What to do with actionable intelligence: E2Coach as an intervention engine

Abstract

In this paper, we describe a new, analytics driven approach to supporting students in large introductory physics courses. For this project, we have assembled data for more than 49,000 physics students at the University of Michigan. For each, we combine an extensive portrait of background and preparation with details of progress through the course and final outcome. This information allows us to construct models predicting student performance with a dispersion of half a letter grade. We explore residuals to this model, conducting structured interviews with students who did better (and worse) than expected, identifying strategies which lead to student success (and failure) at all levels of preparation. This work was done in preparation for the launch of E2Coach: a computer tailored educational coaching project which provides a model for an intervention engine, capable of dealing with actionable information for thousands of students.