The Curious Case of Centrality Measures: A Large-Scale Empirical Investigation

Abstract

There has been extensive research using centrality measures in educational settings. One of the most common lines of such research has tested network centrality measures as indicators of success. The increasing interest in centrality measures has been kindled by the proliferation of learning analytics. Previous works have been dominated by single-course case studies that have yielded inconclusive results regarding the consistency and suitability of centrality measures as indicators of academic achievement. Therefore, large-scale studies are needed to overcome the multiple limitations of existing research (limited datasets, selective and reporting bias, as well as limited statistical power). This study aims to empirically test and verify the role of centrality measures as indicators of success in collaborative learning. For this purpose, we attempted to reproduce the most commonly used centrality measures in the literature in all the courses of an institution over five years of education. The study included a large dataset (n=3,277) consisting of 69 course offerings, with similar pedagogical underpinnings, using meta-analysis as a method to pool the results of different courses. Our results show that degree and eigenvector centrality measures can be a consistent indicator of performance in collaborative settings. Betweenness and closeness centralities yielded uncertain predictive intervals and were less likely to replicate. Our results have shown moderate levels of heterogeneity, indicating some diversity of the results comparable to single laboratory replication studies.