MathEDU: Feedback Generation on Problem-Solving Processes for Mathematical Learning Support
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866912830614142976 |
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| author | Hsu, Wei-Ling Tang, Yu-Chien Yen, An-Zi |
| author_facet | Hsu, Wei-Ling Tang, Yu-Chien Yen, An-Zi |
| contents | The increasing reliance on Large Language Models (LLMs) across various domains extends to education, where students progressively use generative AI as a tool for learning. While prior work has examined LLMs' mathematical ability, their reliability in grading authentic student problem-solving processes and delivering effective feedback remains underexplored. This study introduces MathEDU, a dataset consisting of student problem-solving processes in mathematics and corresponding teacher-written feedback. We systematically evaluate the reliability of various models across three hierarchical tasks: answer correctness classification, error identification, and feedback generation. Experimental results show that fine-tuning strategies effectively improve performance in classifying correctness and locating erroneous steps. However, the generated feedback across models shows a considerable gap from teacher-written feedback. Critically, the generated feedback is often verbose and fails to provide targeted explanations for the student's underlying misconceptions. This emphasizes the urgent need for trustworthy and pedagogy-aware AI feedback in education. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_18056 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MathEDU: Feedback Generation on Problem-Solving Processes for Mathematical Learning Support Hsu, Wei-Ling Tang, Yu-Chien Yen, An-Zi Computation and Language The increasing reliance on Large Language Models (LLMs) across various domains extends to education, where students progressively use generative AI as a tool for learning. While prior work has examined LLMs' mathematical ability, their reliability in grading authentic student problem-solving processes and delivering effective feedback remains underexplored. This study introduces MathEDU, a dataset consisting of student problem-solving processes in mathematics and corresponding teacher-written feedback. We systematically evaluate the reliability of various models across three hierarchical tasks: answer correctness classification, error identification, and feedback generation. Experimental results show that fine-tuning strategies effectively improve performance in classifying correctness and locating erroneous steps. However, the generated feedback across models shows a considerable gap from teacher-written feedback. Critically, the generated feedback is often verbose and fails to provide targeted explanations for the student's underlying misconceptions. This emphasizes the urgent need for trustworthy and pedagogy-aware AI feedback in education. |
| title | MathEDU: Feedback Generation on Problem-Solving Processes for Mathematical Learning Support |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2505.18056 |