Sequence-Aware Learning Analytics for Early Identification of At-Risk Academic Trajectories in Higher Education Using Transformer Models

Early identification of students at risk of academic underperformance remains a persistent challenge in higher education, particularly in learning environments characterized by complex, temporally evolving patterns of engagement and assessment. Conventional learning analytics approaches typically rely on static or weakly temporal indicators, limiting their ability to detect emerging risk at early stages of an academic term. This study proposes a sequence-aware learning analytics framework that leverages transformer-based models to represent student academic trajectories as ordered sequences of learning events derived from learning management systems and student information systems. The framework integrates heterogeneous behavioral, temporal, and performance signals and applies self-attention mechanisms to capture long-range dependencies and evolving risk patterns. Using a supervised predictive modeling design with rolling-window and early-prediction evaluation protocols, the proposed approach is assessed against traditional machine learning and recurrent neural network baselines. Results demonstrate that transformer models achieve superior predictive performance, earlier risk identification, and greater stability across academic terms and cohorts. Attention-based interpretability further reveals meaningful progression patterns associated with academic disengagement and performance decline. The findings underscore the value of sequence-aware modeling for enhancing institutional early-alert systems and supporting proactive, personalized academic interventions. This study contributes to both learning analytics theory and practice by establishing transformer-based sequence modeling as a robust foundation for early academic risk detection and student success initiatives in higher education.