From networked students centrality to student networks density: What really matters for student performance?

CENTRALITY; HIERARCHICAL clustering (Cluster analysis); RANDOM graphs; DENSITY; MULTILEVEL models

The impact of student networks on academic performance has gained importance as a research subject. In addition to well-known centrality measures (i.e., degree and closeness centralities), this study tests indices that received less attention to predict student performance. This set of measures allows for distinguishing the effects on performance of being connected, being located in advantageous position(s), being connected to central peers and being located within connected neighborhoods. Besides, studies on links between network density and student achievement are rare. This research investigates the combined impacts of student centrality and network density on academic performance. We asked 574 college students about their friendships, and drew the network from the collected information. We used the Exponential Random Graph Models to impute missing friendship ties. Then, we applied a hierarchical clustering approach that identified sub-communities within the student network and we computed the density within each sub-community to study density. Finally, we used hierarchical modeling to predict student performance, i.e., by centrality at the student level and by density at the network level. Results demonstrate a positive impact of the geodesic k -path and of the closeness centralities on GPA, together with a positive impact of cluster's density on performance, which seems, however, bounded by a ceiling effect. • We use dimensions of centrality that are different to predict student performance. • We model performance by centrality at student level and by density at network level. • Tools: PCA, ERGM, agglomerative hierarchical clustering and multilevel modeling. • We show positive impact of geodesic k -path and closeness centrality, and of density. [ABSTRACT FROM AUTHOR]

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