Hi,

Thanks for sharing, I also picked this up from their blog.

They can do some cool things because they have lots of people learning the same 
cards, so they can establish relative difficulty averaged over lots of users.

For Mnemosyne, most people make their own cards, though, so that part we cannot 
take into account....

Cheers,

Peter

-----Original Message-----
From: [email protected] [mailto:[email protected]] On Behalf Of Gwern Branwen
Sent: 15 December 2016 15:12
To: Mnemosyne mailing list <[email protected]>
Subject: [mnemosyne-proj-users] Duolingo's use of spaced repetition

Settles & Meeder 2016, "A Trainable Spaced Repetition Model for Language 
Learning" http://aclweb.org/anthology/P/P16/P16-1174.pdf

> 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 “half-life” 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.

Comparison to Leitner/Pimsleur, but nothing in the SuperMemo family.

Blog discussion: http://making.duolingo.com/how-we-learn-how-you-learn
Code/data: https://github.com/duolingo/halflife-regression

--
gwern
https://www.gwern.net

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