Abstract

While a groupwork activity is a good tool for learning how to cooperate with others, it requires participants to achieve productive reflection. To develop an information media for supporting reflection of a groupwork activity, we propose a new method of characterizing groupwork scenes captured in a video. It automatically forms a low-level feature space that well approximates given scenes based on a topic model called Latent Dirichlet Allocation (LDA). We define elementary ``interaction words'' to describe participants' cooperative behaviors and ``scenes'' with a collection of them. Because their relation is corresponding to that between ``words'' and ``documents'' assumed in the topic model, it enables LDA to characterize groupwork activity records. In the experimental evaluation, we applied the proposed method to a cooperative groupwork in which participants build a higher tower as possible within a time limit. We confirmed that each groupwork scene is reasonably characterized in the viewpoint of cooperation.