Toward Automatic Tutoring of Math Word Problems in Intelligent Tutoring Systems
Toward Automatic Tutoring of Math Word Problems in Intelligent Tutoring Systems
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DOI：
10.1109/access.2023.3290478
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发表时间：
2023
期刊：
影响因子：
3.9
通讯作者：
Pablo Arnau-González;Ana Serrano-Mamolar;Stamos Katsigiannis;Turke Althobaiti;M. Arevalillo-Herráez
中科院分区：
文献类型：
--
作者：
Pablo Arnau-González;Ana Serrano-Mamolar;Stamos Katsigiannis;Turke Althobaiti;M. Arevalillo-Herráez
数学应用题解决是指用自然语言解决数学问题，是智能教学系统中普遍采用的数学教学方法。然而，ITS的一个主要缺点是为每个支持的问题编码所有潜在解决方案的复杂性，这既耗时又费力。在这项研究中，我们提出了一种新的方法，自动转换到内部表示的ITS的一个以前看不见的MWP的声明，从而简化了添加新的MWP的任务，只需要的问题陈述。为了实现这一点，我们建议使用大型预训练语言模型将问题转换为Python代码，然后可以轻松导入ITS。实验结果表明，该方法是有效的，适合的任务，随着语言模型的不断改进，准确率有望进一步提高。
Math Word Problem (MWP) solving, which involves solving math problems in natural language, is a prevalent approach employed by Intelligent Tutoring Systems (ITS) for teaching mathematics. However, one major drawback of ITS is the complexity of encoding all potential solutions for each problem supported, which is both time-consuming and labour-intensive. In this study, we propose a novel method for automatically converting the statement of a previously unseen MWP into the internal representation of an ITS, thereby simplifying the task of adding new MWPs by only requiring the problem statement. To accomplish this, we propose the use of large pre-trained language models to translate the problem into Python code, which can then be easily imported into an ITS. Experimental results indicate that this approach is effective and suitable for the task, and as language models continue to improve, the accuracy rates are expected to increase further.