Creating Actionable Human Capital Analytics for Studying School-Level Teacher Retention

One potentially fruitful application for human capital analytics is to support policies and practices that might reduce undesirable teacher turnover. Teacher turnover can be harmful to student achievement and faculty cohesiveness and can exacerbate teacher shortages. This article describes an attempt to build a human capital analytics tool to help a school district better identify schools with problems retaining teachers as well as schools that are doing an especially good job of retaining them. It describes the theory of action for the school-level retention analytics tool, the models used to estimate persistent school effects and predict which schools would continue to have retention problems, and the features of a web-based tool developed to help district staff gain insights about the degree of variation among schools and likely contributing factors. We found that there were reliable differences in schools’ persistent rates of teacher retention, the differences persisted after controlling for student demographics, teachers at different levels of effectiveness and experience have different predicted levels of retention, and there were substantial differences among schools in their ability to retain teachers at different levels of experience and effectiveness. Finally, we describe the initial reactions of the users for whom we designed the tool and what we learned about developing a retention analytic tool for use by school district administrators.