Exploiting Structured Error to Improve Automated Scoring of Oral Reading Fluency

In order to track the development of young readers' oral reading fluency (ORF) at scale, it is necessary to move away from handscoring responses to automating the assessment of ORF, while retaining the quality of the scores. We present a method for improving automated ORF scoring that utilizes an observed systematicity in machine error, namely, that cases with low estimated reading accuracy are harder to score correctly for fluency. We show that the method yields an improved performance, including on out-of-domain data.