Coenrollment networks and their relationship to grades in undergraduate education

In this paper, we evaluate the complete undergraduate coenrollment network over a decade of education at a large American public university. We provide descriptive properties of the network, demonstrating that the coenrollment networks evaluated follow power-law degree distributions similar to many other large-scale networks; that they reveal strong performance-based assortativity; and that network-based features can significantly improve GPA-based student performance predictors. We then implement a network-based, multi-view classification model to predict students' final course grades. In particular, we adapt a structural modeling approach from [19, 34], whereby we model the university-wide undergraduate coenrollment network as an undirected graph. We compare the performance of our predictor to traditional methods used for grade prediction in undergraduate university courses, and demonstrate that a multi-view ensembling approach outperforms both prior "flat" and network-based models for grade prediction across several classification metrics. These findings demonstrate the usefulness of combining diverse approaches in models of student success, and demonstrate specific network-based modeling strategies which are likely to be most effective for grade prediction.