Modeling Spaced Repetition with LSTMs

Jakub Pokrywka, Marcin Biedalak, Filip Graliński, Krzysztof Biedalak

2023

Abstract

Spaced repetition is a human learning technique focused on optimizing time intervals between a student’s repetitions of the same information items. It is designed for the most effective long-term high-retention knowledge acquisition in terms of a student’s time spent on learning. Repetition of an information item is performed when its estimated recall probability falls to the required level. Spaced repetition works particularly well for itemized knowledge in areas requiring high-volume learning like languages, computer science, medicine, etc. In this work, we present a novel machine-learning approach for the prediction of recall probability developed using the massive repetition data collected in the SuperMemo.com learning ecosystem. The method predicts the probability of remembering an item by a student using an LSTM neural network. In our experiments, we observed that applying the spaced repetition research expert algorithms (Woźniak et al., 2005), like imposing the negative exponential function as the output forgetting curve, increases the LSTM model performance. We analyze how this model compares to other machine-learning or expert methods such as the Leitner method, XGBoost, half-life regression, and the spaced repetition expert algorithms. We found out that the choice of evaluation metric is crucial. Furthermore, we elaborate on this topic, finally selecting macro-average MAE and macro-average Likelihood for the primary and secondary evaluation metrics.

Paper Citation

in Harvard Style

Pokrywka J., Biedalak M., Graliński F. and Biedalak K. (2023). Modeling Spaced Repetition with LSTMs. In Proceedings of the 15th International Conference on Computer Supported Education - Volume 2: CSEDU, ISBN 978-989-758-641-5, SciTePress, pages 88-95. DOI: 10.5220/0011724000003470

in Bibtex Style

@conference{csedu23,
author={Jakub Pokrywka and Marcin Biedalak and Filip Graliński and Krzysztof Biedalak},
title={Modeling Spaced Repetition with LSTMs},
booktitle={Proceedings of the 15th International Conference on Computer Supported Education - Volume 2: CSEDU,},
year={2023},
pages={88-95},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011724000003470},
isbn={978-989-758-641-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 15th International Conference on Computer Supported Education - Volume 2: CSEDU,
TI - Modeling Spaced Repetition with LSTMs
SN - 978-989-758-641-5
AU - Pokrywka J.
AU - Biedalak M.
AU - Graliński F.
AU - Biedalak K.
PY - 2023
SP - 88
EP - 95
DO - 10.5220/0011724000003470
PB - SciTePress