Analytics has become a trend over the last several years, reflected in large numbers of graduate programs promising to make someone a master of analytics, proclamations that analytics skills offer lucrative employment opportunities (Manyika et al., 2011), and airport waiting lounges filled with advertisements from different consultancies promising to significantly increase profits through analytics. When applied to education, these methodologies are referred to as learning analytics (LA) and educational data mining (EDM). In this chapter, the authors focus on the shared similarities as we review both parallel areas, while also noting some important differences. In the sections that follow, the authors argue that learning analytics has the potential to substantially increase the sophistication of how the field of learning sciences understands learning, contributing both to theory and practice. We have downloaded a copy of this Columbia University’s resource for your viewing.
Educational Data Mining and Learning Analytics