Adaptive multimodal imputation and normalization (amin): a practical preprocessing framework for predicting student academic performance using smartphone behavioral data
Predicting student academic performance from smartphone usage patterns requires careful preprocessing of heterogeneous mobile sensor data before deep learning model training. This research introduces AMIN (Adaptive Multimodal Imputation and Normalization), a systematic preprocessing framework designed to standardize noisy, incomplete smartphone behavioral data for educational prediction tasks.