Team Collaboration: How do leaders build in time and team structures for data-driven instruction?

Using data effectively and efficiently in classrooms is key to personalizing instruction in service of equitable student outcomes. Educators often go through lengthy data cycles (from the creation and collection of data to the organization and analysis of data), encountering numerous challenges and consequently seeing a reduction in the time they have to actually plan instruction. Without dedicated time, streamlined structures, and comprehensive supports, teachers can become inundated with data, reducing the energy and focus they have available to realize the potential of data.

In order to serve students equitably, schools must leverage data to both inform daily instruction as well as continually evaluate and improve programs in the longer term. Employing a cohesive vision with well-designed structures for data use – including intentional time, capacity-building, staffing, and tools – also ensures that data use is efficient, allowing educators to spend as much effort as possible planning actions and engaging in instruction – rather than getting “lost” in the process of data analysis.

The research says...

• In urban schools, there are meaningful links between teacher and principal data use and student achievement on state assessments in some grades and subjects. School-level supports for data use (such as data infrastructure, time to review and discuss data, professional development, and staff capacity) were also related to higher student achievement on state tests.
• Leaders can support data-driven instruction by investing in the staff, training, dedicated time, and professional development needed to effectively integrate tools and practice.
• Teachers see data use as an integral part of good teaching and make data work for their students, but a lack of support and training leaves them to fill the gaps and make data use possible on their own.

This guide focuses on the ongoing structures and supports leaders can put into place to enable effective use of data. However, it’s important to note that before jumping into how to collaborate around data, you first need to think about two key pieces:

1. Ensuring student data privacy – Educators access and manage copious amounts of student data, all of which can be powerful for tailoring student learning – but also dangerous if in the wrong hands. It is crucial that leaders and educators stay abreast of the best practices for maintaining student data privacy. Explore Common Sense’s Student Data & Privacy resources and CoSN’s Protecting Privacy Toolkit to find resources, tools, and practices to keep student data secure.
2. Organizing data by an aligned data model and set of standards – Parsing through data effectively requires an organizing framework to focus analysis. Leaders must determine the set of academic (often derived from Common Core State Standards) and non-academic data standards, as well as the sub-standards (at the skill-level) for schools and systems. By aligning on and ensuring data are accessible based on a model, educators can effectively plan instruction and assess learning against such standards. Explore the Common Education Data Standards for more guidance and resources around organizing data.

Once leaders have implemented secure data practices and determined data standards for your school or system, they can foster the collaborative use of data by assuring protected time, implementing strategies to engage with the data itself, and offering opportunities for coaching and support. While exploring these key components, make sure to consider these important pieces of the puzzle:

• Assessment literacy – How can we ensure educators understand the various types of assessments, purposes of each, and associated data produced?
• Professional learning – How can we support adult learning to develop educator data literacy and skills?

• Resources and tools – How can we creatively tap into various staff members and online tools (e.g., data dashboards, collaboration software) to support these structures and data-driven instruction?

Dedicate time to data analysis and collaboration

Leaders should first start by carving out dedicated time focused on using data; otherwise, data use can be deprioritized, inconsistent, and/or opportunistic. To enable the ongoing use of data, protected time should be built into schedules in various ways throughout the day and year (e.g., during daily planning blocks, weekly staff meetings, regular in-service staff days, or through additional blocks and days for teacher planning). While the specific activities during these allotted times may vary, they should all be collaborative, continually develop educator skills, and enable teachers to identify and plan targeted instruction based on the data in an actionable manner.

When carving out time for data use, think about these questions:

• How can leaders rearrange schedules to allow for planning time?
• Who should be present during these meetings? How can leaders optimize team collaboration?
• How can leaders schedule data time and activities based on needs, particularly around influxes of data from assessments?

Structure analysis through data inquiry cycles

Considering the wide variety of possible assessment types, knowing when to use which data can be challenging. Leaders can promote streamlined practices and guide educators in using prioritized data through data inquiry cycles, which are structured processes that take educator teams through the stages of data collection, organization, analysis, and action. Schools should implement a variety of data cycles based on the types of data and assessments they review.

• Daily and weekly cycles: use formative assessment and ongoing progress data to identify daily shifts in instruction and individual interventions.
• Unit of study cycles (often 3-5 weeks): use summative assessments to plan opportunities for reteaching and diagnostic data to adjust instruction for upcoming units.
• Interim cycles (often 6-8 weeks): use data from interim assessments to reflect on instruction, adjust the pacing of learning, and determine interventions.
• Term cycles (often based on trimesters or semesters); use summative data to reflect on instructional design and efficacy in order to improve teacher practice.

When designing data inquiry cycles, consider these questions:

• How can leaders ensure all data collected from assessments are meaningful and actionable, avoiding data that are not useful?
• Which data should be managed by educators? Which data should be managed by school or district leaders?
• What routine protocols and practices should be implemented to facilitate data use?

Support educators with ongoing team and coaching activities

Leaders must support educators through coaching structures and team processes. Coaches can help educators throughout the whole data cycle, providing guidance, building educator skills, and even lending additional capacity when needed. School and district staff can also make data use more efficient and effective by streamlining the initial stages of the data cycle, ensuring data are readily translated into insights – by coaches or data tools – so educators can focus on targeted instructional planning.

Teachers can also continually build their skills through peer collaboration opportunities like professional learning communities (PLCs). In such structures, teachers regularly meet with similar team members – across a common grade level or content area – to analyze data, plan instruction, reflect on practice, and holistically understand the needs of their students. Through promoting collaboration around data use, these communities also enable the spread of effective data practices.

When developing coaching and team processes, think about the following questions:

• What skills do educators need to build through coaching and team collaborations?
• How can leaders strategically distribute leadership and ownership across the data cycle?
• How can leaders design structures to ensure the sustainable and effective use of data?

Once you have a grasp on creating the culture and practices needed to support data-driven instruction, you should consider strategically building capacity and thinking about ways to scale practices. You can take data use in your schools to the next level by:

• Developing internal data capacity: Increase the data capacity across a network of schools through systems-level roles and technological solutions.
• Exploring leadership design tensions: Scaling systems across schools requires intentionality. Explore these seven design tensions to align with your team and think through scaling data systems.

Additional Resources and Content:

• Leading Educator's Blog Series, Bringing an Equity Lens to Data Reviews: Part 1 and Part 2
• Forum Guide to Personalized Learning Data
• Harvard’s Data Wise Project
• Data Matters Framework
• DQC: ESSA Resources to Make Data Work for Students