@inproceedings{cahill-etal-2020-context,
title = "Context-based Automated Scoring of Complex Mathematical Responses",
author = "Cahill, Aoife and
Fife, James H and
Riordan, Brian and
Vajpayee, Avijit and
Galochkin, Dmytro",
editor = "Burstein, Jill and
Kochmar, Ekaterina and
Leacock, Claudia and
Madnani, Nitin and
Pil{\'a}n, Ildik{\'o} and
Yannakoudakis, Helen and
Zesch, Torsten",
booktitle = "Proceedings of the Fifteenth Workshop on Innovative Use of NLP for Building Educational Applications",
month = jul,
year = "2020",
address = "Seattle, WA, USA → Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.bea-1.19",
doi = "10.18653/v1/2020.bea-1.19",
pages = "186--192",
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.",
}
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%0 Conference Proceedings
%T Context-based Automated Scoring of Complex Mathematical Responses
%A Cahill, Aoife
%A Fife, James H.
%A Riordan, Brian
%A Vajpayee, Avijit
%A Galochkin, Dmytro
%Y Burstein, Jill
%Y Kochmar, Ekaterina
%Y Leacock, Claudia
%Y Madnani, Nitin
%Y Pilán, Ildikó
%Y Yannakoudakis, Helen
%Y Zesch, Torsten
%S Proceedings of the Fifteenth Workshop on Innovative Use of NLP for Building Educational Applications
%D 2020
%8 July
%I Association for Computational Linguistics
%C Seattle, WA, USA → Online
%F cahill-etal-2020-context
%X 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.
%R 10.18653/v1/2020.bea-1.19
%U https://aclanthology.org/2020.bea-1.19
%U https://doi.org/10.18653/v1/2020.bea-1.19
%P 186-192
Markdown (Informal)
[Context-based Automated Scoring of Complex Mathematical Responses](https://aclanthology.org/2020.bea-1.19) (Cahill et al., BEA 2020)
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.