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
Language:
URL:
https://www.aclweb.org/anthology/2020.bea-1.19
DOI:
10.18653/v1/2020.bea-1.19
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/2020.bea-1.19.pdf