Consensus calibration closely reproduces pooled-benchmark item-parameter means and standard deviations on an operational assessment¶
Claim · All claims
Evidence · 1 study · designr2·q2
Currency · newest study 2026 · checked against its sources 2026-10-18 (automated, 1 of 1 entry) · not reviewed by a person · recheck by 2029: tool and model accuracy changes quickly
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? Posterior means agreed closely between the consensus aggregate and the pooled benchmark (r = .998 for d, .991 for log a1), indicating no systematic distortion of location. → Paul A. Jewsbury and Steven W. Nydick and Manqian Liao and Siyuan (Marco) Chen 2026
Evidence¶
Paul A. Jewsbury and Steven W. Nydick and Manqian Liao and Siyuan (Marco) Chen 2026¶
Paul A. Jewsbury and Steven W. Nydick and Manqian Liao and Siyuan (Marco) Chen. (2026). Bayesian Consensus Calibration of Continuously Evolving IRT Item Banks. https://arxiv.org/abs/2609.13590 doi:10.48550/arxiv.2609.13590
q2 · i? · design · r2
Benchmark comparison on operational Duolingo English Test data across four quarterly calibration periods, restricted to converged item parameters. The article reports "r=.998 for d and.991 for loga 1" with RMSE 0.129 and 0.081 for posterior means.
"Posterior means agreed closely (r=.998 for d and.991 for loga 1; RMSE 0.129 and 0.081), indicating that the per-draw linking and the precision-weighted aggregation introduce no systematic distortion of location."
Discussion¶
Related Claims¶
- Per-period ability distributions implied by the linked calibrations agree closely with the pooled benchmark, checking the linking independently of the items — related
- Consensus calibration shows mild under-dispersion relative to the pooled benchmark, strongest for sparsely observed items, consistent with residual prior over-counting — related
- A convergence screen excluding non-converged (item, parameter) pairs removed 1.9% of pairs from the benchmark comparison — related
- The Bayesian context layer applies stronger posterior personalization to students with fewer training clips, correlating clip count with KL divergence from the prior (r=-0.748, p=0.001) — related
Page metadata
| Field | Value |
|---|---|
type |
claim |
id |
consensus-posterior-agrees-with-pooled-benchmark |
title |
Consensus calibration closely reproduces pooled-benchmark item-parameter means and standard deviations on an operational assessment |
description |
Consensus calibration closely reproduces pooled-benchmark item-parameter means and standard deviations on an operational assessment |
status |
review |
evidence_strength |
moderate |
newest_evidence |
2026 |
source_checked |
2026-10-18 |
recheck_by |
2029 |
generated |
by process:wiki-ingest on 2026-10-10 |
trust |
unverified — no one has confirmed this page's content |
sources |
1 — paul-a-jewsbury-and-steven-w-nydick-and-manqian-liao-and-siyuan-marco-chen-2026 |
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