PERMA-guided multi-topology graph neural networks for cross-cultural student well-being prediction.

ABSTRACT: Student well-being prediction is of great significance for promoting personalized education and preventing mental health problems, but existing methods suffer from limitations including lack of psychological theory guidance, neglect of student relationship modeling, and insufficient cross-cultural adaptability. This study proposes the PERMA-GNN-Transformer model, which innovatively integrates Seligman's PERMA positive psychology theory with graph neural networks and Transformer architecture. The model achieves the transformation from raw educational data to psychologically meaningful representations through a theory-driven feature embedding mechanism, designs four types of student relationship graphs based on cosine similarity, Euclidean distance, learning styles, and PERMA weighting, and

SUBMITTER: Mo L 

PROVIDER: S-EPMC12707682 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature