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

M. Lenard, M. Morrill, and J. Westall. 2020. “High School Start Times and Student Achievement: Looking Beyond Test Scores.” Economics of Education Review, 76. Publisher's VersionAbstract
The American Academy of Pediatrics recommends high schools begin after 8:30 AM to better align with the circadian rhythms of adolescents. Yet due to economic and logistic considerations such as transportation, athletics, and students’ after-school employment, the vast majority of high schools begin the school day considerably earlier. We leverage a quasi-natural experiment whereby five comprehensive high schools in a large and diverse school district moved start times forty minutes earlier to better coordinate with high schools already starting at 7:25 AM. In this setting, disruption effects from moving start times should exacerbate any harmful consequences of earlier start times. Early start times might negatively impact test scores, student engagement, and non-cognitive skill formation. We report on the effect of earlier start times on a broad range of outcomes, including mandatory ACT test scores, absenteeism, on-time progress in high school, and college-going. While we fail to find evidence of harmful effects on test scores, we do see a rise in absenteeism and tardiness rates, as well as higher rates of dropping out of high school. These results suggest that the harmful effects of early start times may not be well captured by considering test scores alone.
Lindsay C. Page, Matthew A. Lenard, and Luke Keele. Working Paper. “The Design of Clustered Observational Studies in Education”. Publisher's VersionAbstract
Clustered observational studies (COSs) are a critical analytic tool for educational effectiveness research. We present a design framework for the development and critique of COSs. The framework is built on the counterfactual model for causal inference and promotes the concept of designing COSs that emulate the targeted randomized trial that would have been conducted were it feasible. We emphasize the key role of understanding the assignment mechanism to study design. We review methods for statistical adjustment and highlight a recently developed form of matching designed specifically for COSs. We review how regression models can be profitably combined with matching and note best practice for estimates of statistical uncertainty. Finally, we review how sensitivity analyses can determine whether conclusions are sensitive to bias from potential unobserved confounders. We demonstrate concepts with an evaluation of a summer school reading intervention in Wake County, North Carolina.
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.