What is ELDA?
Schools and districts are awash in data, from more traditional data sources such as test scores, grades, attendance, discipline reports and surveys, to more recent innovations such as intelligent tutoring computer systems, computer interaction logfile data, and multimodal instrumented instructional spaces. Education Leadership Data Analytics (ELDA) helps school leaders make sense of the data that are collected on students and schools on a daily basis through applying current big data, data mining, and data science analytic techniques to educational issues that are important to teachers, principals, superintendents, parents, and most importantly, students.
Identifying “Outperforming” School Districts in Ohio and Texas Over the Long-Term – A Complement to Current State Accountability Systems
Since the authorization of No Child Left Behind policy in 2002, an increasing number of states began to provide designation letters such as A, B, C, D to evaluate school and school districts based on students’ performance on standardized test scores. Such designation letters not only provide information for multiple stakeholders on how school districts perform within a state for a given year but also allow researchers and practitioners to identify “outperforming” school districts for further in-depth qualitative studies to generalize successful leadership practice. However, this method has been critiqued historically for not considering school district contextual or demographic effects on students’ achievement test scores. Evaluating school district effectiveness based on single-year performance has been criticized as “snapshot” research without considering a school district’s capacity for achievement growth over time. In the recently published chapter in the book Leading Holistically: How Schools, Districts, and States Improve Systemically, Alex J. Bowers, Xinyu Ni and Jennifer Esswein addressed the above critiques through applying hierarchical linear growth modelling to all school districts in the states of Ohio and Texas to identify districts that significantly outperform or underperform their contexts, demographics, and resources based on their seven-year longitudinal performance.