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Toward Automatic Tutoring of Math Word Problems in Intelligent Tutoring Systems

Arnau-González, Pablo; Serrano-Mamolar, Ana; Katsigiannis, Stamos; Althobaiti, Turke; Arevalillo-Herráez, Miguel

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Authors

Pablo Arnau-González

Ana Serrano-Mamolar

Turke Althobaiti

Miguel Arevalillo-Herráez



Abstract

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.

Citation

Arnau-González, P., Serrano-Mamolar, A., Katsigiannis, S., Althobaiti, T., & Arevalillo-Herráez, M. (2023). Toward Automatic Tutoring of Math Word Problems in Intelligent Tutoring Systems. IEEE Access, 11, 67030-67039. https://doi.org/10.1109/access.2023.3290478

Journal Article Type Article
Acceptance Date Jun 26, 2023
Online Publication Date Jun 28, 2023
Publication Date 2023
Deposit Date Jun 27, 2023
Publicly Available Date Jul 12, 2023
Journal IEEE Access
Publisher Institute of Electrical and Electronics Engineers
Peer Reviewed Peer Reviewed
Volume 11
Pages 67030-67039
DOI https://doi.org/10.1109/access.2023.3290478
Public URL https://durham-repository.worktribe.com/output/1170152

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