Effects of a Data-Driven District Reform Model on State Assessment Outcomes

Abstract

A district-level reform model created by the Center for Data-Driven Reform in Education (CDDRE) provided consultation with district leaders on strategic use of data and selection of proven programs. Fifty-nine districts in seven states were randomly assigned to CDDRE or control conditions. A total of 397 elementary and 225 middle schools were followed over a period of up to 4 years. In a district-level hierarchical linear modeling (HLM) analysis controlling for pretests, few important differences on state tests were found 1 and 2 years after CDDRE services began. Positive effects were found on reading outcomes in elementary schools by Year 4. An exploratory analysis found that reading effects were larger for schools that selected reading programs with good evidence of effectiveness than for those that did not.

Factors promoting and hindering data-based decision making in schools

“…Finally, it is important not only to develop, implement, and evaluate professional development programs and interventions for data use (e.g., programs such as those discussed by Boudett et al, 2005;Campbell & Levin, 2009;Carlson et al, 2011;Lai et al, 2009;Slavin, Cheung, Holmes, Madden, & Chamberlain, 2011;Timperley & Parr, 2009) but also to invest in teacher education colleges. Little attention is paid to data use in the curriculum of most teacher education colleges (Mandinach & Gummer, 2013).…”

Longitudinal Effects of Teacher Use of a Computer Data System on Student Achievement

“…After 1 year of the CDDRE initiative, Carlson et al (2011) reported a small positive statistically significant impact of the program on math achievement and a positive, though not statistically significant, impact on reading achievement. After 4 years of the CDDRE intervention, Slavin et al (2013) found stronger effects on elementary reading and math achievement. However, effects on middle school achievement were less substantial, with benefits to reading and math achievement found only in the 2 years of intervention.…”

Measuring opportunity: Redirecting education policy through research

“…Use is guided by context, judgment and policy. The effects of achievement measures, for instance, depend on whether they are deployed in summative or formative ways, the extent to which they are used in high-stakes environments, whether they are focussed on specific content domains (e.g., reading and math), and how they are 2 Several studies have evaluated reforms that foreground the use of achievement data, including: performance measurement (Propper & Wilson, 2003;Verbeeten, 2008), data-driven district initiatives that focus on benchmark assessments and data consultants (Carlson, Borman, & Robinson, 2011;Slavin, Cheung, Holmes, Madden, & Chamberlain, 2013); assessing and reassessing student reading comprehension, creating reports and employing data-coaches (Quint, Sepanik, & Smith, 2008); benchmark and formative assessment reforms (Konstantopoulos, Miller, & van der Ploeg, 2013), and high-stakes testing and test-driven accountability (Jacob, 2005;Lee, 2008). These studies, several of them experimental, came to mixed conclusions about the academic effects of focusing on achievement data.…”