Addressing Math Learning Loss in Low-Income Middle Schools: A Qualitative Study of Data-Informed Leadership

Abstract

Following the COVID-19 pandemic, middle school students of low-income backgrounds exhibited particularly significant learning losses in mathematics. Empirical research indicates that data-informed leadership practices can have an important role in accelerating mathematics achievement among low-achieving students, but there is limited research exploring how instructional leaders seek to use data-informed approaches to counteract post-pandemic learning loss. This study investigates current data-informed leadership practices by performing semi-structured interviews with (n=25-30) instructional leaders in low-income middle schools. To interpret and communicate the findings, Datnow and Park’s 4 P’s framework - People, Policies, Practices, and Patterns - is used to assess data-informed instructional leadership practices. Findings from this study indicate that math instructional leaders view data-informed practices as central to improving student outcomes, with effectiveness shaped by teacher capacity, collaborative trust, and adaptive responses to post-pandemic challenges such as staffing instability, student engagement, and widening learning gaps.