Estimating each grader's bias alone accounts for 95% of the RMSE reduction, while modeling grader reliability contributes little with only four grades per student¶
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Evidence · 1 study · designr2·q2
Currency · newest study 2013 · not yet checked against its sources · not reviewed by a person · overdue for a recheck since 2016: tool and model accuracy changes quickly
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q2 i? The single change of estimating each grader's bias (Model PG1-bias) is responsible for 95% of the reduction in RMSE; modeling grader precision does little to improve performance. → Chris Piech 2013
Evidence¶
Chris Piech 2013¶
Chris Piech, Jonathan Huang, Zhenghao Chen, Chuong Do, Andrew Ng, Daphne Koller. (2013). Tuned Models of Peer Assessment in MOOCs. arXiv. https://arxiv.org/abs/1307.2579
q2 · i? · design · r2
Simulation-based evaluation on the HCI datasets (Table 2). The article reports that the bias-only model "is responsible for 95% of our reduction in RMSE", and that on a synthetic dataset generated from Model PG1, reliability is hard to estimate with only four grades per student, becoming notable only with more than 10 assignments graded.
"But the single change that provides the most significant gains in accuracy is obtained by estimating each grader's bias (Model PG1-bias ). This simple model is responsible for 95% of our reduction in RMSE."
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Page metadata
| Field | Value |
|---|---|
type |
claim |
id |
grader-bias-estimation-dominates-accuracy-gains |
title |
Estimating each grader's bias alone accounts for 95% of the RMSE reduction, while modeling grader reliability contributes little with only four grades per student |
description |
Estimating each grader's bias alone accounts for 95% of the RMSE reduction, while modeling grader reliability contributes little with only four grades per student |
status |
review |
evidence_strength |
moderate |
newest_evidence |
2013 |
recheck_by |
2016 |
generated |
by process:wiki-ingest on 2026-10-16 |
trust |
unverified — no one has confirmed this page's content |
sources |
1 — chris-piech-2013 |
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