Collaborative Proposal: A Data-Intensive Exploration of the Links Between SES and STEM Learning Outcomes

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The links between family SES and students' learning, in STEM and other disciplines, is well noted in the literature. However, the nature of the data that has been used for these studies is quite coarse grained and is increasingly hard to get as schools are no longer required to determine who is eligible for free and reduced-price lunch. The researchers in this project will use Supplemental Nutrition Assistance Program (SNAP) or food stamps data utilization data as a proxy for socioeconomic status (SES) to determine the utility of such data. In addition, educators are increasingly exhorted to use data to inform decision-making, though the data available and the data representations are not always sufficient for educator use. The collaborative interaction between researchers collecting and organizing data and stakeholders who are brought together to make sense of the data provides the strong context in which better interpretations of data are supported.The researchers in this exploratory study funded as part of the Ideas Lab for Data Intensive Research will examine the relationships among fluctuations in patterns of students' SES measured by daily transaction data using SNAP data and multiple STEM achievement, behavioral referral, and attendance outcomes. The researchers will also examine the extent to which the relationships identified can be visualized in ways that have utility to various stakeholders including school staff and those who work in youth-serving organizations. Three research questions will structure this research as follows: 1). In what ways can SNAP transaction data and student achievement data be used to explore the relationships between temporal variation in SES and STEM learning outcomes?; 2). How do stakeholders make sense of and how do they construct implications for action based on data intensive analyses and visualizations of students' time-varying SES?; and, 3). What supports do stakeholders need in order to construct implications for action and engage in collaborative data analyses? Food stamp data provided from North Carolina's Department of Health and Human Services will be integrated with data on students' data obtained from the North Carolina Education Research Data Center at Duke University. These data will be analyzed with longitudinal data analysis models, visual data analytics, and multi-level modeling techniques. Stakeholders from the Durham, North Carolina school district will include teachers, administrators and counselors from the districts' K-12 schools and youth-serving organizations. This research-practitioner collaboration will identify ways in which new data about students and new data visualizations support interpretations that enhance stakeholder's decision-making related to students' success in STEM.