FedX-Edu: Explainable Academic Performance Prediction with Hybrid LSTM-Transformer Fusion in Federated Learning
Year: 2025
Type: conference-paper
Abstract: This study proposes a novel academic performance prediction model integrating temporal features and attention mechanisms within a federated learning framework. By developing a hybrid LSTM-Transformer architecture, we achieve dynamic fusion of sequential patterns and global dependencies from multimodal learning behaviors. The model enhances interpretability through feature importance tracing and decision visualization, while preserving data privacy via distributed computation. Experimental results demonstrate 15.7% RMSE improvement over baseline models, with visualized knowledge graph analysis revealing actionable pedagogical insights. This work establishes a new paradigm for explainable AI in educational prediction systems.
Author: Xiongfei Yao
Institution: Nanning University
Language: English
Subfield: Computer Science Applications
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: gold
OpenAlex ID: W4412822317