Using Data to Improve Instruction in the Great City Schools: Key Dimensions of Practice. Urban Data Study

Council of the Great City Schools. 1301 Pennsylvania Avenue NW Suite 702, Washington, DC 20004. Tel: 202-393-2427; Fax: 202-393-2400; Web site: http://www.cgcs.org

N

52

Academic Achievement; Teaching Methods; Decision Making; Hypothesis Testing; Data Analysis; Data; Intervals; Instructional Improvement; Educational Assessment; Accountability; Student Needs; Surveys; Benchmarking; School Districts; Path Analysis

Recent years have seen increased interest in data-driven decision making in education; that is, using various types of data, particularly quantitative assessment data, to inform a range of decisions in schools and classrooms. At the same time, districts, states, and schools have invested resources in tools designed to provide teachers, principals, and other key actors with ready access to (and analysis of) information regarding student performance. Of particular interest in this area is the development of interim assessments, administered at regular intervals throughout the academic year, which are designed to predict student performance on end-of-year accountability tests. In this preliminary report, the authors first review the literature on using data from interim assessments and put forth a Theory of Action that undergirds their investigation. The theory of action identifies a set of Key Dimensions of data use practice and hypothesizes that supporting conditions in states, districts, and schools can facilitate effective classroom-level use of data to respond to students' instructional needs. In the final section of this report, the authors provide an initial empirical test of this theory of action, using survey data from more than 500 teachers across four urban districts collected during the 2009-2010 school year. The focus in this preliminary report is on the predictors of whether teachers use interim assessment data to change their instruction. Future reports will extend the findings to examine the data-use practices that predict improvements in student achievement. Appended are: (1) Measures; and (2) Estimation Methods and Hypothesis Testing. (Contains 1 figure, 2 exhibits, 11 tables, and 6 footnotes.)

ERIC

67

2012

ED536737