@inproceedings{beigman-klebanov-etal-2023-dynamic,
title = "A dynamic model of lexical experience for tracking of oral reading fluency",
author = "Beigman Klebanov, Beata and
Suhan, Michael and
Wang, Zuowei and
O{'}reilly, Tenaha",
editor = {Kochmar, Ekaterina and
Burstein, Jill and
Horbach, Andrea and
Laarmann-Quante, Ronja and
Madnani, Nitin and
Tack, Ana{\"\i}s and
Yaneva, Victoria and
Yuan, Zheng and
Zesch, Torsten},
booktitle = "Proceedings of the 18th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2023)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.bea-1.48",
doi = "10.18653/v1/2023.bea-1.48",
pages = "567--575",
abstract = "We present research aimed at solving a problem in assessment of oral reading fluency using children{'}s oral reading data from our online book reading app. It is known that properties of the passage being read aloud impact fluency estimates; therefore, passage-based measures are used to remove passage-related variance when estimating growth in oral reading fluency. However, passage-based measures reported in the literature tend to treat passages as independent events, without explicitly modeling accumulation of lexical experience as one reads through a book. We propose such a model and show that it helps explain additional variance in the measurements of children{'}s fluency as they read through a book, improving over a strong baseline. These results have implications for measuring growth in oral reading fluency.",
}
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<abstract>We present research aimed at solving a problem in assessment of oral reading fluency using children’s oral reading data from our online book reading app. It is known that properties of the passage being read aloud impact fluency estimates; therefore, passage-based measures are used to remove passage-related variance when estimating growth in oral reading fluency. However, passage-based measures reported in the literature tend to treat passages as independent events, without explicitly modeling accumulation of lexical experience as one reads through a book. We propose such a model and show that it helps explain additional variance in the measurements of children’s fluency as they read through a book, improving over a strong baseline. These results have implications for measuring growth in oral reading fluency.</abstract>
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%0 Conference Proceedings
%T A dynamic model of lexical experience for tracking of oral reading fluency
%A Beigman Klebanov, Beata
%A Suhan, Michael
%A Wang, Zuowei
%A O’reilly, Tenaha
%Y Kochmar, Ekaterina
%Y Burstein, Jill
%Y Horbach, Andrea
%Y Laarmann-Quante, Ronja
%Y Madnani, Nitin
%Y Tack, Anaïs
%Y Yaneva, Victoria
%Y Yuan, Zheng
%Y Zesch, Torsten
%S Proceedings of the 18th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2023)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F beigman-klebanov-etal-2023-dynamic
%X We present research aimed at solving a problem in assessment of oral reading fluency using children’s oral reading data from our online book reading app. It is known that properties of the passage being read aloud impact fluency estimates; therefore, passage-based measures are used to remove passage-related variance when estimating growth in oral reading fluency. However, passage-based measures reported in the literature tend to treat passages as independent events, without explicitly modeling accumulation of lexical experience as one reads through a book. We propose such a model and show that it helps explain additional variance in the measurements of children’s fluency as they read through a book, improving over a strong baseline. These results have implications for measuring growth in oral reading fluency.
%R 10.18653/v1/2023.bea-1.48
%U https://aclanthology.org/2023.bea-1.48
%U https://doi.org/10.18653/v1/2023.bea-1.48
%P 567-575
Markdown (Informal)
[A dynamic model of lexical experience for tracking of oral reading fluency](https://aclanthology.org/2023.bea-1.48) (Beigman Klebanov et al., BEA 2023)
ACL