“KARL: Knowledge-Aware Retrieval and Representations Aid Retention and Learning in Students”, Matthew Shu, Nishant Balepur, Shi Feng, Jordan Boyd-Graber2024-02-19 (Transformer, spaced repetition)⁠:

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.