Predicting course enrollment alongside grades significantly improves academic performance forecasting—jointly modeling what students take and how they'll perform is more accurate than predicting grades alone.
This paper presents TRACE, a transformer-based model that jointly predicts which courses students will take and their grades in those courses for upcoming semesters. Unlike traditional approaches that treat student history as a simple sequence, TRACE captures how courses taken concurrently within a semester affect performance, reducing prediction error by nearly 50% compared to grade-only models.