Optimizing spaced repetition system schedules

Many papers published along these lines. Collecting some favorites:

• Mozer, M. C., & Lindsey, R. V. (2016). Predicting and Improving Memory Retention: Psychological Theory Matters in the Big Data Era. In M. N. Jones (Ed.), Big data in cognitive science (pp. 34–64).
• Ye, J., Su, J., & Cao, Y. (2022). A Stochastic Shortest Path Algorithm for Optimizing Spaced Repetition Scheduling. Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 4381–4390
• Ebisu (a solution inspired by Bayesian statistics)
• Comparison of contemporary implementations by Jarrett Ye and collaborators
• Shu, M., Balepur, N., Feng, S., & Boyd-Graber, J. (2024). KARL: Knowledge-Aware Retrieval and Representations aid Retention and Learning in Students (arXiv:2402.12291). arXiv. http://arxiv.org/abs/2402.12291
  • this model cleverly uses BERT-based similarity metrics to parameterize new items

Some of my own notes on the subject:

• Conceptual information may have much slower optimal spaced repetition schedules
• Forgotten spaced repetition prompts should probably be reviewed the next day
• Forgotten spaced repetition prompts should probably have smaller future spacing intervals
• more: Quantum Country data analyses

Queue

• Tabibian, B., Upadhyay, U., De, A., Zarezade, A., Schölkopf, B., & Gomez-Rodriguez, M. (2019). Enhancing human learning via spaced repetition optimization. Proceedings of the National Academy of Sciences, 116(10), 3988–3993. https://doi.org/10.1073/pnas.1815156116
  • derives an “optimal” scheduling algorithm using control theory
• Settles, B., & Meeder, B. (2016). A Trainable Spaced Repetition Model for Language Learning. Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 1848–1858. https://doi.org/10.18653/v1/P16-1174
  • “high-life regression” tested at scale on Duolingo