In a Mechanical Turk flashcard experiment, raising the new-item arrival rate first increases mastered items, but past the optimum fewer items are mastered and more get stuck in deck 1.¶
Claim · All claims
Evidence · 1 study (2 entries) · causalr?·q2
Currency · newest study 2016 · not yet checked against its sources · 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.
q2 i? As arrival rate increased, the number of mastered items first increased. → Reddy 2016
q2 i? Past the optimum arrival rate, relatively fewer items were mastered and relatively more items got stuck in deck 1. → Reddy 2016 (2)
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
q2 · i? · causal · r?
Mechanical Turk experiment: average distribution of items across decks at session end, by arrival-rate condition (Fig. 15). "as the arrival rate increases, we first see an increase in the number of mastered items"; descriptive counts, no test reported.
"Fig. 15 illustrates the same phase transition observed earlier: as the arrival rate increases, we first see an increase in the number of mastered items."
Reddy 2016 (2)¶
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
q2 · i? · causal · r?
Same Mechanical Turk experiment and deck-distribution analysis (Fig. 15). Past the optimum, "relatively fewer items are mastered and relatively more items get ‘stuck’ in deck 1"; descriptive, no test reported.
"However, as the arrival rate increases past the optimum, relatively fewer items are mastered and relatively more items get ‘stuck’ in deck 1."
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. — possibly the same claim (merge candidate)
- In simulation, the mean-recall approximation matches the clocked-delay Leitner Queue Network for small arrival rates, and its phase-transition threshold appears to be a conservative lower bound. — related
Page metadata
| Field | Value |
|---|---|
type |
claim |
id |
excess-new-item-rate-leaves-items-stuck-in-first-leitner-deck |
title |
In a Mechanical Turk flashcard experiment, raising the new-item arrival rate first increases mastered items, but past the optimum fewer items are mastered and more get stuck in deck 1. |
description |
In a Mechanical Turk flashcard experiment, raising the new-item arrival rate first increases mastered items, but past the optimum fewer items are mastered and more get stuck in deck 1. |
status |
review |
evidence_strength |
moderate |
newest_evidence |
2016 |
generated |
by claude/unspecified on 2026-09-24 |
trust |
unverified — no one has confirmed this page's content |
sources |
2 — reddy-2016, reddy-2016-2 |
Frontmatter is the page's machine-readable record — see the Schema & Guide.