A Trainable Spaced Repetition Model for Language Learning
We present half life regression (HLR) a novel model for spaced repetition practice with applications to second language acquisition. HLR combines psycholinguistic theory with modern machine learning techniques, indirectly estimating the "halflife" of a word or concept in a student's long term memory. We use data from Duolingo a popular online language learning application to fit HLR models, reducing error by 45% compared to several baselines at predicting student recall rates. HLR model weights also shed light on which linguistic concepts are systematically challenging for second language learners. Finally, HLR was able to improve Duolingo daily student engagement by 12% in an operational user study.
ACL