KARL: Knowledge-Aware Retrieval and Representations aid Retention and Learning in Students
<|reference_start|>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 KARL, a simple but effective content-aware student model employing deep knowledge tracing (DKT), retrieval, and BERT to predict student recall. We train KARL by collecting a new dataset of 123,143 study logs on diverse trivia questions. KARL 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 KARL online. Based on 32 study paths from 27 users, KARL improves learning efficiency over SOTA, showing KARL's strength and encouraging researchers to look beyond historical study data to fully capture student abilities.<|reference_end|>
@article{shu2024karl:,
    title={KARL: Knowledge-Aware Retrieval and Representations aid Retention and
  Learning in Students},
    author={Matthew Shu, Nishant Balepur, Shi Feng, Jordan Boyd-Graber},
    journal={arXiv preprint arXiv:2402.12291},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.12291},
    primaryClass={cs.CL}
}