A spatio-temporal graph diffusion and federated contrastive learning framework for cross-institutional educational evaluation.

ABSTRACT: This study introduces a novel evaluation framework that combines a space-time graph diffusion model (STG-DM) and federated contrastive learning (FedCL) to address collaborative optimization challenges in cross-school education evaluation. This integration enables the creation of a thermodynamically driven space-time diffusion equation and an adaptive graph convolution mechanism, facilitating accurate modeling of the space-time evolution of multimodal educational behaviors. It effectively overcomes the shortcomings of traditional methods, which often suffer from local overfitting and dynamic correlation modeling failures due to data silos. The graph diffusion operator, constrained by non-equilibrium thermodynamic principles, has proven to enhance the prediction accuracy of cross-regional ed

SUBMITTER: Fang X 

PROVIDER: S-EPMC12834942 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature