Time-Aware Sequence Modeling of Student Activities: Calibrated Next-Event Prediction from Large-Scale Campus Logs

Learning analytics increasingly relies on large-scale educational activity logs to understand student behavior and support intelligent decision making. However, many existing approaches primarily exploit either semantic information or sequential dependencies independently while overlooking the joint influence of temporal context, behavioral transitions, and probabilistic reliability. This paper addresses the problem of next-event prediction in student activity streams by proposing the Time-Aware Student Event Model (TSEM), a unified framework that integrates semantic representation learning, temporal contextual encoding, and sequential transition modeling within a hybrid neural–probabilistic architecture. The proposed framework is evaluated using a large-scale institutional dataset containing approximately 2.49 million student activity events collected over four academic years, from which nearly 900,000 temporally ordered next-event prediction pairs are constructed. Extensive experiments compare the proposed approach against strong statistical and deep learning baselines, including Markov models, TF-IDF + Logistic Regression, LSTM, GRU, and Transformer architectures, under both random and temporal evaluation protocols. Furthermore, cross-domain validation is performed on the public EdNet benchmark to assess generalization across heterogeneous educational environments. Experimental results demonstrate that jointly modeling semantic, temporal, and sequential information consistently improves predictive performance, robustness to temporal distribution shifts, and probability calibration while maintaining competitive generalization across independent datasets. Ablation studies, statistical significance analysis, and calibration evaluation further confirm the contribution of each modeling component and the reliability of the proposed framework. These findings demonstrate that TSEM provides a robust and practically deployable solution for intelligent educational analytics, supporting applications such as adaptive learning recommendation, early student intervention, learning analytics, and confidence-aware educational decision support.