Schools that employ data-driven instructional techniques and policies also tend to employ data meetings, in which teachers and other stakeholders exchange ideas, form agendas, and otherwise, apply data-derived insights that result in pedagogical action. The problem investigated in this study was that the local district has not yet measured the effectiveness of these data meetings. The purpose of this correlational study was to measure the relationship between teachers\u27 concerns about data meetings and students\u27 performance in math as measured by the change scores on the Standardized Test for the Assessment of Reading (STAR) test in a North Alabama school. The theoretical framework was Astin\u27s student engagement theory, which hypothesizes that students\u27 academic improvements are caused by a link between higher levels of teacher concern related to key aspects of pedagogy and student engagement. A linear regression was conducted to measure the relationship between the concerns of 53 teachers regarding data meetings and the change scores of their students on the STAR math test from one year to the next. The results indicated a significant (p \u3c .05) positive correlation between teachers\u27 concerns about data meetings and STAR math test scores, with variation in readiness associated with 68% of variation in math scores. Therefore, more attention should be paid to increasing teachers\u27 concerns toward data meetings. Doing so can produce positive change for students who, because of improved math outcomes, will do better in school and in the employment market
