Value-added models have become increasingly popular in today’s policy environment as a way to evaluate, reward, and dismiss teachers. These statistical models aim to isolate each teacher’s unique contribution to their students’ educational outcomes based in part on student test scores. But NYU professor Sean Corcoran uses data analysis to argue that value-added models are not precise enough to be useful for high-stakes decision making or professional development. Corcoran cautions policy-makers, in particular, to be fully aware of the limitations and shortcomings of these models and consider whether their minimal benefits outweigh the cost.
Prepared by Sean Corcoran, assistant professor of educational economics at New York University’s Steinhardt School of Culture, Education and Human Development, and research fellow at the Institute for Education and Social Policy in collaboration with the Annenberg Institute.