This project will design opportunities for mathematics and science teachers to coordinate their instruction to support a more coherent approach to teaching statistical model-based inference in middle school. It will prepare teachers to help more students develop a deeper understanding of ideas and practices related to measurement, data, variability, and inference. Since there is little research to show how to productively coordinate learning experiences across disciplinary boundaries of mathematics and science education, this project will address this gap by: (1) creating design principles for integrating instruction about statistical model-based inference in middle grades that coordinates data modeling instruction in mathematics classes with ecology instruction in science classes; (2) generating longitudinal (2 years) evidence about how mathematical and scientific ideas co-develop as students make use of increasingly sophisticated modeling and inferential practices; and (3) designing four integrated units that coordinate instruction across mathematics and science classes in 6th and 7th grade to support statistical model-based inference.

This project will use a multi-phase design-based research approach that will begin by observing teachers' current practices related to statistical model-based inference. Information from this phase will help guide researchers, mathematics teachers, and science teachers in co-designing units that integrate data modeling instruction in mathematics classes with ecological investigations in science classes. This project will directly observe students' thinking and learning across 6th and 7th grades through sample classroom lessons, written assessment items, and interviews. Data from these aspects of the study will generate evidence about how students make use of mathematical ideas in science class and how their ecological investigations in science class provoke a need for new mathematical tools to make inferences. The resulting model will integrate mathematics and science learning in productive ways that are sensitive to both specific disciplinary learning goals and the ways that these ideas and practices can provide a better approximation for students to knowledge generating practices in STEM disciplines.