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, and the analytic methods we outline, we illustrate one way that schools can engage in system-level formative evaluation by examining their students’ gains across an academic year conditional on initial skill level relative to the performance of a large sample of other schools. We provide the empirical Bayes estimates of school-level effects and their associated standard errors for second grade, DIBELS oral reading fluency using a percentile band plot. We illustrate a practical way that schools could use this output to improve their data-based decision making procedures.
Reading and Writing – Springer Journals
Published: May 5, 2014
It’s your single place to instantly
discover and read the research
that matters to you.
Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.
All for just $49/month
Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly
Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.
All the latest content is available, no embargo periods.
“Whoa! It’s like Spotify but for academic articles.”@Phil_Robichaud