Persistence and Performance in Co-Enrollment Network Embeddings: An Empirical Validation of Tinto's Student Integration Model

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In this article, we empirically validate Tinto's Student Integration model, in particular, the predictions the model makes regarding both students' academic outcomes and their dropout decisions. In doing so, we analyze three decades' worth of student enrollments at an Australian university and present a novel methodological approach using graph embedding techniques to capture both structural and neighborhood-based features of the co-enrollment network. In keeping with Tinto's model, we find that not only do these embedded representations of students' social network predict their final grade point average (GPA), but also are able to successfully classify students who dropout. Our results show that these embedded representations of a student's social network can achieve F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -scores of up to 0.79 when classifying dropout and explain up to 10% of the variance in student's final GPA. When controlling for a small set of covariates and variables common to the literature, this performance increases to 0.83 and 24%, respectively. Furthermore, the performance of these methods is robust to both changes in their parameterization and to corruption of the underlying social networks. Importantly, this implies that hyperparameters may be selected to reduce the computational demands of this method without loss of predictive power. The novelty of this method, and its ability to identify student dropout, merits further investigation to preemptively identify at-risk students.