Expertise on the Team
Guanglei Hong is Professor in the Comparative Human Development Department and the Committee on Education at the University of Chicago. She is the Inaugural Chair of the University-wide Committee on Quantitative Methods in Social, Behavioral, and Health Sciences. Dr. Hong develops and applies causal inference theories and methods for evaluating educational and social policies and programs in multi-level, longitudinal settings. Her work is currently focused on developing concepts and methods for analyzing causal mediation mechanisms in multisite randomized trials (Qin & Hong, 2017; Qin et al, 2019). She has published a number of studies on math instruction in which she creatively applied new methods for evaluating multivalued treatments (Garrett & Hong, 2017) and time-varying treatments (Hong & Raudenbush, 2008) and for conducting causal mediation analysis (Hong & Nomi, 2012). Her research monograph “Causality in a Social World: Moderation, Mediation, and Spill-over” (2015) clarifies for applied researchers the theoretical concepts of moderated effects, mediated effects, and spill-over effects. It systematically introduces innovative statistical strategies for investigating these causal effects and aims to make them readily accessible to a broad audience. Her other publications have appeared in top-tier statistics, education, and psychology journals. She was Guest Editor for the Journal of Research on Educational Effectiveness special issue on the statistical approaches to studying mediator effects in education research published in 2012. She and her colleagues have developed several software programs for causal inference and for causal mediation analysis. She has received research funding from the National Science Foundation, the Institute of Education Sciences of the U.S. Department of Education, the William T. Grant Foundation, the Spencer Foundation, and the Social Sciences and Humanities Research Council of Canada. She has served multiple times as the methodological expert on the IES Mathematics and Science review panel.
Stephen W. Raudenbush is Lewis-Sebring Distinguished Service Professor in the Department of Sociology at the University of Chicago, Chair of the Committee on Education and a member of the National Academy of Sciences. He is interested in statistical models for child and youth development within social settings such as classrooms, schools, and neighborhoods. He is best known for his work developing hierarchical linear models, with broad applications in the design and analysis of longitudinal and multilevel research. He is currently studying the development of math skills in early childhood, elementary school, and high school. The focus of this work is on tailoring instruction to individual differences in skills with the aim of reducing social inequality in math achievement. Raudenbush and colleagues have developed a new preschool intervention known as “Longitudinally Adaptive Instruction and Assessment in Pre-School Mathematics” (Carrazza, Dulaney, Levine, Sorokin, and Raudenbush, 2019). A randomized trial of this intervention produced a large and significant positive effect on children’s numerical learning. Raudenbush and colleagues have also recently published a book entitled The Ambitious Elementary School” (McGhee-Hassrick, Raudenbush, and Rosen, 2017) that applies a similar approach at the elementary school. A randomized lottery study showed large effects and lasting of this approach on math learning. At the high school level, Nomi and Raudenbush (2016) evaluated the impact of “Double Dose Algebra” in Chicago’s public high schools using a regression discontinuity design. They have followed those students into and through college. Remarkably, receiving Double-Dose algebra in 9th grade in 2003 significantly and substantially increased high-school and college graduation rates. All of these studies used hierarchical linear models (developed by Raudenbush and Bryk, 2002). Together, they give evidence that tailoring instruction and expanding instructional time has substantial potential to improve math learning and reduce inequality, and these efforts pay at each epoch of the child’s school career.
Yanyan Sheng is Senior Lecturer in the Committee on Quantitative Methods in Social, Behavioral, and Health Sciences at the University of Chicago. She has over fourteen years of experience in teaching graduate-level statistics and measurement courses in education, and researching in psychometrics and especially Bayesian item response theory modeling. Her work has focused on developing fully Bayesian IRT unidimensional and multidimensional models (e.g., Kuo & Sheng, 2015; Sheng, 2015), empirically investigating test theory and model (e.g., Ptukhin & Sheng, 2019; Sheng, 2017), and applying high-performance computing with Bayesian IRT models (e.g., Sheng, Welling, & Zhu, 2014; 2015). In addition to theoretical work, she has also published research on serious game analytics and on psychometric properties of existing instruments. Dr. Sheng is Associate Editor for the International Journal of Quantitative Research in Education and Frontiers in Quantitative Psychology and Measurement. She has led a psychometric team to work on evaluating and developing the Illinois Science Assessment for the Illinois State Board of Education since 2017.
Kenneth Frank is MSU Foundation Professor of Sociometrics. He has published widely on teacher and adolescent networks (e.g., Frank et al., 2011; Frank et al., 2008; Frank Muller and Mueller, 2013; Frank, Xu & Penuel, 2018; Frank, Lo, Torphy and Kim, 2018, Frank et al, accepted), and how schools function as social organizations. These include empirical studies of how schools distribute relevant expertise (Frank, 2009; Frank, Penuel, and Krause, 2015; Frank, Penuel et al., 2013) and agent based models of the network processes behind organizational culture (Frank and Fahrbach, 1999; Frank, Xu & Penuel, 2018). He has focused particularly on the diffusion and implementation of innovations in organizations (Frank, Zhao and Borman, 2004; Frank and Zhao 2005; Zhao and Frank, 2003). He has also published widely in methodology, including on social network analysis (Frank, 1995, 1996, Frank, Field et al., 2006), causal inference/sensitivity analysis (Frank, 2000; Frank and Min 2007; Frank et al., 2013) and multi-level models (Frank 1998; Miyazaki and Frank, 2006). For more info click here.
Dr. Jiliang Tang joined Michigan State University as an Assistant Professor in 2016. Dr.Tang has a strong background in data mining and machine learning including model robustness, multiple source learning, feature learning, and network embedding. He was the recipient of the Best Paper award in ASONAM2018, the Best Student Paper in WSDM2018, the Best Paper Award in KDD2016, the runner up of the Best KDD Dissertation Award in 2015, Dean’s Dissertation Award, the 2014 ASU President Award for Innovation and the best paper shortlist of WSDM2013. Dr. Tang led a team building the well-received feature learning project, which is recognized as “5 Machine Learning Projects You Can No Longer Overlook”. Dr. Tang has filed more than 10 patents and published more than 100 papers in highly ranked journals and top conference proceedings, which received thousands of citations with h-index 38 and extensive media coverage.
Kaitlin Torphy, Ph.D. is the Lead Researcher and Founder of the Teachers in Social Media Project at Michigan State University. This project considers the intersection of cloud to class, nature of resources within virtual resource pools, and implications for equity as educational spaces grow increasingly connected (including 6 recent or forthcoming papers concerning the use of social media in education, especially by teachers — see recent and forthcoming special issues of the American Journal of Education and Teachers’ College Record). Dr. Torphy conceptualizes the emergence of a teacherpreneurial guild in which teachers turn to one another for instructional content and resources. She has expertise in teachers’ engagement across virtual platforms, teachers’ physical and virtual social networks, and education policy reform. Dr. Torphy was a co-PI and presenter for an American Education Research Association conference convened in October 2018 at Michigan State University on social media and education. She has published work on charter school impacts, curricular reform, teachers’ social networks, and presented work regarding teachers’ engagement within social media at the national and international level. Her other work examines diffusion of sustainable practices across social networks within The Nature Conservancy. Dr. Torphy earned a Ph.D. in education policy, a specialization in the economics of education from Michigan State University in 2014, and is a Teach for America alumni and former Chicago Public Schools teacher.
Darnell Leatherwood is currently completing a Ph.D. at the University of Chicago School of Social Service Administration. A quantitatively trained interdisciplinary social scientist, his research interests include education, social inequality/policy, and identity formation. Darnell is a recent recipient of the Certificate in Education Sciences from the Department of Education Institute of Education Sciences Predoctoral Fellowship Program through the University of Chicago Committee on Education, was awarded the 2020 Allison Davis Research Award through the University of Chicago Division of the Social Sciences, and was named to the Chicago Scholars 35 under 35 list for 2020. He is also an Illinois Board of Higher Education Fellow. Darnell currently serves as a Young Scholar on The Journal of Negro Education Editorial/Advisory Board out of Howard University in Washington DC, on the Advisory Board of the Chicago State University College of Education, and was coordinator/chair of the Workshop on Education at the University of Chicago from 2016-2018. He holds a M.A. in the Social Sciences from the University of Chicago and a B.S. from the College of Business at the University of Illinois at Urbana-Champaign. To learn more about Darnell and his work follow him on IG @therealdarnellleatherwood and LinkedIn.