ITP Fellows will have the opportunity to participate in a wide range of rigorous research underway by participating faculty and that is constantly evolving to address new areas of interest and need. Examples of current research opportunities available to students are provided below. (A brief description of each faculty’s research interests is provided here)
ITP Director Geoffrey Borman (educational leadership) leads a project that focuses on the impacts of brief social-psychological interventions designed to self-affirm students and enhance their sense of belonging in school in a districtwide sample of sixth- and seventh-graders. To date, the study involves 11 schools, more than 100 teachers, and nearly 3,000 students. Borman also provides students with access to several other RCTs. Currently, he leads analyses of IES-supported national Goal 4 RCTs on the effects of the new Open Court Imagine It! series, and the Everyday Mathematics core curriculum program. In partnership with the American Institutes for Research, Borman is co-principal investigator (PI) on a national study of the impacts on teachers and students of a content-focused math professional development program. Borman also directs analyses of national longitudinal data using growth and multilevel modeling to identify effects of summer school and school and neighborhood contexts. Graduate students are deeply involved in many of these studies.
Sara Goldrick-Rab (educational policy studies and sociology, founding director of Wisconsin HOPE Lab) co-directs an RCT to assess the impact of the Wisconsin Scholars Grant, on which Christopher Taber (economics) and Aaron Brower (social work) have served as co-PIs. This large-scale study of private financial aid, which has involved a team of ITP fellows, takes advantage of the lottery used to determine aid recipients to permit causal analysis of program effects. Goldrick-Rab regularly applies quasi-experimental methods, including an instrumental variables approach, to estimate the impacts of undergraduate debt. Taber leads two studies using econometric methods for causal inference. Brower is involved with projects that develop more sophisticated and rigorous uses of data to, for instance, develop learning analytics algorithms to identify students at risk for dropping out of college.
Judith Harackiewicz (psychology) is PI on a large-scale RCT involving 2,000 students in introductory college biology classes. She is testing an intervention to promote academic motivation and performance, particularly for first-generation and underrepresented minority students. She also continues her work with ITP graduate Chris Hulleman on utility value manipulations in high school and college classes. These studies of motivation have important policy implications, particularly in the current context of standards-based reform. Harackiewicz is also engaged in developing new interventions through laboratory studies. Her work includes research on conflict among peers in educational contexts. Her work and her students’ work often contrasts laboratory and field-based findings in interesting and important ways (e.g., Hulleman & Cordray, 2009). Borman and Harackiewicz are both fielding RCTs of social-psychological interventions of a similar design, and their teams regularly interact around issues of implementation and analysis.
Martha Alibali (psychology) studies the effects of varying instructional approaches to mathematics. She oversees studies of the impacts on middle school students’ mathematical thinking when materials and instruction link different representations of mathematical information. Again, the practical implications of her work touch directly on classroom practice. Like Harackiewicz, Alibali develops new interventions, and she and her students navigate the laboratory and real-world of classrooms with their work. These clear connections to practice provide ITP students with models of how research can directly impact practice in well-conceived, evidence-based ways. Another opportunity comes from a project under the direction of Goldrick-Rab, whereby a team of What Works Clearinghouse-certified UW–Madison graduate students evaluates data from experimental and quasi-experimental studies to determine their suitability for What Works Clearinghouse reporting.
Further, Peter Steiner, Jee-Seon Kim, and David Kaplan (educational psychology) apply educational statistics in novel and interesting ways that implicitly advance theory and evidence related to causality. Their projects to improve nonexperimental methods and advance scientific thought on cause and effect represent important resources for our students who, in the absence of experimental evidence, often must respond to policy questions with observational data.
Many ITP faculty members have published extensively on policy levers that potentially boost achievement as revealed by advanced statistical modeling of longitudinal data (IES Goal 1). Examples include Borman on summer learning, Taber on Catholic school effects, Goldrick-Rab on postsecondary access, Brower on programs that aid the transition to college, and Harackiewicz on boosting student motivation. Another core faculty member who examines longitudinal data is Katherine Magnuson (social work). Her work looks at whether children benefit when their mothers receive additional education and whether children’s skills at school entry have long-term effects on their school performance. Magnuson is also conducting a meta-analysis of early intervention programs for young children. Felix Elwert (sociology) provides innovative work on graphical causal models and other work around causal inference from observational data. His current work analyzes 30 years of longitudinal data from the Panel Study of Income Dynamics using new statistical models capable of dealing with time-varying effect modification. Eric Grodsky (sociology) has contributed to our capacity to train students in advanced statistical methods for analyzing longitudinal data. His current projects include regression-discontinuity and interrupted time series analyses of the effects of early information on need for postsecondary remediation.
Steven Durlauf (economics) leads two projects that aim to develop econometric tools in the study of social interactions. One project focuses on uncovering peer group influences in the presence of unobserved group effects. A second project explores what can be learned from socioeconomic data about the role of genes and environment in various types of outcomes. Each of these methodological advances will provide new tools for quantitative researchers in education policy, and our students will have opportunities to learn about and apply the new methods. Daniel Bolt (educational psychology) is developing new multilevel item response theory methodologies for estimating group-level profiles. The technique was applied to international data from the Trends in Mathematics and Science Study. Kim also has developed new applications within the multilevel modeling framework. She has tested the impact of omitted school variables and obtaining robust estimators for hierarchical linear modeling. In addition, she is collaborating with Alibali on the statistical analysis for an IES-funded study of math instruction and learning.
Research led by Mark Seidenberg (psychology) focuses on how educators can respond to dialect mismatch as a strategy to prevent and reduce achievement gaps. To that end, he and his colleagues are developing interventions to increase the facility of African American children with standard American English so they can be more successful in school. This interdisciplinary project will have major policy implications once moved outside the laboratory and into school systems. Alibali and Harackiewicz also lead development studies. One is an IES-funded study on how teachers can use visual scaffolding to communicate during instruction. The project involves diverse methodologies, including quantitative and qualitative analysis of video data, randomized experiments in classroom and tutorial settings, and laboratory experiments with video delivery of instruction. Another is an NSF-funded project on developing new approaches for communicating mathematical ideas about space and motion.