Under the mean-recall approximation, the optimal Leitner Queue Network review schedule spends more time on lower decks than on higher decks.¶
Claim · All claims
Evidence · 1 study · theoreticalr3·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? Optimizing the static planning problem (n = 20, U = 1, θ = 0.01 in Fig. 7) yields a schedule that spends more time on lower decks than higher decks. → 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 under the mean-recall approximation, shown for one parameter setting (Fig. 7). "the optimal schedule spends more time on lower decks than on higher decks".
"In Fig. 7, we see that the optimal schedule spends more time on lower decks than on 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 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. — a narrower finding that bears on this claim
- 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
- 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-reviews-lower-decks-more-often |
title |
Under the mean-recall approximation, the optimal Leitner Queue Network review schedule spends more time on lower decks than on higher decks. |
description |
Under the mean-recall approximation, the optimal Leitner Queue Network review schedule spends more time on lower decks than on higher 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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