Modeling micro-interactions in self-regulated learning: A data-driven methodology

We explore whether interactive navigational behaviours can be used as a reliable and effective source to measure the progress, achievement, and engagement of a learning process. To do this, we propose a data-driven methodology involving sequential pattern mining and thematic analysis of the low-level navigational interactions. We applied the method on an online learning platform which involved 193 students resulting in six interactive behaviours...