Automated Math Word Problem Knowledge Component Labeling and Recommendation

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Automated Math Word Problem Knowledge Component Labeling and Recommendation
Title:
Automated Math Word Problem Knowledge Component Labeling and Recommendation
Journal Title:
Lecture Notes in Networks and Systems
Publication Date:
28 December 2024
Citation:
Tan, C. S., & Kim, J.-J. (2024). Automated Math Word Problem Knowledge Component Labeling and Recommendation. In Methodologies and Intelligent Systems for Technology Enhanced Learning, 14th International Conference (pp. 338–348). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-73538-7_30
Abstract:
The accurate annotation of math exercises and word problems according to a diverse set of knowledge components is an important task for many education applications. It is an extremely complex and AQ1 resource-intensive process, and has traditionally been done manually by experienced educators. There has been research work in recent years to apply machine learning to automate the process, however, due to the AQ2 varied datasets used by different researchers and the private nature of these datasets, there is no good benchmark for which model performs the best for such a task. Moreover, the datasets used in literature typically comprise math exercises that follow similar templates. In this paper, we AQ3 benchmark some of the best reported models on a math word problem dataset with fine-grained knowledge component annotations, and highlight the challenges in making accurate predictions. We propose models to improve the prediction accuracy, and demonstrate how they can be used to extract similar problems based on knowledge component from an unlabelled pool of questions, thus empowering educators to better identify the knowledge gaps of their students and tailor suitable practice problems for them.
License type:
Publisher Copyright
Funding Info:
This research / project is supported by the Ministry of Education, Singapore - Science of Learning Grant
Grant Reference no. : MOE-MOESOL2021-0006
Description:
This is a post-peer-review, pre-copyedit version of an article published in Lecture Notes in Networks and Systems. The final authenticated version is available online at: http://dx.doi.org/10.1007/978-3-031-73538-7_30
ISSN:
9783031735387
ISBN:
9783031735370
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