Multigraph indegree centrality is consistently and positively correlated with student performance across 12 PBL courses, with stronger correlations than other configurations in eight courses¶

Claim · All claims
Evidence · 1 study · associational r1 · q3 · i3 large
Currency · newest study 2020 · checked against its sources 2026-10-20 (automated, 1 of 1 entry) · not reviewed by a person · overdue for a recheck since 2023: tool and model accuracy changes quickly

Subclaims¶

q3 i3 Indegree centrality computed on multigraph networks was positively correlated with performance in all 12 courses, ranging r = 0.54 to r = 0.77, and was stronger than other configurations in eight courses. → Mohammed Saqr 2020

Evidence¶

Mohammed Saqr 2020¶

Mohammed Saqr, et al. (2020). Capturing the participation and social dimensions of computer-supported collaborative learning through social network analysis: which method and measures matter? International Journal of Computer-Supported Collaborative Learning 15:227–248. https://doi.org/10.1007/s11412-020-09322-6

q3 · i3 · associational · r1

Observational analysis of LMS forum interaction networks from 12 problem-based learning courses (13,428 interactions, 598 students). The study reports that multigraph indegree was consistently positively correlated with performance, with correlations from "r = 0.54, p < 0.01 to r = 0.77, p < 0.01". Simplified and weighted variants missed correlations in two to three courses.

"The Spearman correlation between indegree centrality calculated with the multigraph method proved to be consistently positively correlated, and with higher strength of correlation coefficient with performance, in the 12 studied courses (Fig. 3). The correlation coefficient ranged from r = 0.54, p < 0.01 to r = 0.77, p < 0.01; the coefficient value was also stronger than the other configurations in eight courses."

Discussion¶

Related Claims¶

• In a correlation meta-analysis across 69 course offerings, diffusion-based centralities correlated with academic achievement with high combined coefficients and predictive intervals
• Multigraph closeness centrality is the most consistent of the three configurations for correlating closeness with performance, though less robust than degree and eigenvector centralities
• Betweenness centrality shows the least consistent correlation with performance, being largely insignificant or negative across configurations except in one course
• Multigraph outdegree centrality is the most robust participation indicator, with significant positive correlations in all courses, while weighted outdegree was negatively correlated in two courses
• Courses with a low number of forum interactions show inconsistent participation-dimension results, while eigenvector centrality remains robust there.
• Post size is not a reliable tie weight for centrality measures; receiving interactions better indicates the value of a contribution. — related