Cross-Dataset Bloom Question Classification: Supervised Models and Prompted LLMs
Automatic Bloom's taxonomy classification of assessment questions can substantially reduce instructor workload, but labeling is subjective and teacher-dependent. Prior machine learning (ML) and deep learning (DL) approaches reported strong within-dataset results, yet were rarely evaluated in cross-dataset settings, leaving real-world generalizability unclear; meanwhile, LLM effectiveness for Bloom question classification has not been systematically studied. We evaluated the cross-dataset generalization of existing ML/DL methods and assessed LLMs with multiple prompting strategies on five datasets; the best prompting strategy combined in-context examples with course-specific action verbs. ...
| Authors | Abdolali Faraji, Mohammadreza Molavi, Zohreh Rasoulkhani et al. |
|---|---|
| Primary category | cs.CY |
| Published | 22 April 2026 |
| Last updated at source | 22 April 2026 |
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