[cross-posted at eduwonkette; see also her response]

When eduwonkette asked me to guest blog about data-driven decision-making in schools, I eagerly agreed. Why? Because in my work with numerous school organizations in multiple states, I have seen the power of data firsthand. When done right, data-driven education can have powerful impacts on the learning outcomes of students.

Unfortunately, most school districts still are struggling with their data-driven practice. Much of this is because they continue to think about using data from a compliance mindset rather than using data for meaningful school improvement. An uninformed model of data-driven decision-making looks something like this:

DDDM_Model_Old

This is the NCLB model. Schools are expected to collect data once a year, slice and dice them in various ways, set some goals based on the analyses, do some things differently, and then wait another whole year to see if their efforts were successful. Somehow, this model is supposed to get schools to 100% proficiency on key learning outcomes. This is dumb. It's like trying to lose weight but only weighing yourself once a year to see if you're making progress. Compounding the problem is the fact that student learning data often are collected near the end of the year and given back to educators months later, which of course is helpful to no one.

A better model looks something like this:

DDDM_Model

The key difference in this model is an emphasis on ongoing progress monitoring and continuous, useful data flow to teachers. Under this approach, schools have good baseline data available to them, which means that the data are useful for diagnostic purposes in the classroom and thus relevant to instruction. The data also are timely, meaning that teachers rarely have to wait more than a few days to get results. In an effective data-driven school, educators also are very clear about what essential instructional outcomes they are trying to achieve (this is actually much rarer than one would suppose) and set both short- and long-term measurable instructional goals from their data.

Armed with clarity of purpose and clarity of goals, effective data-driven educators then monitor student progress during the year on those essential outcomes by checking in periodically with short, strategic formative assessments. They get together with role-alike peers on a regular basis to go over the data from those formative assessments, and they work as a team, not as isolated individuals, to formulate instructional interventions for the students who are still struggling to achieve mastery on those essential outcomes. After a short period of time, typically three to six weeks, they check in again with new assessments to see if their interventions have worked and to see which students still need help. The more this part of the model occurs during the year, the more chances teachers have to make changes for the benefit of students.

It is this middle part of the model that often is missing in school organizations. When it is in place and functioning well, schools are much more likely to achieve their short- and long-term instructional goals and students are much more likely to achieve proficiency on accountability-oriented standardized tests. Teachers in schools that have this part of the model mastered rarely, if ever, complain about assessment because the data they are getting are helpful to their classroom practice.

NCLB did us no favors. It could've stressed powerful formative assessment, which is the driving engine for student learning and growth on whatever outcomes one chooses. Instead, it went another direction and we lost an opportunity to truly understand the power of data-driven practice. There are hundreds, and probably thousands, of schools across the country that have figured out the middle part of the model despite NCLB. It is these schools that are profiled in books such as Whatever It Takes and It's Being Done (both recommended reads) and by organizations such as The Education Trust.

When done right, data-driven decision-making is about helping educators make informed decisions to benefit students. It is about helping schools know whether what they are doing is working or not. I have seen effective data-driven practice take root and it is empowering for both teachers and students. We shouldn't unilaterally reject the idea of data-driven education just because we hate NCLB. If we do, we lose out on the potential of informed practice.

DDDM_not_NCLB

Thanks for the guest spot, eduwonkette!