  Accurate prediction of students knowledge is a fundamental building block of
personalized learning systems. Here, we propose a novel ensemble model to
predict student knowledge gaps. Applying our approach to student trace data
from the online educational platform Duolingo we achieved highest score on both
evaluation metrics for all three datasets in the 2018 Shared Task on Second
Language Acquisition Modeling. We describe our model and discuss relevance of
the task compared to how it would be setup in a production environment for
personalized education.
