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
- Venue:
- BEA
- SIG:
- SIGEDU
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 186–192
- Language:
- URL:
- https://aclanthology.org/2020.bea-1.19
- DOI:
- 10.18653/v1/2020.bea-1.19
- Cite (ACL):
- Aoife Cahill, James H Fife, Brian Riordan, Avijit Vajpayee, and Dmytro Galochkin. 2020. Context-based Automated Scoring of Complex Mathematical Responses. In Proceedings of the Fifteenth Workshop on Innovative Use of NLP for Building Educational Applications, pages 186–192, Seattle, WA, USA → Online. Association for Computational Linguistics.
- Cite (Informal):
- Context-based Automated Scoring of Complex Mathematical Responses (Cahill et al., BEA 2020)
- PDF:
- https://preview.aclanthology.org/starsem-semeval-split/2020.bea-1.19.pdf