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.¶
Claim · All claims
Evidence · 1 study · causalr2·q1
Currency · newest study 2016 · checked against its sources 2026-10-20 (automated, 1 of 1 entry) · not reviewed by a person
Subclaims¶
Reading the evidence codes
q is the design tier, i the impact, n the sample, and the kind with its r
the rigour judged within that kind: what each code means.
This claim: no standardized effect is recorded in observations/ for it yet, so no benchmark reading is possible.
q1 i? Under the optimal schedule, expected delay between subsequent reviews increases as an item moves to higher decks (Fig. 8). → 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? · causal · r2
Optimization result under the mean-recall approximation (Fig. 8, n = 20, U = 1, θ = 0.01). The network "increases the ex- pected delay between subsequent reviews as an item moves up through the system".
"However, in Fig. 8 we ob- serve that the Leitner Queue Network also increases the ex- pected delay between subsequent reviews as an item moves up through the system."
Discussion¶
Related Claims¶
-
Total retrieval spacing improved one-week vocabulary recall while relative schedules were not distinguished — the general claim this finding bears on, with the studies behind it
-
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
- Spaced Practice Improves Long Term Retention
- 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
- 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. — related
- Under the mean-recall approximation, the optimal Leitner Queue Network review schedule spends more time on lower decks than on higher decks. — related
- Over eight weeks, university learners receiving reinforcement learning-optimized oral practice sequencing attained normalized learning gains approximately 2.2 times higher than learners following fixed curricula — related
Page metadata
| Field | Value |
|---|---|
type |
claim |
id |
optimal-leitner-schedule-expands-intervals-between-reviews |
title |
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. |
description |
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. |
status |
review |
evidence_strength |
weak |
newest_evidence |
2016 |
source_checked |
2026-10-20 |
generated |
by claude/unspecified on 2026-09-24 |
trust |
unverified — no one has confirmed this page's content |
sources |
1 — reddy-2016 |
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