Supporting self-regulated learning with learning analytics

Abstract

In an increasingly information rich world, students are exposed to diverse sources of material to inform their learning. As more formal and informal learning occurs in digital environments, students often need to monitor their own learning, making judgements about their progress and deciding what action to take next. Supporting self-regulated learning is increasingly important in this context but is difficult for teachers, particularly when students are learning online and/or independently. To leverage the affordances of learning analytics to help enhance self-regulated learning, it is critical to understand what trace data from digital environments indicates about student progress. From there, we argue that the most promising data-driven interventions do not attempt to build student capacity directly through feedback but rather nudge and prompt students to consider and reconsider the strategies they are using, how they are judging their progress and to help them make better decisions as they learn.