An interpretability framework for multimodal English learning behaviors using temporal graph networks and adaptive SHAP

To address the challenge of interpreting complex spatiotemporal patterns in multimodal English learning behaviors, this study proposes a novel computational framework integrating dynamic graph construction with adaptive interpretable machine learning. First, multi-source data fusion technology is used to integrate logs, data, and evaluation scores to construct a time-series learning behavior graph. Second, the XGBoost LSTM (long short-term memory) hybrid model is used to predict learning effectiveness, and the hierarchical sampling SHAP interpretation algorithm is proposed simultaneously. Third, a three-level warning mechanism is constructed using dynamic threshold sliding windows. Key behavioral characteristics are identified through SHAP values and a risk probability model is created using logistic regression. Validated across educational and industrial datasets, the framework enhances interpretability in predictive maintenance scenario, demonstrating cross-domain applicability in IoT sensor networks and healthcare monitoring systems.