MATHWELL: Generating Educational Math Word Problems Using Teacher Annotations
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866916412621062144 |
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| author | Christ, Bryan R Kropko, Jonathan Hartvigsen, Thomas |
| author_facet | Christ, Bryan R Kropko, Jonathan Hartvigsen, Thomas |
| contents | Math word problems are critical K-8 educational tools, but writing them is time consuming and requires extensive expertise. To be educational, problems must be solvable, have accurate answers, and, most importantly, be educationally appropriate. We propose that language models have potential to support K-8 math education by automatically generating word problems. However, evaluating educational appropriateness is hard to quantify. We fill this gap by having teachers evaluate problems generated by LLMs, who find existing models and data often fail to be educationally appropriate. We then explore automatically generating educational word problems, ultimately using our expert annotations to finetune a 70B language model. Our model, MATHWELL, is the first K-8 word problem generator targeted at educational appropriateness. Further expert studies find MATHWELL generates problems far more solvable, accurate, and appropriate than public models. MATHWELL also matches GPT-4's problem quality while attaining more appropriate reading levels for K-8 students and avoiding generating harmful questions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_15861 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | MATHWELL: Generating Educational Math Word Problems Using Teacher Annotations Christ, Bryan R Kropko, Jonathan Hartvigsen, Thomas Computation and Language Math word problems are critical K-8 educational tools, but writing them is time consuming and requires extensive expertise. To be educational, problems must be solvable, have accurate answers, and, most importantly, be educationally appropriate. We propose that language models have potential to support K-8 math education by automatically generating word problems. However, evaluating educational appropriateness is hard to quantify. We fill this gap by having teachers evaluate problems generated by LLMs, who find existing models and data often fail to be educationally appropriate. We then explore automatically generating educational word problems, ultimately using our expert annotations to finetune a 70B language model. Our model, MATHWELL, is the first K-8 word problem generator targeted at educational appropriateness. Further expert studies find MATHWELL generates problems far more solvable, accurate, and appropriate than public models. MATHWELL also matches GPT-4's problem quality while attaining more appropriate reading levels for K-8 students and avoiding generating harmful questions. |
| title | MATHWELL: Generating Educational Math Word Problems Using Teacher Annotations |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2402.15861 |