Learning Analytics and Computational Techniques for Detecting and Evaluating Patterns in Learning: An Introduction to the Special Issue

The learning sciences community’s interest in learning analytics (LA) has been growing steadily over the past several years. Three recent symposia on the theme (at the American Educational Research Association 2011 and 2012 annual conferences, and the International Conference of the Learning Sciences 2012), organized by Paulo Blikstein, led to the meeting of learning scientists working in this area and ultimately generated the proposal for this special issue. In the two years that we have worked on putting together this special issue, the task of writing an introduction has become both much simpler and significantly more difficult. On the one hand, many of the trends that are driving the increasing attention to LA— big data, the Cloud—have become so prominent that we can count on readers to have some familiarity with them. Thus, we do not need to start at the beginning in our discussion of LA for the Journal of the Learning Sciences (JLS) audience. On the other hand, the scope of the field and the potential applications have grown tremendously in this short time. The result is that, if anything, we have fallen further behind. Although the educational data mining and LA communities have produced a steady stream of interesting results, work in education has far to go in