Unbounded Human Learning: Optimal Scheduling for Spaced Repetition
Today I read a paper titled “Unbounded Human Learning: Optimal Scheduling for Spaced Repetition”
I like the fact that there is an empirical evaluation of the results against an actual audience and not just merely conjecture. The queuing implementation is useful. My concern is with “human boredom” that could arise from a badly tuned, automatic system (though no worse than a badly tuned system designed by a human). We want to help the subjects memorise what they need, but we also need to keep feeding them novel, i.e. new, knowledge to keep them engaged.
The abstract is:
In the study of human learning, there is broad evidence that our ability to retain information improves with repeated exposure and decays with delay since 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 spaced repetition, which uses periodic review of content to improve long-term retention. Though spaced repetition is widely used in practice, e.g., in electronic flashcard software, there is little formal understanding of the design of these systems. Our paper addresses this gap in three ways. First, we mine log data from spaced repetition software to establish the functional dependence of retention on reinforcement and delay. Second, we use this memory model to develop a stochastic model for spaced repetition systems. We propose a queueing network model of the Leitner system for reviewing flashcards, along with a heuristic approximation that admits a tractable optimization problem for review scheduling. Finally, we empirically evaluate our queueing model through a Mechanical Turk experiment, verifying a key qualitative prediction of our model: the existence of a sharp phase transition in learning outcomes upon increasing the rate of new item introductions.