KARL: Knowledge-Aware Retrieval and Representations aid Retention and Learning in Students

Flashcard schedulers rely on 1) student models to predict the flashcards a student knows; and 2) teaching policies to pick which cards to show next via these predictions. Prior student models, however, just use study data like the student's past responses, ignoring the text on cards. We propose content-aware scheduling, the first schedulers exploiting flashcard content. To give the first evidence that such schedulers enhance student learning, we build KAR 3 L, a simple but effective content-aware student model employing deep knowledge tracing (DKT), retrieval, and BERT to predict student recall. We train KAR 3 L by collecting a new dataset of 123,143 study logs on diverse trivia questions. KAR 3 L bests existing student models in AUC and calibration error. To ensure our improved predictions lead to better student learning, we create a novel delta-based teaching policy to deploy KAR 3 L online. Based on 32 study paths from 27 users, KAR 3 L improves learning efficiency over SOTA, showing KAR 3 L's strength and encouraging researchers to look beyond historical study data to fully capture student abilities. 1