Purpose: In this study, the research team will examine whether a professional development program designed to help teachers make better use of assessment and other data, Using Data (UD) will translate into improved student performance in mathematics. The team will evaluate the effectiveness of the intervention using a randomized controlled trial. At the conclusion of the study the team will have answered two primary questions: (a) whether students of treatment teachers, in comparison to control teachers, use a greater number of high-capacity data strategies; and (b) whether students of the UD teachers show greater improvement in mathematics achievement than students of the control teachers?

Project Activities: To answer the question of effectiveness of the UD intervention, the research team will randomly assign a total of 60 schools to either receive the intervention or to serve as controls. In each treatment school four teachers and one instructional leader will receive the intervention and serve as the site’s data team. Student outcomes will be measured using state standardized tests before and after intervention. The control teachers will receive the treatment after the two-year implementation trial is finished.

Products: At the conclusion of the project, the team will have evidence as to the effects of the UD intervention on both teacher and student outcomes. In addition, the team will produce peer-reviewed publications.

Structured Abstract

Setting: The study will take place in a single large district.

Sample: Participants will include 60 schools. A total 150 teachers in grades 3, 4, or 5 will participate in the treatment, including 30 data coaches (lead teachers or instructional specialists).

Intervention: The Using Data (UD) intervention is intended to work at both the teacher and school level. A data coach, a group of teachers, and sometimes an administrator receive training on using high-capacity data strategies and then meet periodically to review student level data. In addition to improving their own understanding of data use, this team supports the development of a school-wide data culture.

Research Design and Methods: The initial year of the project will be used for sample recruitment and instrument refinement and validation. Once a site and sample are recruited, the data teams in the treatment schools (consisting of a data coach and a small group of teachers) will receive UD through professional development workshops and technical assistance days, spread over the 2011–2012 and 2012–2013 academic years. Using a block randomized design, schools will be randomly assigned to treatment or control. Schools will be blocked on prior test scores, racial composition, eligibility for free or reduced price lunch, and Title I status. Teachers in both the control and treatment groups will answer several instruments including data scenarios and questionnaires. These instruments will indicate their ability to use high-capacity data strategies. Student outcomes will be measured using the state standardized achievement test in mathematics.

Control Condition: The control schools will use practice as usual and will consist of approximately the same number of teachers. The control teachers will receive the treatment after the two-year implementation trial is finished.

Key Measures: For student performance, the research team will use the state standardized achievement test.

Data Analytic Strategy: The research team will use Hierarchical Linear Modeling (HLM) to examine the treatment effects because the students are nested in classes, and classes nested within schools.

** This project was submitted to and funded under Teacher Quality: Mathematics and Science Education in FY 2010.