Predicting student performance in a blended MOOC

Predicting student performance is a major tool in learning analytics. This study aims to identify how different measures of massive open online course (MOOC) data can be used to identify points of improvement in MOOCs. In the context of MOOCs, student performance is often defined as course completion. However, students could have other learning objectives than MOOC completion. Therefore, we define student performance as obtaining personal learning objective(s). This study examines a subsample of students in a graduate-level blended MOOC who shared on-campus course completion as a learning objective. Aggregated activity frequencies, specific course item frequencies, and order of activities were analysed to predict student performance using correlations, multiple regressions, and process mining. All aggregated MOOC activity frequencies related positively to on-campus exam grade. However, this relation is less clear when controlling for past performance. In total, 65% of the specific course items showed significant correlations with final exam grade. Students who passed the course spread their learning over more days compared with students who failed. Little difference was found in the order of activities within the MOOC between students who passed and who failed. The results are combined with course evaluations to identify points of improvement within the MOOC.