Unfinished but not lost: 7 ways to use assessment data to support student learning during COVID-19

Interrupted learning during the pandemic has increased inequity in education. The research proves this. But there’s a lot we can do to come together as educators and support children this year. One critical area is how we talk about, think about, and use assessment data.

Team collaboration is so important when creating a culture of data use at your school. Even better is the kind of collaboration that goes beyond individual action and results in collective efficacy: the shared commitment to working toward a common goal of student progress and the perseverance to continue problem-solving until we’re successful in meeting our goal.

So how can we embrace more effective assessment data use? A great place to start is by becoming even more intentional in discussions about data and about how teams function. When we identify patterns in mindset, language, and behavior that have become entrenched over time—and then determine whether or not those patterns are productive—we can start to change them. Here are seven things to try.

1. Begin with psychological safety

When Google decided to find out what made its internal teams successful—or less than successful—it created an initiative nicknamed Project Aristotle. The initiative’s findings were very clear: what really mattered for the success of a team, above all else, was psychological safety.

When we identify patterns in mindset, language, and behavior that have become entrenched over time—and then determine whether or not those patterns are productive—we can start to change them.

A successful data culture depends on this idea of psychological safety, defined as a shared belief that team members can speak up and take risks without fear of embarrassment or reprisal. How can you foster this in your school? When everyone feels safe to express contradictory opinions, voice concerns, and share wonderings, all while knowing that they’ll be safe from any kind of retribution or estrangement from the group, they will feel free to challenge norms and have the kinds of open discussions that lead to constructive data analysis. And in a year that has been anything but normal, it’s important to challenge the status quo.

2. Ground data interpretation in a learner orientation

Continue to keep students at the forefront of your data investigations, as you always do. But remember that you and your fellow educators are also always learning. By bringing awareness to the unconscious biases that you and your colleagues may bring to a discussion, you can prevent missteps in interpretation and keep the focus on facts. Remain open to others’ ideas as you interpret data and discuss what steps to implement.

3. Use a strength-based approach

You give your students lots of credit for their successes, right? Do that in your conversations about assessment data with colleagues, too. Don’t just focus on what isn’t working or where there’s room for improvement. While it’s crucial to mine data for ways to support students and help them make up unfinished learning, it’s just as crucial to focus on what is working—and why.

4. Establish norms and expectations so collaboration and psychological safety can flourish

As Robert Garmston and Bruce Wellman explain in The Adaptive School: A Sourcebook for Developing Collaborative Groups, universal norms of collaboration are invaluable. There’s a lot of research to support their worth, and they are used in many fields, from business and counseling to education. What are they, exactly? They’re the behaviors we’ve decided help ensure group success: being on time, sticking to the agenda, and paying attention.

Don’t just focus on what isn’t working or where there’s room for improvement.

Working agreements, by contrast, are the specific behaviors that group members determine will help support their needs for psychological safety and efficacy as they work to achieve their desired outcomes. Because working agreements are reflective of each unique working environment, even the acts of coming up with and sticking to them can help build awareness, empathy, and collective efficacy in your team.

Team working agreements might focus on relationships between team members; on mindsets, attitudes, and habits; on how to constructively give and receive feedback; or on how best to facilitate collaboration. But these are just examples. For a working agreement to be effective, it has to come from honest discussion between team members and team leaders.

Beginning and ending each meeting by restating norms and working agreements, along with regular group and individual assessments of consistency, can help these concepts take root.

5. Work toward collective efficacy

High-performing teams focus, commit to one another, reinforce equity of thought and of talk time, and assume collective responsibility. But those group behaviors really depend on the repeated decisions and actions of individuals. The goal is collective efficacy, where:

Independence becomes interdependence.
Compliance turns into accountable commitment.
Collaboration shifts into joint work.
The group becomes a team.

Group behavior is a construct, meaning it is made up of the collective actions of the individuals. When individual team members believe in a group’s mission and engage in effective behaviors over and over again, the group develops increased relational trust and productive collaborative patterns emerge, bringing the team ever closer to achieving its desired outcomes.

6. Create an action plan

Charting a course can help you know where you’re headed, and when you’re getting off track. Here are some questions you can ask as you dive into the work of creating stronger teams and more successful data use:

To what degree do the teams you lead and support have high levels of psychological safety?
To what degree do these teams adhere to universal norms of collaboration?
To what degree do your teams adhere to member-defined working agreements?
To what degree are you able to leverage those norms and agreements to achieve collective efficacy?

Once you have answers to these questions, it can be easier to determine the steps you need to take to accomplish your goals. Just like when you work on student goal setting, consider long- and short-term milestones and ways to evaluate your progress along the way. Think, too, about what will keep you and the team motivated.

7. Make team meetings more purposeful

Think about how you’d describe the processes, protocols, or practices in your current data conversations. The process steps below are designed to help fine-tune data conversations and create a high-performing team, through focus, reaffirmation of commitment, equity of thought and of talk time, and the reinforcing of collective responsibility.

Predict. Before you see the data, activate what you know, make predications, and surface assumptions. Note what questions you’re asking.
Observe. Focus on the facts and use numerical information in your discussions. The cycle of interpretation, analysis, and action can happen rapidly in education. Make sure you allow time to do critical analysis of perspective and storylines.
Explain. What are some probable explanations for what you see? Explore multiple theories of causation, prioritize them, and ask what other data sources you need to confirm or disprove what you see. Make sure you find a balance between the five causal categories: students, infrastructure, curriculum, instruction, and teachers.
Act. Be clear about what you’re trying to accomplish. Ask yourself: What are we trying to do? How will we measure and monitor, and when? Convert problem statements into goals and detail next steps. Implement data-driven monitoring systems and continue to come back to the action plan and reassess throughout the year.

Stay the course

The culture and environment in which data is discussed is critical to how it is interpreted and how effectively insights can be turned into action. Help create the right conditions for continuous student improvement by identifying the building blocks of a strong culture of data use and exploring model data-conversation protocols.

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