At Education Elements we believe deeply in the power of helping school districts become more responsive organizations. We believe that schools that are responsive to the changing conditions around them can better respond to the educational needs of their students. But in many school districts, this means moving away from deeply entrenched school management and leadership practices that were developed for educational success in the early 20th century – practices that were designed for a much less diverse student population and to prepare students for highly structured environments, like factories and large organizations.
In order to stay competitive and seize the opportunities presented by new ideas and innovations, school districts must reinvent how they work and embrace organizational models that allow for continuous innovation, learning, and change. This means more distributed authority, getting comfortable with experimentation and “safe enough to try”, planning for change instead of perfection, sharing information more openly, and focusing on being learning organizations.
A strong data culture can drive responsive practices within schools and teams, and across districts as a whole. In districts with strong data culture, systems, policies, and practices support timely, in the moment iteration. When the needs of their end users change, a responsive data culture can quickly pivot to support those needs. For example, when a global pandemic shuts down schools indefinitely, district and school leaders can immediately access information they’ve always collected but rarely used, like which students have access to computers at home.
In a responsive data culture, various data are collected for evaluative purposes, to determine whether or not a program or intervention achieved its intended outcomes. But data are also collected for responsive improvements and iterations along the way. For example, when a school implements a new math curriculum, the principal doesn’t wait until the end of the year to look at the data. She reviews data every month, or even every week to determine whether the program is being implemented with fidelity and whether or not there are signs of incremental change. Teams establish habits of using and talking about data and the learning from those conversations allows them to be agile in times of rapid change.