Structural information in mathematical formulas for exercise difficulty prediction: a comparison of NLP representations

Ekaterina Loginova, Dries Benoit


Abstract
To tailor a learning system to the student’s level and needs, we must consider the characteristics of the learning content, such as its difficulty. While natural language processing allows us to represent text efficiently, the meaningful representation of mathematical formulas in an educational context is still understudied. This paper adopts structural embeddings as a possible way to bridge this gap. Our experiments validate the approach using publicly available datasets to show that incorporating syntactic information can improve performance in predicting the exercise difficulty.
Anthology ID:
2022.bea-1.14
Volume:
Proceedings of the 17th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2022)
Month:
July
Year:
2022
Address:
Seattle, Washington
Venues:
BEA | NAACL
SIG:
SIGEDU
Publisher:
Association for Computational Linguistics
Note:
Pages:
101–106
Language:
URL:
https://aclanthology.org/2022.bea-1.14
DOI:
10.18653/v1/2022.bea-1.14
Bibkey:
Cite (ACL):
Ekaterina Loginova and Dries Benoit. 2022. Structural information in mathematical formulas for exercise difficulty prediction: a comparison of NLP representations. In Proceedings of the 17th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2022), pages 101–106, Seattle, Washington. Association for Computational Linguistics.
Cite (Informal):
Structural information in mathematical formulas for exercise difficulty prediction: a comparison of NLP representations (Loginova & Benoit, BEA 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.bea-1.14.pdf
Data
MATH