@inproceedings{jacobs-etal-2026-math,
title = "Math-Specialized or General-Purpose? A Comparison of {M}ath{BERT} and {D}e{BERT}a-v3-large for Automated Scoring of Mathematics-Explanation Items",
author = "Jacobs, Gregory M. and
Bediwy, Ahmed H. and
Bellows, Martha",
editor = "Wilson, Joshua and
Ormerod, Christopher and
Beiting-Parrish, Magdalen",
booktitle = "Proceedings of the Artificial Intelligence in Measurement and Education Conference ({AIME}-Con): Works in Progress",
month = oct,
year = "2026",
address = "Wyndham Grand Pittsburgh Downtown, Pittsburgh, Pennsylvania, United States",
publisher = "National Council on Measurement in Education (NCME)",
url = "https://aclanthology.org/2026.aimecon-wip.25/",
pages = "193--199",
ISBN = "979-8-9983004-1-7",
abstract = "Automated scoring of math-explanation items should handle responses that mix natural language with symbolic reasoning. The 2023 NAEP Automated Scoring Challenge showed that fine-tuning pre-trained encoders can reach human-like agreement, but its winning system. used a large, general-purpose encoder, while smaller math-specific pre-trained encoders remain a plausible and more efficient alternative. We fine-tune MathBERT (110M parameters) and DeBERTa-v3-large (183M parameters) as single-output regression scorers on 31 math-explanation items from one statewide summative assessment administration, benchmarking every model against human{--}human reliability and summarizing deployability with a three-tier acceptance criteria. On the 18 items fine-tuned under both encoders with identical datasets, the resulting performance between the two was practically indistinguishable: mean best QWK was 0.931 for MathBERT and 0.933 for DeBERTa-v3-large{---}a difference of only 0.002{---}and the models traded top performance at similar rates. Given similar performance, we tested a two-stage approach that used the larger general-purpose encoder only when the smaller math-focused encoder failed acceptance criteria. Extending MathBERT to all 31 items, it reached a mean QWK of 0.926 against a human{--}human benchmark of QWK = 0.940 and met operational acceptance criteria on 81{\%} of items. Escalating the six items where a fine-tuned MathBERT model failed acceptance to a fine-tuned DeBERTa-v3-large model recovered four of the six, yielding acceptable automated-scoring models for 29 of 31 items. Because the smaller, math-pre-trained encoder matches the larger one at roughly 40{\%} fewer parameters, we recommend fine-tuning a MathBERT encoder as the default and escalating to the larger, general purpose encoder like DeBERTa- v3-large only when a MathBERT model fails when deploying automated scoring models operationally at scale for math-explanation items."
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<abstract>Automated scoring of math-explanation items should handle responses that mix natural language with symbolic reasoning. The 2023 NAEP Automated Scoring Challenge showed that fine-tuning pre-trained encoders can reach human-like agreement, but its winning system. used a large, general-purpose encoder, while smaller math-specific pre-trained encoders remain a plausible and more efficient alternative. We fine-tune MathBERT (110M parameters) and DeBERTa-v3-large (183M parameters) as single-output regression scorers on 31 math-explanation items from one statewide summative assessment administration, benchmarking every model against human–human reliability and summarizing deployability with a three-tier acceptance criteria. On the 18 items fine-tuned under both encoders with identical datasets, the resulting performance between the two was practically indistinguishable: mean best QWK was 0.931 for MathBERT and 0.933 for DeBERTa-v3-large—a difference of only 0.002—and the models traded top performance at similar rates. Given similar performance, we tested a two-stage approach that used the larger general-purpose encoder only when the smaller math-focused encoder failed acceptance criteria. Extending MathBERT to all 31 items, it reached a mean QWK of 0.926 against a human–human benchmark of QWK = 0.940 and met operational acceptance criteria on 81% of items. Escalating the six items where a fine-tuned MathBERT model failed acceptance to a fine-tuned DeBERTa-v3-large model recovered four of the six, yielding acceptable automated-scoring models for 29 of 31 items. Because the smaller, math-pre-trained encoder matches the larger one at roughly 40% fewer parameters, we recommend fine-tuning a MathBERT encoder as the default and escalating to the larger, general purpose encoder like DeBERTa- v3-large only when a MathBERT model fails when deploying automated scoring models operationally at scale for math-explanation items.</abstract>
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%0 Conference Proceedings
%T Math-Specialized or General-Purpose? A Comparison of MathBERT and DeBERTa-v3-large for Automated Scoring of Mathematics-Explanation Items
%A Jacobs, Gregory M.
%A Bediwy, Ahmed H.
%A Bellows, Martha
%Y Wilson, Joshua
%Y Ormerod, Christopher
%Y Beiting-Parrish, Magdalen
%S Proceedings of the Artificial Intelligence in Measurement and Education Conference (AIME-Con): Works in Progress
%D 2026
%8 October
%I National Council on Measurement in Education (NCME)
%C Wyndham Grand Pittsburgh Downtown, Pittsburgh, Pennsylvania, United States
%@ 979-8-9983004-1-7
%F jacobs-etal-2026-math
%X Automated scoring of math-explanation items should handle responses that mix natural language with symbolic reasoning. The 2023 NAEP Automated Scoring Challenge showed that fine-tuning pre-trained encoders can reach human-like agreement, but its winning system. used a large, general-purpose encoder, while smaller math-specific pre-trained encoders remain a plausible and more efficient alternative. We fine-tune MathBERT (110M parameters) and DeBERTa-v3-large (183M parameters) as single-output regression scorers on 31 math-explanation items from one statewide summative assessment administration, benchmarking every model against human–human reliability and summarizing deployability with a three-tier acceptance criteria. On the 18 items fine-tuned under both encoders with identical datasets, the resulting performance between the two was practically indistinguishable: mean best QWK was 0.931 for MathBERT and 0.933 for DeBERTa-v3-large—a difference of only 0.002—and the models traded top performance at similar rates. Given similar performance, we tested a two-stage approach that used the larger general-purpose encoder only when the smaller math-focused encoder failed acceptance criteria. Extending MathBERT to all 31 items, it reached a mean QWK of 0.926 against a human–human benchmark of QWK = 0.940 and met operational acceptance criteria on 81% of items. Escalating the six items where a fine-tuned MathBERT model failed acceptance to a fine-tuned DeBERTa-v3-large model recovered four of the six, yielding acceptable automated-scoring models for 29 of 31 items. Because the smaller, math-pre-trained encoder matches the larger one at roughly 40% fewer parameters, we recommend fine-tuning a MathBERT encoder as the default and escalating to the larger, general purpose encoder like DeBERTa- v3-large only when a MathBERT model fails when deploying automated scoring models operationally at scale for math-explanation items.
%U https://aclanthology.org/2026.aimecon-wip.25/
%P 193-199
Markdown (Informal)
[Math-Specialized or General-Purpose? A Comparison of MathBERT and DeBERTa-v3-large for Automated Scoring of Mathematics-Explanation Items](https://aclanthology.org/2026.aimecon-wip.25/) (Jacobs et al., AIME-Con 2026)
ACL