Identifying Beneficial Learning Behaviors from Large-Scale Interaction Data

Abstract

Understanding the effect of learning behavior is fundamental to improving learning outcomes. In this paper, we perform a behavioral analysis based on data from a large high-stakes exam preparation platform. By measuring the importance of a set of candidate learning behaviors in predicting final exam outcomes, we identify a suite of beneficial behaviors. In particular, we find that breadth (wide coverage of content per week) and intensity together with consistency (frequent and equal-length practice for a limited period) are most predictive of final exam success rate, among eleven studied behaviors.