Tracking progress: predictors of students' weekly achievement during a circuits and electronics MOOC

Abstract

Massive open online courses (MOOCs) provide learning materials and automated assessments for large numbers of virtual users. Because every interaction is recorded, we can longitudinally model performance over the course of the class. We create a panel model of achievement in an early MOOC to estimate within- and between-user differences. In this study, we hope to contribute to HCI literature by, first, applying quasi-experimental methods to identify behaviors that may support student learning in a virtual environment, and, second, by using a panel model that takes into account the longitudinal, dynamic nature of a multiple-week class.