@inproceedings{guerraoui-etal-2023-teach,
title = "Teach Me How to Argue: A Survey on {NLP} Feedback Systems in Argumentation",
author = "Guerraoui, Camelia and
Reisert, Paul and
Inoue, Naoya and
Mim, Farjana Sultana and
Singh, Keshav and
Choi, Jungmin and
Robbani, Irfan and
Naito, Shoichi and
Wang, Wenzhi and
Inui, Kentaro",
editor = "Alshomary, Milad and
Chen, Chung-Chi and
Muresan, Smaranda and
Park, Joonsuk and
Romberg, Julia",
booktitle = "Proceedings of the 10th Workshop on Argument Mining",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.argmining-1.3",
doi = "10.18653/v1/2023.argmining-1.3",
pages = "19--34",
abstract = "The use of argumentation in education has shown improvement in students{'} critical thinking skills, and computational models for argumentation have been developed to further assist this process. Although these models are useful for evaluating the quality of an argument, they often cannot explain why a particular argument score was predicted, i.e., why the argument is good or bad, which makes it difficult to provide constructive feedback to users, e.g., students, so that they can strengthen their critical thinking skills. In this survey, we explore current NLP feedback systems by categorizing each into four important dimensions of feedback (Richness, Visualization, Interactivity and Personalization). We discuss limitations for each dimension and provide suggestions to enhance the power of feedback and explanations to ultimately improve user critical thinking skills.",
}
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%0 Conference Proceedings
%T Teach Me How to Argue: A Survey on NLP Feedback Systems in Argumentation
%A Guerraoui, Camelia
%A Reisert, Paul
%A Inoue, Naoya
%A Mim, Farjana Sultana
%A Singh, Keshav
%A Choi, Jungmin
%A Robbani, Irfan
%A Naito, Shoichi
%A Wang, Wenzhi
%A Inui, Kentaro
%Y Alshomary, Milad
%Y Chen, Chung-Chi
%Y Muresan, Smaranda
%Y Park, Joonsuk
%Y Romberg, Julia
%S Proceedings of the 10th Workshop on Argument Mining
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F guerraoui-etal-2023-teach
%X The use of argumentation in education has shown improvement in students’ critical thinking skills, and computational models for argumentation have been developed to further assist this process. Although these models are useful for evaluating the quality of an argument, they often cannot explain why a particular argument score was predicted, i.e., why the argument is good or bad, which makes it difficult to provide constructive feedback to users, e.g., students, so that they can strengthen their critical thinking skills. In this survey, we explore current NLP feedback systems by categorizing each into four important dimensions of feedback (Richness, Visualization, Interactivity and Personalization). We discuss limitations for each dimension and provide suggestions to enhance the power of feedback and explanations to ultimately improve user critical thinking skills.
%R 10.18653/v1/2023.argmining-1.3
%U https://aclanthology.org/2023.argmining-1.3
%U https://doi.org/10.18653/v1/2023.argmining-1.3
%P 19-34
Markdown (Informal)
[Teach Me How to Argue: A Survey on NLP Feedback Systems in Argumentation](https://aclanthology.org/2023.argmining-1.3) (Guerraoui et al., ArgMining-WS 2023)
ACL
- Camelia Guerraoui, Paul Reisert, Naoya Inoue, Farjana Sultana Mim, Keshav Singh, Jungmin Choi, Irfan Robbani, Shoichi Naito, Wenzhi Wang, and Kentaro Inui. 2023. Teach Me How to Argue: A Survey on NLP Feedback Systems in Argumentation. In Proceedings of the 10th Workshop on Argument Mining, pages 19–34, Singapore. Association for Computational Linguistics.