Recomposing Small Learning Groups at Scale—A Data-driven Approach and a Simulation Experiment

Abstract

Group re-composition has thus far been rarely studied. The recent emergence of large scale online learning contexts (e.g. MOOCs) might bring about an opportunity for its application due to the reported high drop-out rate. In this paper, we propose a novel data-driven approach to address the problem of group re-composition. Through a simulation experiment, we saw its capability in decreasing the drop-out rate in groups and bringing more cohesive groups when compared against a random grouping strategy.