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

• Published in: American educational research journal Vol. 50; no. 2; pp. 371 - 396
• Main Authors: Slavin, Robert E., Cheung, Alan, Holmes, GwenCarol, Madden, Nancy A., Chamberlain, Anne
• Format: Journal Article
• Language: English
• Published: Los Angeles, CA SAGE Publications 01.04.2013; American Educational Research Association
• Subjects: Data; Education policy; Educational administration; Educational Change; Educational leadership; Educational reform; Educational research; Elementary Schools; Elementary Secondary Education; Formative Evaluation; Hierarchical Linear Modeling; High Schools; Information Utilization; Mathematics; Mathematics education; Middle Schools; Outcomes of education; Pretests; Program Effectiveness; Reading; Reading Improvement; Reading Programs; School Districts; Teachers; Teaching, Learning, and Human Development; district-level reform models; formative assessment; cluster randomized experiments; data-driven instruction
• ISSN: 0002-8312; 1935-1011
• DOI: 10.3102/0002831212466909

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.