  Flashcard schedulers are tools that rely on 1) student models to predict the
flashcards a student knows; and 2) teaching policies to schedule cards based on
these predictions. Existing student models, however, only use flashcard-level
features, like the student's past responses, ignoring the semantic ties of
flashcards. Deep Knowledge Tracing (DKT) models can capture semantic relations
with language models, but are inefficient, lack content-rich datasets for
evaluation, and require robust teaching policies. To address these issues, we
design KARL, a DKT-inspired student model that uses retrieval and BERT
embeddings for efficient and accurate student recall predictions. To test KARL,
we collect a new dataset of diverse study history on trivia questions. KARL
bests existing student models in AUC and calibration error. Finally, we propose
a novel teaching policy that exploits the predictive power of DKT models to
deploy KARL online. Based on 27 learners and 32 6-day study trajectories, KARL
shows the ability to enhance medium-term educational learning, proving its
efficacy for scheduling.
