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
- Editors:
- Ekaterina Kochmar, Jill Burstein, Andrea Horbach, Ronja Laarmann-Quante, Nitin Madnani, Anaïs Tack, Victoria Yaneva, Zheng Yuan, Torsten Zesch
- Venue:
- BEA
- 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
- 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)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2022.bea-1.14.pdf
- Data
- MATH