In this project, researchers and school district partners are collaborating to develop a system of practical measures, routines and representations to support the implementation of instructional improvement strategies in middle-grades mathematics. We intend to continue to collaborate with practitioners and researchers in this work. You can learn more about our collaborators, consultants and research team here. If you'd like to join us in this work, we would love to learn with you.
We are developing a system of practical measures that provide information about key aspects of middle-grades mathematics teaching and high-quality professional learning. Practical measures are designed to provide frequent, rapid feedback that enables practitioners to assess and improve their practices. They are in contrast to research measures, which tend to be laborious to administer and thus unable to inform implementation on a rapid basis, and accountability measures, which tend to be broad in scope and unable to pinpoint where to take action.
An assumption of this work is that these tools will not, by themselves, lead to instructional improvement. Current research focuses on the potential of embedding the tools in ongoing professional learning for various role groups (e.g., teachers, coaches, district math leaders). We are investigating the potential of different routines and representations for supporting various role groups to interpret and act on the basis of the resulting data.
Data visualizations provide powerful supports to aid in making sense of practical measures and enhancing improvement routines. We are co-designing a data visualization platform with each of our partners to document how the design of visual analytics (when closely aligned with the goals and routines of our partners) can play an important role in improvement efforts. Our design studies highlight our participatory process, we’re trying to understanding the range of sensemaking that can occur with data representations (charts, graphs etc.), and the conditions under which data is taken up productively and unproductively.