When Administrative Networks Fail: Curriculum Structure, Early Performance, and the Limits of Co-enrolment Social Synchrony for Dropout Prediction in Engineering Education

TL;DR

This study evaluates whether social network analysis from co-enrolment data improves dropout prediction in engineering education, finding that curriculum structure and early performance are more predictive than social network features.

Contribution

It demonstrates that in curriculum-constrained programs, administrative co-enrolment social network features do not enhance dropout prediction beyond curriculum topology and early academic metrics.

Findings

Network features do not improve prediction accuracy.

Curriculum structure and early performance are key predictors.

Adding social network features degrades model performance.

Abstract

Social integration theories suggest that students embedded in supportive peer networks are less likely to drop out. In learning analytics, this has motivated the use of social network analysis (SNA) from institutional co-enrolment data to predict attrition. This study tests whether such administrative network features add predictive value beyond a leakage-aware, curriculum-graph-informed model in a long-cycle Civil Engineering programme at a public university in Argentina. Using a three-semester observation window and a 16-fold leave-cohort-out design on 1,343 students across 15 cohorts, we compare four configurations: a baseline model (M0), baseline plus network features (M1), baseline plus curriculum-graph features (M2), and a full model (M3). After a leakage audit removed two post-outcome variables that had produced implausibly perfect performance, retrained models show that M0 and M2 achieve F1 = 0.9411 and ROC-AUC = 0.9776, while adding network features systematically degrades performance (M1 and M3: F1 = 0.9367; ROC-AUC = 0.9768). We conclude that in curriculum-constrained programmes, administrative co-enrolment SNA does not provide additional risk information beyond curriculum topology and early academic performance.