Data science foundry for MOOCs

Abstract

In this paper, we present the concept of data science foundry for data from Massive Open Online Courses. In the foundry we present a series of software modules that transform the data into different representations. Ultimately, each online learner is represented using a set of variables that capture his/her online behavior. These variables are captured longitudinally over an interval. Using this representation we then build a predictive analytics stack that is able to predict online learners behavior as the course progresses in real time. To demonstrate the efficacy of the foundry, we attempt to solve an important prediction problem for Massive Open Online Courses (MOOCs): who is likely to stopout? Across a multitude of courses, with our complex per-student behavioral variables, we achieve a predictive accuracy of 0.7 AUCROC and higher for a one-week-ahead prediction problem. For a two-to-three-weeks-ahead prediction problem, we are able to achieve 0.6 AUCROC. We validate, via transfer learning, that these predictive models can be used in real time. We also demonstrate that we can protect the models using privacy-preserving mechanisms without losing any predictive accuracy.