Under the Leitner Queue Network optimization, easy items call for roughly uniform time across decks, while more difficult items call for more time on lower decks.¶

Claim · All claims
Evidence · 1 study · theoretical r3 · q1
Currency · newest study 2016 · not yet checked against its sources · not reviewed by a person

Subclaims¶

q1 i? Comparing optimal schedules for different item difficulties at the same budget (Fig. 12) suggests uniform deck time for easy items and more lower-deck time for harder items. → Reddy 2016

Evidence¶

Reddy 2016¶

Reddy, S., Labutov, I., Banerjee, S., & Joachims, T. (2016). Unbounded Human Learning: Optimal Scheduling for Spaced Repetition. KDD ’16, San Francisco, CA, USA. https://doi.org/10.1145/2939672.2939850

q1 · i? · theoretical · r3

Optimization result from the item-difficulty extension of the model (Fig. 12). It "suggests that when items are generally easy, the user should spend a roughly uniform amount of time on each deck", and more on lower decks for harder items.

"Fig. 12 suggests that when items are generally easy, the user should spend a roughly uniform amount of time on each deck; however, when items are of higher general difficulty, the user should spend more time on lower decks than higher decks."

Discussion¶

Related Claims¶

• Under the Leitner Queue Network model, raising the rate of new-item introduction beyond a threshold causes a phase transition in learning rate, which a Mechanical Turk vocabulary experiment reproduced. — the general claim this finding bears on, with the studies behind it
• Under the mean-recall approximation, the optimal Leitner Queue Network schedule increases the expected delay between reviews as an item moves up through the decks. — related
• Under the mean-recall approximation, the optimal Leitner Queue Network review schedule spends more time on lower decks than on higher decks. — a broader claim this one bears on
• Under the mean-recall approximation, the maximum achievable learning rate is convex in the learner's review frequency budget, suggesting increasing returns at lower budgets. — related