It is an exhilarating and important time for conducting research on learning, with unprecedented quantities of data available. There is a danger, however, in thinking that with enough data, the numbers speak for themselves. In fact, with larger amounts of data, theory plays an ever-more critical role in analysis. In this introduction to the…

The article, "Distributed Revisiting: An Analytic for Retention of Coherent Science Learning" is an interesting study that operates at the intersection of learning theory and learning analytics. The authors observe that the relationship between learning theory and research in the learning analytics field is constrained by several…

In this commentary on Van Leeuwen (2015, this issue), I explore the relation between theory and practice in learning analytics. Specifically, I caution against adhering to one specific theoretical doctrine while ignoring others, suggest deeper applications of cognitive load theory to understanding teaching with analytics tools, and comment on…

A long history of laboratory and field experiments have demonstrated that dividing study time into many sessions is often superior to massing study time into few sessions, a phenomenon known as the "spacing effect." We use this well-established finding from the psychology literature as inspiration for investigating how students…

In Miyamoto et al. (2015, this issue) the authors looked to substantiate the presence of the spacing effect, referenced from the psychology literature, in several MOOCs. Their secondary analyses constituted a robust, empirical finding on the correspondence between session distribution and certification but with only a coarse, analogous…

Learning analytics (LA) are summaries, visualizations, and analyses of student data that could improve learning in multiple ways, for example by supporting teachers. However, not much research is available yet concerning how LA may support teachers to diagnose student progress and to intervene during student learning activities. There is evidence…