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Nese, Joseph F. T.; Kamata, Akihito – School Psychology, 2021
Curriculum-based measurement of oral reading fluency (CBM-R) is widely used across the United States as a strong indicator of comprehension and overall reading achievement, but has several limitations including errors in administration and large standard errors of measurement. The purpose of this study is to compare scoring methods and passage…
Descriptors: Curriculum Based Assessment, Oral Reading, Reading Fluency, Reading Tests
Cooke, Nancy L.; Slee, Jill M.; Young, Cheryl A. – Reading Improvement, 2020
There is some evidence that reading and spelling are complementary processes. The purpose of this study was to investigate the extent to which contextualized spelling (i.e., spelling activities within the context of reading instruction) is used to support reading in first-grade core reading programs. Analysis of 75 lessons across five programs…
Descriptors: Spelling, Reading Instruction, Teaching Methods, Reading Programs
Nese, Joseph F. T.; Kahn, Josh; Kamata, Akihito – Grantee Submission, 2017
Despite prevalent use and practical application, the current and standard assessment of oral reading fluency (ORF) presents considerable limitations which reduces its validity in estimating growth and monitoring student progress, including: (a) high cost of implementation; (b) tenuous passage equivalence; and (c) bias, large standard error, and…
Descriptors: Automation, Speech, Recognition (Psychology), Scores
Cummings, Kelli D.; Stoolmiller, Michael L.; Baker, Scott K.; Fien, Hank; Kame'enui, Edward J. – Reading and Writing: An Interdisciplinary Journal, 2015
We present a method for data-based decision making at the school level using student achievement data. We demonstrate the potential of a national assessment database [i.e., the University of Oregon DIBELS Data System (DDS)] to provide comparative levels of school-level data on average student achievement gains. Through the DDS as a data source,…
Descriptors: Academic Achievement, Formative Evaluation, Achievement Gains, Bayesian Statistics