Using Predictive Analytics to Allocate Educational Resources: An Application to Career and Technical Education

Education leaders increasingly seek to integrate data into decision-making processes as they confront difficult choices about how to invest public resources. This paper assesses the consequences of data-driven decisions related to systems-level investments in career and technical education (CTE). Using Massachusetts administrative records, we evaluate various decision rules—from simple population-based approaches to sophisticated predictive models—for forecasting CTE concentration. We find inves