Predicting Student Course Grades Using Individual and Peer Academic Trajectories

Predicting student grades plays a critical role in providing individualized academic guidance and identifying students at risk of failing to complete their studies. While previous studies on student performance prediction have often relied on student demographics or attempted to model students’ entire academic careers, we focus on predicting future grades using both individual academic records and transcripts of similar students. Our proposed framework leverages peer information to model student grade trajectories and aligns them with the academic records of previously enrolled students who followed similar academic paths. This approach enhances prediction accuracy while maintaining adaptability across diverse curricula. Furthermore, to address data scarcity and privacy concerns, we present a methodology for generating synthetic datasets using generative language models, enabling the creation of realistic academic records without compromising student privacy. Our evaluation of sequential academic progression modeling includes four temporal models: RNN, LSTM, GRU, and Transformer. LSTM achieved the best performance, closely followed by GRU and RNN, while the Transformer performed slightly worse.