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