SoDa Symposium: Educational Data Science in the Measurement, Statistics and Evaluation (MSE) Program
Event Start Date: Tuesday, April 12, 2022 - 12:00 pm
Event End Date: Tuesday, April 12, 2022 - 1:00 pm
Location: Virtual on Zoom
Current data science projects in education research will be presented:
1. Dr. Stapleton will discuss a current AERA Research Grant aimed at measuring school quality using propensity score methods for the Baltimore City Public School System.
2. Dr. Sweet will present work on applying machine learning methods to predict high stakes test scores for student in Maryland public schools during the pandemic.
3. Dr. Liao, an MSE alumna of the program, will discuss her work as a psychometrician at Duolingo.
MSE would like to acknowledge the graduate students working on the projects being discussed; Yi Feng, Ph.D. student, Ashani Jayasekera, M.S. student, Brennan Register, Ph.D. student, and Patrick Sheehan, Ph.D. student.
Laura M. Stapleton
Interim Dean and Professor
College of Education, University of Maryland
Bio: Laura M. Stapleton is Interim Dean for the College of Education. Prior to the 2021-22 academic year, she served as Associate Dean for Research, Innovation, and Partnerships. She is also a Professor in Measurement, Statistics and Evaluation (EDMS) in the Department of Human Development and Quantitative Methodology at the University of Maryland and is the Director of the NSF-funded Quantitative Research Methods Scholars Program, which trains 20 early career scholars who focus on STEM education equity and access. She currently serves as Associate Editor of AERA Open and each year teaches as part of the faculty of the National Center for Education Research funded Summer Research Training Institute on Cluster Randomized Trials at Northwestern University. She joined the faculty of the college in Fall 2011 after being on the faculty in Psychology at the University of Maryland, Baltimore County and in Educational Psychology at the University of Texas, Austin. She served as the Associate Director of the Research Branch of the Maryland State Longitudinal Data System Center from 2013-2018. Prior to earning her Ph.D. in Measurement, Statistics and Evaluation, she was an economist at the Bureau of Labor Statistics and, subsequently, conducted educational research at the American Association of State Colleges and Universities and as Associate Director of institutional research at the University of Maryland.
Associate Professor, Measurement, Statistics and Evaluation Program.
College of Education, University of Maryland
Bio: Tracy Sweet is an Associate Professor in the Measurement, Statistics and Evaluation program in the Department of Human Development and Quantitative Methodology. Her research focuses on methods for social network analysis with particular focus on multilevel social network models. Recent projects include network interference and missing data. She serves as the Associate Director of Research for UMCP for the Maryland Longitudinal Data System Center and currently overseeing projects on applying data science and statistical methods on large-scale educational data. Finally, Dr. Sweet is committed to improving diversity in the fields of statistics and quantitative methodology. She serves on the DEI committee for her department and the College’s Council on Racial Equity and Justice, and is interested in exploring how race and ethnicity is analyzed in quantitative methods. She completed her Ph.D. in Statistics at Carnegie Mellon University and M.A. in Mathematics at Morgan State University.
Bio: Manqian (Mancy) Liao is a psychometrician at Duolingo. Currently, her work focuses on conducting validity research on the Duolingo English Test, including developing and improving the quality assurance process of the test and conducting differential item functioning studies. She got her Ph.D. degree in Educational Measurement, Statistics and Evaluation from the University of Maryland, College Park in 2020. Her research interests include item response theory and cognitive diagnostic modeling. She is passionate about utilizing her expertise to find innovative solutions to improve validity and fairness of the test scores under the applied settings.
About the Measurement, Statistics and Evaluation Program
Most of the educational and social science research that takes place today relies on the expertise of those who develop data collection instruments such as assessments, questionnaires, and interview protocols, plan research and evaluation studies, design sampling frameworks, collect and analyze data, and develop new statistical models and methods.
The program in Measurement, Statistics and Evaluation trains future professionals in these areas, and we offer both the Master of Science (M.S.) and Doctor of Philosophy (Ph.D.) degrees. Our programs are widely recognized as being among the best programs in the country with exceptional students trained by faculty who are respected leaders within their specialties.
The M.S. program gives individuals the broad range of skills necessary to serve as research associates in academic, government, education and business settings. M.S. students generally take introductory coursework in measurement, applied statistics, and evaluation. The Ph.D. program qualifies individuals to provide leadership in the conduct of research studies, to serve as statisticians, measurement, or evaluation specialists in school systems, industry, and government, and to conduct methodological research both in and outside of academia. While we do not have an undergraduate program, we offer a fifth year B.S./M.S. program.
The SoDa Center at UMD
The powerful information available in large social science data sets is critical to understanding and addressing many of our nation and world’s most pressing challenges: from Covid-19 to racial, social and economic injustice; and from climate change to deep and damaging political and cultural divides. To help address these challenges, the University of Maryland has launched a new Social Data Science Center (SoDa) designed to advance research, education, and applications of social data measurement and analysis.