Because these assessments measure growth on a continuous, equal-interval vertical scale within and across grades, they provide more precise information on students’ growth over time than other types of measures ( von Hippel et al., 2018). As school districts have adopted practices such as universal screening and periodic universal progress monitoring using norm-referenced, vertically scaled assessments, more data are becoming available that include beginning, mid-year, and endof-year assessments. However, studies that track student growth at multiple time points across more than one academic year (i.e., beginning, middle, and end of grade) and allow for examination of both within-year and between-year growth are rare. These studies provide valuable information about how much growth occurs from the end of one grade to the end of the following grade. Other researchers have used cross-sectional data from the norming samples of standardized assessments to provide effect sizes for growth from spring of one grade through spring of the next grade ( Bloom, Hill, Black, & Lipsey, 2008 Scammacca, Fall, & Roberts, 2015). Clotfelter, Ladd, and Vigdor, 2012 Schulte, Stevens, Elliott, Tindal, & Nese 2016). Much of the research on student growth rates in reading and math has been conducted using either nationally representative longitudinal datasets such as the Early Childhood Longitudinal Survey (ECLS e.g., Judge & Watson, 2011 Reardon & Galindo, 2009 Reardon & Portilla, 2016 von Hippel, Workman, & Downey, 2018) or longitudinal data from annual state assessments of reading and math conducted in the spring of each grade (e.g. These insights can spur new research on ways to help raise the achievement of struggling students and inform policy discussions around achievement gaps. Documenting growth trajectories in the absence of researcher-introduced interventions increases our knowledge about the pattern of effects of typical instruction on student achievement. Studies of typical instruction also can shed light on the extent to which progress is being made in closing achievement gaps and when these gaps are more likely to widen or narrow. However, a better understanding of the patterns of growth that characterize and differentiate student achievement across time can help education researchers to identify malleable factors that may be responsive to efforts to reduce achievement gaps and reveal critical periods when interventions aimed at narrowing achievement gaps are more likely to be successful. The factors that initiate and maintain achievement gaps likely are multi-faceted and systemic, reaching far beyond the classroom door. Indeed, the most recent evidence suggests that achievement gaps based on household income are present at the beginning of kindergarten and change little over time ( Reardon, 2013 Reardon & Portilla, 2016). Despite these and many other state and local efforts, achievement gaps remain apparent between higher and lower income students and to some extent between White and ethnic minority students ( Reardon, 2011 Reardon & Portilla, 2016). Just since 2000, major initiatives such as the No Child Left Behind legislation and the Race to the Top program have focused considerable federal resources on students with low achievement. Efforts to close achievement gaps have been a priority for America’s education system for decades.
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