Cross-dataset Bloom classification benchmark corpus of five datasets totaling 4,179 questions¶
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Evidence · 2 claims (2 mixed) · 1 study (1 design),q2· 0 of 1 report an effect size · 2 claims rest on one study
Description¶
A benchmark corpus assembled by Abdolali Faraji et al. from five Bloom-taxonomy-annotated question datasets, with mapped labels, code, prompts, and data for cross-dataset evaluation of Bloom-level question-classification methods.
Components¶
Claims¶
- Model accuracy on Bloom classification drops with question text length, with LLMs degrading least [~W]
- Model retraining on labeled OOD data provides the largest improvement in Bloom classification across models and datasets [~W]
Related Products and Programmes¶
Key Sources¶
- Abdolali Faraji, Mohammadreza Molavi, Zohreh Rasoulkhani, Mohammadreza Tavakoli, and Gábor Kismihók. (2026). Cross-Dataset Bloom Question Classification: Supervised Models and Prompted LLMs. https://arxiv.org/abs/2606.13684
Page metadata
| Field | Value |
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type |
product |
id |
cross-dataset-bloom-classification-benchmark-corpus-of-five-datasets-totaling-4179-questio |
title |
Cross-dataset Bloom classification benchmark corpus of five datasets totaling 4,179 questions |
description |
A benchmark corpus assembled by Abdolali Faraji et al. |
status |
review |
product_kind |
dataset |
generated |
by process:settle-candidates on 2026-10-10 |
trust |
unverified — no one has confirmed this page's content |
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