@inproceedings{beigman-klebanov-etal-2024-miscue,
title = "From Miscue to Evidence of Difficulty: Analysis of Automatically Detected Miscues in Oral Reading for Feedback Potential",
author = "Beigman Klebanov, Beata and
Suhan, Michael and
O{'}Reilly, Tenaha and
Wang, Zuowei",
editor = {Kochmar, Ekaterina and
Bexte, Marie and
Burstein, Jill and
Horbach, Andrea and
Laarmann-Quante, Ronja and
Tack, Ana{\"\i}s and
Yaneva, Victoria and
Yuan, Zheng},
booktitle = "Proceedings of the 19th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2024)",
month = jun,
year = "2024",
address = "Mexico City, Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.bea-1.38",
pages = "459--469",
abstract = "This research is situated in the space between an existing NLP capability and its use(s) in an educational context. We analyze oral reading data collected with a deployed automated speech analysis software and consider how the results of automated speech analysis can be interpreted and used to inform the ideation and design of a new feature {--} feedback to learners and teachers. Our analysis shows how the details of the system{'}s performance and the details of the context of use both significantly impact the ideation process.",
}
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%0 Conference Proceedings
%T From Miscue to Evidence of Difficulty: Analysis of Automatically Detected Miscues in Oral Reading for Feedback Potential
%A Beigman Klebanov, Beata
%A Suhan, Michael
%A O’Reilly, Tenaha
%A Wang, Zuowei
%Y Kochmar, Ekaterina
%Y Bexte, Marie
%Y Burstein, Jill
%Y Horbach, Andrea
%Y Laarmann-Quante, Ronja
%Y Tack, Anaïs
%Y Yaneva, Victoria
%Y Yuan, Zheng
%S Proceedings of the 19th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2024)
%D 2024
%8 June
%I Association for Computational Linguistics
%C Mexico City, Mexico
%F beigman-klebanov-etal-2024-miscue
%X This research is situated in the space between an existing NLP capability and its use(s) in an educational context. We analyze oral reading data collected with a deployed automated speech analysis software and consider how the results of automated speech analysis can be interpreted and used to inform the ideation and design of a new feature – feedback to learners and teachers. Our analysis shows how the details of the system’s performance and the details of the context of use both significantly impact the ideation process.
%U https://aclanthology.org/2024.bea-1.38
%P 459-469
Markdown (Informal)
[From Miscue to Evidence of Difficulty: Analysis of Automatically Detected Miscues in Oral Reading for Feedback Potential](https://aclanthology.org/2024.bea-1.38) (Beigman Klebanov et al., BEA 2024)
ACL