LAUSR

Quantifying the decoding difficulty (i.e., ‘decodability’) of text is important for accurately matching young readers to appropriate text and scaffolding reading development. Since no easily accessible, quantitative, word-level metric of decodability exists, we developed a decoding measure (DM) that can be calculated via a web-based scoring application that takes into account sub-lexical components (e.g. orthographic complexity), thus measuring decodability at the grapheme-phoneme level, which can be used to judge decodability of individual words or passages. Here we report three experiments using the DM: two predicting children’s word-level errors and one predicting passage reading fluency. Generalized linear mixed effect models showed that metrics from the DM explained unique variance in children’s oral reading miscues after controlling for word frequency in two samples of children (experiments 1 and 2), and that more errors were made on words with higher DM scores for poor readers. Furthermore, the DM metrics predicted children’s number of words read correctly per minute after accounting for estimated Lexile passage scores in a third sample (experiment 3). These results show that after controlling for word frequency (experiments 1 and 2) and estimated Lexile scores (experiment 3) the model including the DM metrics was significantly better in predicting children’s word reading fluency both for individual words and passages. While further refinement of this DM measure is ongoing, it appears to be a promising new measure of decodability at both the word and passage level. The measure also provides the opportunity to enable precision teaching techniques, as grapheme-phoneme correspondence profiles unique to each child could facilitate individualized instruction, and text.