Learn From Your (Markov) Neighbor: Coenrollment, Assortativity, and Grade Prediction in Undergraduate Courses

Abstract

In this paper, we evaluate the complete undergraduate co-enrollment network over a decade of education at a large American public university. We provide descriptive and exploratory analyses of the network, demonstrating that the co-enrollment 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 improve GPA-based student performance predictors. We model the university-wide undergraduate co-enrollment network as an undirected graph, and implement multiple network-augmented approaches to student grade prediction, including an adaption of the structural modelling approach from (Getoor, 2005,  Lu, 2003}. We compare the performance of this 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 modelling strategies that are likely to be most effective for grade prediction.