AEEGS and human raters produced similarly shaped score distributions, with most students' scores clustered around the mean in the 'good' band¶
Claim · All claims
Evidence · 1 study (2 entries) · associationalr2·q2
Currency · newest study 2026 · not yet checked against its sources · not reviewed by a person · recheck by 2029: tool and model accuracy changes quickly
Subclaims¶
Reading the evidence codes
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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? Under AEEGS, 598 students (59.3%) scored in the good band, 89 (8.8%) very good and 7 (0.7%) excellent; under human raters, 633 (62.8%) scored good, 71 (7.0%) very good and 5 (0.5%) excellent. → Olaoye DD 2026
q2 i? Human raters' distribution showed 633 students (62.8%) in the good band, 183 (18.2%) fair and 116 (11.5%) low, with scores normally distributed around the mean. → Olaoye DD 2026 (2)
Evidence¶
Olaoye DD 2026¶
Olaoye DD, Owolabi HO and Olaoye OT (2026). Automated software scoring of senior school certificate examination mathematical items in economics using a contextual similarity model. Front. Educ. 11:1669504. https://doi.org/10.3389/feduc.2026.1669504
q2 · i? · design · r2
Descriptive frequency analysis of AEEGS scores for all 1,008 students, grouped into five levels using the standard normal curve. The article reports 598 (59.3%) in the good band and 68.8% of scores within good, very good and excellent combined.
"the 7 (0.7%) of the students had excellent scores, 89 (8.8%) and 598 (59.3%) of the students had very good and good scores, while less than half of the students' scores falls within fair and low with 181 (18.0%) and 133 (13.2%) respectively."
Olaoye DD 2026 (2)¶
Olaoye DD, Owolabi HO and Olaoye OT (2026). Automated software scoring of senior school certificate examination mathematical items in economics using a contextual similarity model. Front. Educ. 11:1669504. https://doi.org/10.3389/feduc.2026.1669504
q2 · i? · associational · r2
Descriptive frequency analysis of the 12 human raters' scores for the same 1,008 students, grouped into the same five levels. The article reports 633 (62.8%) good and states the scores are "normally distributed, with majority of the scores cluster around the mean".
"Out of 1,008 students, 5 (0.5%) of them had excellent scores, 71 (7.0%) of them had very good scores, 633 (62.8%) had good scores, 183 (18.2%) had fair scores, and 116 (11.5%) of them had low scores"
Discussion¶
Related Claims¶
- AEEGS scores show high absolute agreement with 12 human expert raters' scores on mathematical Economics essay items (average-measures ICC = 0.863) — related
- AEEGS and human raters produced nearly equivalent, low mean scores (5.94 vs 5.97 of 20), with AEEGS slightly higher — related
- Each of the 12 human raters' scores correlates significantly with AEEGS scores, with coefficients ranging from 0.604 to 0.864 — related
Page metadata
| Field | Value |
|---|---|
type |
claim |
id |
aeegs-human-score-distributions-similar |
title |
AEEGS and human raters produced similarly shaped score distributions, with most students' scores clustered around the mean in the 'good' band |
description |
AEEGS and human raters produced similarly shaped score distributions, with most students' scores clustered around the mean in the 'good' band |
status |
review |
evidence_strength |
moderate |
newest_evidence |
2026 |
recheck_by |
2029 |
generated |
by process:wiki-ingest on 2026-10-10 |
trust |
unverified — no one has confirmed this page's content |
sources |
2 — olaoye-dd-2026, olaoye-dd-2026-2 |
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