Although there are many problems that can be fixed with duct tape, very few should be. The strong adhesive can be instrumental in temporarily repairing issues, such as reattaching a car headlight or sealing a crack in a pipe. However, it can also pose a danger: we settle for good-enough solutions that don’t target the root issue, and the compromised material is exposed and vulnerable to additional damage. The headlight circuits blow from rain damage; the fracture in the pipe grows, and the pipe bursts.
In schools, we are often forced to apply duct-tape solutions. With limited resources and time, educators commonly adopt a scrappy mentality in their work, particularly with the use of data to inform and improve instruction. The use of technology increases access to valuable real-time data, but without the right practices and systems to process the influx of data, schools resort to fragmented shortcuts and messy spreadsheets.
When I was a classroom teacher, I loved crafting spreadsheets to identify trends in student mastery. As a systems leader supporting student-centered learning, I developed even more intricate spreadsheets to deliver data insights to teachers as efficiently as possible. While these solutions had value, in retrospect, manually processing data was not the best use of time and resources, as I ended up stuck in a cycle of organizing it, too.
While scrappy duct-tape data hacks can be impressive and have temporary utility, organizing data from scratch monopolizes educator time. The key is to recognize that we must ditch the duct tape and invest in the broader practices and systems to ensure the sustainable use of data. To move beyond piecemeal data practices and avoid continual reinvention of the wheel, schools must create a vision for data-driven instruction, invest students in their data, and develop the system-wide supports.
How to use student data to differentiate instruction
It’s important to start by clarifying what data use should look like in practice. Using data can be overwhelming, as educators must navigate the deluge of data from a multitude of sources. Data-driven instruction should be focused on action: using insights from data analysis to identify and execute instructional actions to support student needs. Specifically, educators should focus on using data to differentiate instruction at the whole-group, small-group, and individual levels.
For example, Lovett Elementary (Chicago, IL) uses data to differentiate at a macro-level across classes as well as within each class. Pulling from a variety of data–including those from formative assessments, learner profiles, and student conferences– teachers are able to adjust whole-group instruction, group students for collaborative learning, and provide targeted small-group instruction. By aligning on data practices and creating routines to support the ongoing strategies, data use becomes more institutionalized rather than ad hoc.
Fostering student ownership through goal-setting
While most data use should involve educators, it’s also crucial to get students invested in their own data. Schools should co-create goals with students, co-design learner profiles, and continually review data in conferences to foster student agency.
At Cisco Junior High School (Cisco, TX), students are supported by teachers in referencing a variety of data to support ongoing goal-setting. In addition to using academic assessment data, students also monitor activity data on playlists and ongoing mastery data from pretests and posttests to create, track, and share progress on their individual goals. Supporting students in understanding their data and driving their classroom experience empowers them to own their learning within and beyond school, making data use shared and sustainable in schools.
Collaborative practices for data-driven instruction
To scale data-driven instruction, leaders need to create systems-wide structures to make data use sustainable. Individually, educators are limited in their capacity to transform learning with data. Collectively, educators can tap into collaborative structures to harness the potential of data and design equitable learning experiences for students. School and system leaders must dedicate collaborative time, structure data analysis, and continually coach educators to make data use sustainable. For example, administrators at Leadership Public Schools (Richmond, CA) create data dashboards for teachers to use in structured, periodic data reviews, ensuring educators have the time and support to plan instruction from data regularly and sustainably.
Through the intentional design of data practices and supporting structures—at the student, educator, and systems levels—schools can foster a culture of data-driven instruction that targets the core needs of students while also ensuring sustainable practices. It is through these strategies that we avoid the pitfall of duct tape and set up teachers for success with data use.
Have other thoughts on key strategies for data-driven instruction in schools? Reach out. We’re barely scratching the surface, and we look forward to getting your thoughts!