Adaptive Forgetting Curves for Spaced Repetition Language Learning
This work addresses the challenge of optimizing spaced repetition for language learners, though it is incremental as it builds on existing forgetting curve models by incorporating new features.
The study tackled the problem of modeling forgetting curves for personalized vocabulary learning in second language acquisition, finding that word complexity is a highly informative feature that can be effectively learned by neural networks to predict recall probabilities.
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.