EDD8050: Data Literacy for Leaders

Disaggregation receives particular emphasis because it addresses what is arguably the single most consequential pattern in how educational data are routinely misused in practice: the use of aggregate measures that present an overall picture of organizational performance while systematically hiding disparities across subgroups that represent the most important equity and improvement challenges the organization faces. This is not a hypothetical concern — it is a thoroughly documented pattern in education leadership that has been reinforced by accountability systems and public reporting practices. A school district that reports an 80% overall proficiency rate on a state assessment, for example, may be experiencing substantially different outcomes across racial, socioeconomic, English learner, and special education subgroups — with some subgroups well above 80% and others significantly below — but if leadership relies primarily on the aggregate measure, the disparities are invisible at the level where decisions are being made, resources are being allocated, and improvement strategies are being designed. The federal Every Student Succeeds Act (ESSA) of 2015, like No Child Left Behind before it, specifically requires states to report assessment results disaggregated by major student subgroups precisely because of the well-documented history of aggregate reporting masking achievement gaps, particularly for historically underserved student populations. EDD8050 emphasizes disaggregation not merely as a technical data procedure but as an equity practice: the decision to look at or not look at subgroup data is a leadership decision with real consequences for which students' needs are recognized and which remain invisible. The course also develops the interpretive discipline that competent disaggregation requires — teaching leaders to recognize that small subgroup sizes can produce unreliable results (a subgroup of 8 students can show dramatic year-to-year fluctuations driven by individual students rather than any systemic pattern), that disaggregation is a starting point for investigation rather than an endpoint (identifying a gap raises the question of what's causing it, which requires further inquiry), and that data disaggregated along one dimension may need to be further disaggregated along intersecting dimensions to reveal the full picture (gender gaps may look very different within different racial/ethnic subgroups, for instance). The practical upshot is that educational leaders who cannot or do not routinely disaggregate their organizational data are operating with a systematically incomplete picture of how their organization is actually performing — and are likely making planning and resource allocation decisions that inadvertently perpetuate exactly the disparities that data-informed leadership is supposed to address.