I am a Ph.D. student in Education Policy and Program Evaluation at Harvard University. My primary research interests involve identifying and evaluating interventions that improve student achievement and teacher quality—all with an eye toward eliminating disparities. I am particularly interested in interventions that optimize how students and teachers are assigned to schools and classrooms. I also hope to learn from and explore international education contexts in order to identify programs and policies that can inform the U.S. education system. As both a researcher and a practitioner who has benefited from evaluation resources, I hope to develop programs and toolkits that enhance the work of educators in the field.

Most recently, I led data strategy efforts at the Wake County Public School System, having joined as a Strategic Data Project Fellow in 2012. While there, I helped codify an enhanced data- and evidence-use policy, led a diverse series of randomized controlled trials, and developed the district’s research-practice partnership framework. Prior to that, I was a policy analyst at the Southern Regional Education Board, co-founded the education technology company BetterLesson, and taught middle school social studies in the Atlanta Public Schools as a Teach for America corps member. I studied economics and Russian at Wesleyan University and political science at Georgia State University.

Featured Publications

S.W. Hemelt and M.A. Lenard. 2/2020. “Math Acceleration in Elementary School: Access and Effects on Student Outcomes.” Economics of Education Review, 74. Publisher's VersionAbstract
This paper examines curricular acceleration in mathematics during elementary school using administrative data from a large, diverse school district that recently implemented a targeted, test-based acceleration policy. We first characterize access to advanced math and then estimate effects of acceleration in math on measures of short-run academic achievement as well as non- test-score measures of grit, engagement with schoolwork, future plans, and continued participation in the accelerated track. Experiences and effects of math acceleration differ markedly for girls and boys. Girls are less likely to be nominated for math acceleration and perform worse on the qualifying test, relative to boys with equivalent baseline performance. We find negative effects of acceleration on short-run retention of math knowledge for girls, but no such performance decay for boys. After initial exposure to accelerated math, girls are less likely than boys to appear in the accelerated track during late elementary school and at the start of middle school.
S.D. Pimentel, L. Page, M. Lenard, and L. Keele. 2018. “Optimal Multilevel Matching Using Network Flows: An Application to a Summer Reading Intervention.” Annals of Applied Statistics, 12, 3, Pp. 1479-1505. Publisher's VersionAbstract
Many observational studies of causal effects occur in settings with clustered treatment assignment. In studies of this type, treatment is applied to entire clusters of units. For example, an educational in- tervention might be administered to all the students in a school. We develop a matching algorithm for multilevel data based on a network flow algorithm. Earlier work on multilevel matching relied on integer programming, which allows for balance targeting on specific covari- ates but can be slow with larger data sets. Although we cannot di- rectly specify minimal levels of balance for individual covariates, our algorithm is fast and scales easily to larger data sets. We apply this algorithm to assess a school-based intervention through which stu- dents in treated schools were exposed to a new reading program dur- ing summer school. In one variant of the algorithm, where we match both schools and students, we change the causal estimand through optimal subset matching to better maintain common support. In a second variant, we relax the common support assumption to preserve the causal estimand by only matching on schools. We find that the summer intervention does not appear to increase reading test scores. In a sensitivity analysis, however, we determine that an unobserved confounder could easily mask a larger treatment effect.
J. Holbein, D.S. Hillygus, M. Lenard, C. Gibson-Davis, and D. Hill. 2018. “The Development of Students' Engagement in School, Community, and Democracy.” British Journal of Political Science, Pp. 19. Publisher's VersionAbstract
This article explores the origins of youth engagement in school, community and democracy. Specifically, it considers the role of psychosocial or non-cognitive abilities, like grit or perseverance. Using a novel original large-scale longitudinal survey of students linked to school administrative records and a variety of modeling techniques – including sibling, twin and individual fixed effects – the study finds that psychosocial abilities are a strong predictor of youth civic engagement. Gritty students miss less class time and are more engaged in their schools, are more politically efficacious, are more likely to intend to vote when they become eligible, and volunteer more. Our work highlights the value of psychosocial attributes in the political socialization of young people.