We are currently accepting applications
The Center for Education Policy Analysis (CEPA) is seeking undergraduate research assistants (RAs) to work directly with CEPA faculty on active research projects supported by The Office of the Vice Provost for Undergraduate Education (VPUE). Applications will be reviewed as received. Once decisions have been made, applicants will be notified by their faculty mentor.
Eligibility: The CEPA URP program is only open to current Stanford University undergraduates. Students must be enrolled in undergraduate studies in the quarter when they apply for the grant and carry an undergraduate status throughout the period of their project. Selection of RAs will be based on the student’s expressed interest in the project and the fit between faculty needs and student skill sets. Experience working with quantitative data using STATA statistical software is preferred but not required.
Application Process: Students should provide a resume, an unofficial Stanford transcript, and a one-page cover letter describing the applicant’s interest in education policy, previous research experience including any experience with quantitative analyses, and indicate the particular research project/s the student is interested in working on.
Project 1: Predictive Analytics
Faculty Mentor: Eric Bettinger
Project Description: Predictive analytics has been touted as a means for improving targeting of student service programs in higher education. However, little has been done to test whether the predictive analytics improve outcomes. We examine two critical steps. First, we examine whether predictive analytic models can help identify students who can most benefit from interventions. How can the models be improved, especially in the early weeks of students’ collegiate experience. Second, we examine whether interventions targeting identified students can significantly improve retention. The project utilizes high frequency data on student engagement in online courses as well as text analysis to create additional predictors. Basic skills in R, Python, and/or Stata are necessary to contribute to the project.
Project 2: The Promise, Pitfalls, and Possibilities of Gifted Education
Faculty Mentor: Francis Alvin Pearman
Project Description: Gifted programs are designed to provide additional academic services for children who have been identified as “gifted” or “talented.” Despite the seeming universal availability of gifted programs, concerns abound about the criteria and process by which students are assigned to them—specifically how patterns of access and identification map onto prevailing patterns of educational inequality related to race and socioeconomic status. The purpose of this project is to shed new light on gifted and talented programming using detailed data on achievement patterns and gifted rates in schools and districts across the United States. This research project is in need of RAs who will conduct literature searchers on gifted and talented programming and collect data on specific gifted programs from the websites of a sample of school districts. The ideal candidate will have strong writing and organizational skills, have comfort with Microsoft Excel, and have interest in issues related to educational equity.
Project 3: Gentrification and Urban Schooling
Faculty Mentor: Francis Alvin Pearman
Project Description: A growing number of low-income central city neighborhoods are experiencing an influx of affluent households and rising property values—a pattern commonly referred to as gentrification. Notably, these changes have altered the challenges and opportunities of urban schooling in the 21st century. This research project is seeking RAs with interest in gentrification and its relation to urban schooling who will help create a platform that will enable urban schools and educational-stakeholders to make more informed decisions about how best to address gentrification-related pressures in their surrounding communities. The research assistant will be tasked with conducting (virtual) interviews with district leaders, assisting with data cleaning and management, assisting with writing literature reviews and policy briefs, and participating in internal project meetings. The ideal candidate is a self-starter with excellent writing and organizational skills who cares deeply about cities, schools, and issues of educational equity.
Project 4: The Educational Opportunity Project
Faculty Mentor: sean reardon
Project Description: The Educational Opportunity Project (EOP) is a research project dedicated to using data to inform education policy and increase student opportunity. EOP’s analysis builds off of the Stanford Education Data Archive (SEDA), a unique database that measures educational outcomes as well as student demographics and inequality. SEDA is the first nationwide archive of education test score data, now including over 300 million standardized test scores. Using these data, SEDA publishes annual data on student achievement and achievement gaps for every public school and school district in the United States. SEDA is designed to provide scholars, policymakers, educators, and journalists with detailed information on patterns of educational opportunity and outcomes across the U.S., with the expectation that such evidence will inform and improve educational policies and practices. The SEDA project is directed by Professor Sean F. Reardon, Professor of Poverty and Inequality in Education at Stanford Graduate School of Education and Co-Directed by Erin Fahle, Assistant Professor at the St. John’s University School of Education. (more information about SEDA is available at http://seda.stanford.edu).
Project 5: Biological Bases of Self-Regulated Learning
Faculty Mentor: Jelena Obradović
Project Description: In this project, we are examining (1) how young children’s stress physiology is affected by the everyday challenges that occur in educational settings; and (2) whether children’s acute physiological responses are malleable in ways that can promote successful learning outcomes. The goal is to identify ways to help young children achieve and maintain well-regulated physiology and behavior despite acute learning-related challenges.
Dr. Jelena Obradović (https://web.stanford.edu/group/sparklab/) is seeking research assistants interested in the interplay of biology, behavior, and environment. The research assistant will assist with basic research that will lay the groundwork for experimental intervention studies designed to improve children's emotional and physiological self-regulation skills. The research assistant will have the opportunity to work on multiple tasks focused on a project about the biological bases of self-regulated learning. Research assistants will learn about the significance of autonomic nervous system functioning. They will assist in processing and cleaning behavioral and electrocardiogram (EKG) data collected from almost 700 children last summer. Research assistants will also learn how to create literature reviews of relevant topics. The ideal candidate will have great organization and communication skills and attention to detail. Work hours are flexible and may include weekdays and/or weekends. All work will be remote for foreseeable future. Having access to reliable internet is required.
Project 6: Data Cleaning and Analysis for Quantitative Research in Early Childhood Education and Elementary School
Faculty Mentor: Jelena Obradović
Project Description: : In this project, we will examine how different aspects of preschool (Pre-K) classroom experience (e.g., emotional, instructional, organizational) uniquely relate to the growth of children’s academic, social, and emotional learning skills (SEL) as well as school outcomes as they enter K-5 education. We will investigate whether these associations vary by children’s ethnic background, language proficiency, and initial skill levels. We are answering research questions such as “which students are likely to benefit from a Pre-K program the most?”
Dr. Jelena Obradović (https://web.stanford.edu/group/sparklab/) is seeking research assistants who will work with the current data manager to assist with data cleaning, processing, and analysis. Her team is creating longitudinal panels of data that include children’s enrollment and attendance in early childhood education programs as well as K-5 classrooms, measures of classroom and teacher quality, and measures of social and emotional learning and academic skills across several cohorts of children. The measures need to be linked between grades, teachers, classrooms, and students over time in order to answer the research questions. The research assistant will gain practical experience in quantitative research and will learn about standardized early childhood SEL assessments and early childhood development. The ideal candidate will be detail-oriented, communicative, and be available up to 10 hours per week. Knowledge of R is required. Work hours are flexible and will be negotiated with Dr. Obradović and her staff. All work will be remote for foreseeable future. Having access to reliable internet is required.