Data Modeling Supports the Development of Statistical Reasoning
This project is intended to determine the effect of Data Modeling on student learning and attitude toward the mathematics of data and statistics. The student population consists of 6th grade students within a large urban school system. The student population within the system is predominantly African American (86.5%), and from low SES homes (86.5%). The system has almost equal proportions of male and female students. Students in the experimental classrooms will be instructed in statistical reasoning using the Data Modeling curriculum. These students will investigate data display, statistics and chance processes. Students will construct models of chance processes and use these to guide inference. Student achievement in these experimental classrooms will be contrasted to achievement in comparable classrooms where students participate in more standard curricular approaches to data and statistics. Teachers will engage in professional development intended to improve their understanding of statistics while supporting their implementation of the curriculum with high fidelity. Teachers will also be supported through ongoing coaching by teachers who have demonstrated previously high fidelity implementation of the curriculum. Supporting the teacher's role is an assessment system based on a progression of statistical thinking intended for summative and formative assessment. As a formative tool, it informs the teacher about milestones in student thinking, and gives the teacher opportunities to respond to this thinking using the strategies of the curriculum.
Students in control classes will be instructed about data and statistics in a "business as usual" setting. This will include lessons on data analysis, statistics, and probability that are supported by one of the following texts: Holt Middle School Math, Grade 6; Everyday Counts Algebra Readiness; and EPGY Stanford Math K-7. Their teachers will not receive professional development from the Data Modeling team before or during the test years. The research method is experimental, with random assignment by school to condition, with teachers and students nested within schools. The experiment will be conducted for two years, providing the grounds for relating effects related to students to teachers' increased experience with the innovation. The primary student-level measures include summative and formative assessments developed in a previous IES sponsored project that allows for fine-grain tracing of 6 components of statistical reasoning, a more distal measure of statistical literacy, and a survey of student attitudes. Student knowledge on all measures will be estimated with item response modeling that capitalizes on previous calibrations of items. The primary treatment-level measures include teacher logs of their practices and contents of instruction, and an observational
checklist of key instructional practices and processes that are integral to the data modeling approach. The primary analytical approach will be multilevel analyses of the student-level responses, with supplementary analyses of possible moderator effects.