Context-based Automated Scoring of Complex Mathematical Responses
Aoife Cahill, James H Fife, Brian Riordan, Avijit Vajpayee, Dmytro Galochkin
Abstract
The tasks of automatically scoring either textual or algebraic responses to mathematical questions have both been well-studied, albeit separately. In this paper we propose a method for automatically scoring responses that contain both text and algebraic expressions. Our method not only achieves high agreement with human raters, but also links explicitly to the scoring rubric – essentially providing explainable models and a way to potentially provide feedback to students in the future.- Anthology ID:
- 2020.bea-1.19
- Volume:
- Proceedings of the Fifteenth Workshop on Innovative Use of NLP for Building Educational Applications
- Month:
- July
- Year:
- 2020
- Address:
- Seattle, WA, USA → Online
- Venues:
- ACL | BEA | WS
- SIG:
- SIGEDU
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 186–192
- URL:
- https://www.aclweb.org/anthology/2020.bea-1.19
- DOI:
- PDF:
- https://www.aclweb.org/anthology/2020.bea-1.19.pdf
You can write comments here (and agree to place them under CC-by). They are not guaranteed to stay and there is no e-mail functionality.