Over the past 20 years, public schools in the U.S. have come to rely more and more on standardized tests, and the COVID-19 pandemic has halted the flow of these data. This is hardly among the most important disruptions that teachers, parents, and students have endured over the past year or so. But one of the corollaries of skipping a year (or more) of testing is its implications for estimating growth models, which are statistical approaches for assessing the association between students' testing progress and those students' teachers, schools, or districts.
This type of information, used properly, is always potentially useful, but it may be particularly timely right now, as we seek to understand how the COVID-19 pandemic affected educational outcomes, and, perhaps, how those outcomes varied by different peri-pandemic approaches to schooling. This includes the extent to which there were meaningful differences by student subgroup (e.g., low-income students who may have had more issues with virtual schooling).
To be clear, the question of when states should resume testing should be evaluated based on what’s best for schools and students, and in my view this decision should not include consideration of any impact on accountability systems (the latest development is that states will not be allowed to cancel testing entirely but may be allowed to curtail it). In either case, though, the fate of growth models over the next couple of years is highly uncertain. The models rely on tracking student test scores over time, and so skipping a year (and maybe even more) is obviously a potential problem. A new working paper takes a first step toward assessing the short-term feasibility of growth estimates (specifically school and district scores). But this analysis also provides a good context for a deeper discussion of how we use (and sometimes misuse) testing data in education policy.