EdGCL: Disentangling Social and Cognitive Homophily in Graph-Based Educational Recommender Systems
Qingqing Liang, Chunyang Wang, Peiwei Xia, Yanan Zeng, Xin Liu, Xuesong Lu, Aoying Zhou
Abstract
Educational recommendation systems have been a fundamental component for alleviating learning disorientation in self-paced learning. While existing studies mainly leverage cognitive theories to guide learning motivation modeling, they critically overlook the role of social influences. Through empirical analysis, we identify social homophily as an additional driver of learning behaviors, i.e., learners tend to adopt resources validated by their social cohort. However, two challenges impede effective social homophily modeling: (1) the absence and sparsity of predefined social relations in online education, and (2) the deep entanglement of social homophily with cognitive homophily in behavioral data. To tackle these challenges, we propose a graph-based framework EdGCL that explicitly disentangles social homophily and cognitive homophily. EdGCL infers implicit social relations from learners
BibTeX
@inproceedings{aaai2026_edgcldisentangli,
title = {EdGCL: Disentangling Social and Cognitive Homophily in Graph-Based Educational Recommender Systems},
author = {Qingqing Liang and Chunyang Wang and Peiwei Xia and Yanan Zeng and Xin Liu and Xuesong Lu and Aoying Zhou},
booktitle = {AAAI 2026},
year = {2026}
}