Real-Time Multimodal Learning Behaviour Monitoring and At-Risk Prediction in Engineering Education: An Interpretable Machine Learning Framework With Large Language Model Feedback

Linked learner records and analysis scripts for a multimodal monitoring study of 500 engineering and related students, covering platform logs, minute-level behaviour, and facial attention metrics used to predict at-risk status and reproduce the reported tables and figures.