[Please Note: This Ingram Olkin Forum session has already occurred. Go to the News Story for this event to read about what happened.]
In this Ingram Olkin “Statistics Serving Society” Forum, experts from around the country will share statistical and data-analytic challenges they have faced, as they have reported on and researched issues around the impact of COVID-19 in U.S. schools.
Brief panel presentations will begin with Stephen Sawchuck of Education Week sharing what he has learned about whether and how school districts have used data to aid decision making about COVID-19. Researchers from several major research and development institutions will then share what they have learned about the impact of COVID-19 on schools, and will describe statistical models they have proposed and implemented for school closings and re-openings, and for COVID’s impact on learning.
They have proposed and implemented statistical models for school closings and re-openings, as well as for COVID’s impact on learning. Each will share the challenges they faced. The forum will include time for audience questions and a good discussion of the thorny issues that have arisen during these efforts, with a goal of identifying potential strategies for these solving these challenges.
Most participants are listed below, and we are hopeful that participants from the Centers for Disease Control and the Institute for Disease Modeling (IDM) will also be involved.
Tentative Schedule for this Event
Session Organizer/Moderator: Betsy Becker (Florida State University)
Stephen Sawchuk (Education Week) ~ 8 minutes
Brian Gill & Ravi Goyal (Mathematica) ~ 10-12 minutes
Cliff Kerr & Dina Mistry (IDM) ~ 10-12 minutes
Megan Kuhfeld (Northwest Evaluation Association) ~ 8 minutes
Julia Kaufman & Claude Messan Setodji (RAND) ~ 10-12 minutes
Question & Answer / Discussion
Session Organizer/Moderator: Betsy Becker (Florida State University)
About the Speakers
Betsy Becker (Ph.D., University of Chicago) is Mode L. Stone Distinguished Professor of Educational Statistics and Distinguished Research Professor at Florida State University. She has developed methods for synthesizing correlation matrices and regression models, and is a founding member of the Society for Research Synthesis Methodology. She is currently involved in syntheses of studies of lung-cancer survival, automated essay scoring, and game-based learning. With expertise in psychometrics, she co-chairs the Design and Analysis Committee for the National Assessment of Educational Progress, and serves on technical advisory committees for assessments in Florida and Texas. She earned both B.A. and M.A. degrees from The Johns Hopkins University.
Brian Gill (Ph.D., J.D., University of California, Berkeley) is a senior fellow at Mathematica, where he studies K–12 education policy, including charter schools, educator effectiveness, and the implementation and impacts of school accountability regimes. He frequently works with state and local educational leaders. He directs the U.S. Department of Education’s Mid-Atlantic Regional Educational Laboratory, assisting educators and policymakers with high-priority projects, including the development and refinement of measures of school performance. Working with the Pennsylvania Department of Education, he led a study that used an agent-based model to predict the spread of Covid-19 in schools under widely varying conditions.
Ravi Goyal (Ph.D., Harvard University) is a senior statistician at Mathematica, where he applies data science and statistical techniques to improve public well-being. His research interests lie in developing statistical methodologies (in particular, those based on network science) to investigate questions—at the population and molecular level—relevant to infectious diseases. His recent work includes leveraging agent-based models (ABMs) to provide guidance on COVID-19 mitigation strategies for K-12 schools and universities. In addition, he has used ABMs to evaluate the long-term cost-effectiveness of the Ryan White HIV/AIDS program and assist in the design of the Botswana Combination Prevention Program.
Julia Kaufman (Ph.D., New York University) is a senior policy researcher and co-director of the American Educator Panels at RAND. Her research focuses on the policies and programs states and schools could undertake to ensure high-quality teaching and learning. She is currently leading several projects focused on understanding curriculum and instruction across the U.S., including the American Instructional Resources Survey study and an investigation of teaching and learning during COVID-19. Her research has been published in numerous academic journals and RAND reports. She holds a Ph.D. in international education from New York University and an M.A. in teaching from the University of Pittsburgh.
Cliff Kerr (Ph.D., University of Sydney) is a Senior Research Scientist at the Institute for Disease Modeling (IDM). His work includes optimization algorithm development, modeling of family planning interventions, and value-of-information analyses. His postdoctoral work examined how simulations of a monkey brain could be used to operate robotic arms. He extended this work to explore the neural basis of computation. He co-founded and led the Optima Consortium for Decision Science, a nonprofit that has helped over 50 countries plan health investments. He has a B.S. in neuroscience from the University of Queensland, a Ph.D. in theoretical physics, and a Diploma of Arts from the Sydney Conservatorium of Music.
Megan Kuhfeld (Ph.D., UCLA) is a Senior Research Scientist at NWEA. Her research seeks to understand students’ trajectories of academic and social-emotional learning and the school and neighborhood influences that promote optimal growth. She received a Ph.D. in quantitative methods in education and a master’s degree in statistics from the University of California, Los Angeles (UCLA). Her research has been published in Educational Researcher, Psychological Methods, Multivariate Behavioral Research, and Journal of Research on Educational Effectiveness. She previously worked at the non-profit policy institute Child Trends, the Population Research Center at the University of Texas at Austin, and CRESST.
Dina Mistry (Ph.D., Northeastern University) is a Post-doctoral Research Scientist in the Epidemiology team at the Institute for Disease Modeling (IDM). Her doctoral research aimed to characterize and model social-contact networks and mobility networks involved in the spread of airborne infectious diseases. Her research at IDM focuses on modeling human response to disease awareness and its impact on disease spreading using interdisciplinary approaches from Physics, Network Science, and Cognitive Psychology. She also holds a M.SC in Physics from Northeastern University and an Hon. B.Sc. in Astronomy, Physics, and Mathematics from the University of Toronto.
Stephen Sawchuk is associate editor at Education Week, an independent news organization that specializes in coverage of issues in K-12 education. He covers superintendents, district management, school safety, and civics education for Education Week, and has authored numerous recent articles on COVID-19 in the schools. He joined the newspaper in 2008 and formerly covered the teaching profession, curriculum, and instruction. He holds degrees from Georgetown and Columbia universities and was a 2017 Knight-Wallace Fellow at the University of Michigan.
Claude Messan Setodji (PhD, University of Minnesota) is a RAND senior statistician and the co-director of the RAND Center for Causal Inference who has interest in applications of statistics to public policy, causal inferences, sampling techniques, data reduction and visualization. He has extensive experience in education policy analysis, QRIS assessments, ECE quality, and child development. As a statistician with interest in the application of complex statistical methods to policy questions, he is the senior statistician on the American Educator Panel( AEP) project and in charge of sampling, weighting and some analyses of the data. Dr. Setodji has done extensive work in the analysis of complex longitudinal data, especially on the value-added methodology.