Connecting the Dots: Predicting Student Grade Sequences from Bursty MOOC Interactions over Time

Abstract

In this work, we track the interaction of students across multiple Massive Open Online Courses (MOOCs) on edX. Leveraging the ``burstiness" factor of three of the most commonly exhibited interaction forms made possible by online learning (i.e, video lecture viewing, coursework access and discussion forum posting), we take on the task of predicting student performance (operationalized as grade) across these courses. Specifically, we utilize the probabilistic framework of Conditional Random Fields (CRF) to formalize the problem of predicting the sequence of grades achieved by a student in different MOOCs, taking into account the contextual dependency of this outcome measure on students' general interaction trend across courses. Based on a comparative analysis of the combination of interaction features, our best CRF model can achieve a precision of 0.581, recall of 0.660 and a weighted F-score of 0.560, outweighing several baseline discriminative classifiers applied at each sequence position. These findings have implications for initiating early instructor intervention, so as to engage students along less active interaction dimensions that could be associated with low grades.