  In this paper, we present a transformer architecture for predicting student
performance on standardized tests. Specifically, we leverage students
historical data, including their past test scores, study habits, and other
relevant information, to create a personalized model for each student. We then
use these models to predict their future performance on a given test. Applying
this model to the RIIID dataset, we demonstrate that using multiple
granularities for temporal features as the decoder input significantly improve
model performance. Our results also show the effectiveness of our approach,
with substantial improvements over the LightGBM method. Our work contributes to
the growing field of AI in education, providing a scalable and accurate tool
for predicting student outcomes.
