Predicting Student Cognitive Overload Using Multimodal Behavioral Time-Series Modeling

Cognitive overload is a major contributor to student stress, burnout, and disengagement, yet most educational support systems respond only after performance degradation occurs. This study investigates whether cognitive overload can be predicted in advance using temporal patterns in student planning behavior. I analyzed multimodal behavioral data derived from task management activity, including deadline density, task rescheduling frequency, completion latency, and self-reported stress indicators. Using engineered time-series features, I trained and evaluated multiple machine learning models, including logistic regression, random forest classifiers, and Long Short-Term Memory (LSTM) networks. Experimental results demonstrate that temporal models significantly outperform static baselines, with the LSTM achieving an AUROC of 0.86. Feature trend analysis reveals systematic behavioral drift in the days preceding overload events, suggesting that overload emerges gradually rather than abruptly. These findings highlight the feasibility of early-warning systems for student overload and motivate adaptive planning tools that intervene proactively rather than reactively.