A dynamic model of lexical experience for tracking of oral reading fluency
Beata Beigman Klebanov, Michael Suhan, Zuowei Wang, Tenaha O'reilly
18th Workshop on Innovative Use of NLP for Building Educational Applications Paper
TLDR:
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 passa
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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.