@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.",
}

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.
Beigman Klebanov, Beata Suhan, Michael Wang, Zuowei O{'}reilly, Tenaha
A dynamic model of lexical experience for tracking of oral reading fluency