"Unbounded Human Learning: Optimal Scheduling for Spaced Repetition", Reddy et al 2016 http://arxiv.org/pdf/1602.07032.pdf
> In the study of human learning, there is broad evidence that our ability to > retain a piece of information improves with repeated exposure, and that it > decays with delay since the last exposure. This plays a crucial role in the > design of educational software, leading to a trade-off between teaching new > material and reviewing what has already been taught. A common way to balance > this trade-off is via spaced repetition - using periodic review of content to > improve long-term retention. Though widely used in practice, there is little > formal understanding of the design of these systems. This paper addresses > this gap. First, we mine log data from a spaced repetition system [Mnemosyne] > to establish the functional dependence of retention on reinforcement and > delay. Second, based on this memory model, we develop a mathematical > framework for spaced repetition systems using a queueing-network approach. > This model formalizes the popular Leitner Heuristic for spaced repetition, > providing the first rigorous and computationally tractable means of > optimizing the review schedule. Finally, we empirically confirm the validity > of our formal model via a Mechanical Turk experiment. In particular, we > verify a key qualitative insight and prediction of our model - the existence > of a sharp phase transition in learning outcomes upon increasing the rate of > new item introduction -- gwern http://www.gwern.net -- You received this message because you are subscribed to the Google Groups "mnemosyne-proj-users" group. To unsubscribe from this group and stop receiving emails from it, send an email to [email protected]. To post to this group, send email to [email protected]. To view this discussion on the web visit https://groups.google.com/d/msgid/mnemosyne-proj-users/CAMwO0gydSvP3jB8XWKcMuu4_kMdPyb_-KWEOdHz1nADLAfR%3D4w%40mail.gmail.com. For more options, visit https://groups.google.com/d/optout.
