# Adaptive Forgetting Curves for Spaced Repetition Language Learning

arXiv: https://arxiv.org/abs/2004.11327 · HTML: https://mlsanity.com/paper/2004.11327/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Apr 2020.

## Built on by (most cited first, 4 of 4)

- [One Size Does Not Fit All: The Case for Personalised Word Complexity Models](https://mlsanity.com/paper/2205.02564/) (2022)
- [Dynamic Forgetting and Spatio-Temporal Periodic Interest Modeling for Local-Life Service Recommendation](https://mlsanity.com/paper/2508.02451/) (2025)
- [DIY-MKG: An LLM-Based Polyglot Language Learning System](https://mlsanity.com/paper/2507.01872/) (2025)
- [Interpretable Memory Models for Spaced Repetition](https://mlsanity.com/paper/2610.10548/) (2026)

## Abstract (arXiv)

The forgetting curve has been extensively explored by psychologists, educationalists and cognitive scientists alike. In the context of Intelligent Tutoring Systems, modelling the forgetting curve for each user and knowledge component (e.g. vocabulary word) should enable us to develop optimal revision strategies that counteract memory decay and ensure long-term retention. In this study we explore a variety of forgetting curve models incorporating psychological and linguistic features, and we use these models to predict the probability of word recall by learners of English as a second language. We evaluate the impact of the models and their features using data from an online vocabulary teaching platform and find that word complexity is a highly informative feature which may be successfully learned by a neural network model.