Data Centered Instruction in a Learner Centered World

How does data drive instruction and why is it important?

As an educator, I feel like we hear the word data and immediately roll our eyes. The mere idea of analyzing countless assessment results and combing through student by student can seem dauntless, repetitive, and sometimes even pointless. But does it have to be that way? Do we have to spend valuable time over analyzing assessments and pouring over the reasons our students didn’t perform well? Is it necessary? Is there a better way? When performed correctly, the answer is yes.

Paul Bambrick-Santoyo (2010) believes that “when correctly applied, data-driven instruction has led to dramatic gains in student performance nationwide” (p. xxii). Data-driven instruction is the idea that all instruction is rooted in and focused on the question-are students learning? Rather than devoting all of our time to what and how students are learning, this approach simply looks at data to truly understand if students are truly learning and applying knowledge of skills to additional learning. Teaching from data analysis offers a “clear-eyed, fact-based focus on what students actually learned” (p. xxv).

The Heart of Data-Driven Instruction

While data can, and should, be collected in multiple ways, the most common way to gather data is through assessment. Whether we like it or not, what we teach has to be directed towards what we test. But, what if what we tested directed our teaching? “The practices of data-driven instruction are inextricably bound up with the process of assessment” (p. 6). Most teachers will vigorously teach to their grade-level standards until they are blue in the face. However, “standards are meaningless until you define how you will assess them” (p. 7). When one standard can be interpreted in a plethora of ways, this can create sinkholes in what is being taught. We must know how standards are being assessed to properly teach skills

Interim assessments are the pathway that leads to quality data-driven instruction. In essence, they are the “road map for instruction” (p. 8). When frequent and rigorous interim assessments are planned out and administered at the appropriate times, they can become the most powerful indicator of student success. A common mistake made when creating interim assessments is the fact that many educators create the assessment after learning has already taken place. The upside down idea of data-driven instruction actually calls for the opposite. In order for purposeful data to come from these assessments, they need to be developed before any learning ever takes place.

Interim Assessments in Texas

The Texas Education Agency (TEA) has created optional interim assessments that can be offered during the school year to districts. While these assessments are not available as frequently as they should be, they do offer the opportunity for students to see the testing format through the Learning Management Platform TEA uses for STAAR, as well as “provide actionable data that enable educators to monitor student progress and predict student performance on the STAAR summative assessments” (Educator Guide STAAR Interim Assessments Educator Guide, 2022). Once the assessments have been taken, the Centralized Reporting System (CRS) then generates reports that compare results at the state, district, campus, and individual level. One of the reports that is created through the CRS is a report detailing the students' responses to each question. This type of report is the most beneficial piece of evidence in planning for the next steps of instruction.

An Analysis of Analyzation

In implementing data-drive instruction, analyzing student responses on individual questions and the assessment as a whole is crucial. There must be balance between the two. Bambrick-Santoyo (2010) writes that there are four main levels in which assessment data should be analyzed- question level, standard level, individual student level, and whole class level (p. 41). By looking at data from this perspective, teachers can view data with a more intentional lens.

Within the analysis phase, it’s also important to examine questions and standards relationships side by side. By doing this, educators can more easily establish and clarify the strengths and weaknesses and then create a plan to target instruction of needs. When looking at what students missed on a test, it is imperative to have the test in hand to view the questions. If we don’t know how the standard was assessed, we can’t truly understand the weakness or misconception.

Once analysis of the data has been observed, meaningful conversations with leadership need to happen. By meaningful, I mean a plan is made before the conversation is over. As an educator, this idea was pivotal for me. I can think of multiple conversations I have had about a student’s weaknesses, but I often didn’t walk away from the conversation with a plan to strengthen the weaknesses. By having a plan, our pouring over the data is worth it. When we put it all together to form a dot to dot map of where our students need to go next, we have taken the next crucial step in pushing our students towards excellence.

Plan with Purpose

Once a plan has been established, the leg work begins. Effectively implementing the action plan is the step that receives the most momentum, but is often unsuccessful. The reasoning? Classroom teachers who have always taught the same way because that’s how they’ve always done it. Teachers are great teachers, but sometimes we are terrible students. We become very resistant to change and generally dig our heels in at the idea of teaching in a new way. However, when we cultivate new practices into our teaching that are specifically targeted towards the weaknesses in the data, we are bettering our own teaching, which in turn, will better student learning (p. 75).

There are a million ways to teach and reteach a standard, and there isn’t a correct one size fits all approach. These approaches will vary, student by student, class by class, and teacher by teacher. However, one thing that must remain constant throughout the implementation process is that of accountability. This is where the rubber meets the road and we are held responsible for our actions in effectively operating from the plan. When our leaders are also made aware of our goals and plans, we then have another member on our side of the court that is able to support, encourage, and assist in any way needed. Implementing the plan does not have to, and does not need to, be a solo performance. We are better together.

Collaborative Culture

The foundational stages of data-driven instruction are most definitely an uphill climb that plateaus when data analysis begins transforming classroom practices and then descends as students begin gaining confidence and ownership in their learning. When looking at the entire process as a whole, it can become extremely overwhelming. It’s important for school leaders to implement effective professional development over the topic and provide teachers with resources to borrow to begin. The wheel doesn’t have to be recreated here, it just needs to be moved to a different axle.

Motivation and positivity trickle from the top down. Leaders must be positive and encouraging in implementing data-driven instruction, while also providing ample opportunities for the staff to educate themselves on the research and purpose behind this movement. Educators need to understand the WHY behind the shift, while also understanding the fruit from this labor will not ripen quickly. Ongoing professional development and support from leadership that allows time for creating, planning, administering, and then analyzing interim assessments are indispensable pieces of the puzzle that must be carefully placed. I think that once teachers can understand the destination, they’ll be more willing to go along for the ride.

Conclusion

Nothing worth having comes without obstacles. Data-driven instruction is an obstacle course on steroids. However, the research speaks for itself and clearly demonstrates that teaching from data, rather than for data, helps cultivate a learner centered environment. By jumping through many purposeful hoops, this approach can transform classrooms and in turn, transform the future through our students. This type of instruction helps to establish and nurture the growth mindset outlook that implores students to understand where they are and where they aren’t-yet.

Resources

https://www.edutopia.org/blog/7-steps-becoming-data-driven-school-eric-crites

References

Bambrick-Santoyo, P. (2010). Driven by data : a practical guide to improve instruction. Jossey-Bass.

Educator Guide STAAR Interim Assessments Educator Guide. (2022). https://tea.texas.gov/sites/default/files/2022-2023-interim-assessments-educator-guide.pdf