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

家庭SES和学生的学习之间的联系，在STEM和其他学科，是在文献中很好地指出。然而，用于这些研究的数据的性质是相当粗糙的，并且越来越难以获得，因为学校不再需要确定谁有资格获得免费和降价的午餐。该项目的研究人员将使用补充营养援助计划（SNAP）或食品券数据利用数据作为社会经济地位（SES）的代理，以确定这些数据的效用。 此外，教育工作者越来越多地被劝告使用数据来为决策提供信息，尽管现有的数据和数据表示并不总是足以供教育工作者使用。收集和组织数据的研究人员与利益相关者之间的协作互动为更好地解释数据提供了强有力的支持。这项探索性研究的研究人员作为数据密集型研究的思想实验室的一部分，将研究学生SES模式波动之间的关系，这些模式通过使用SNAP数据的日常交易数据测量，多个STEM成就，行为转介和出勤结果。研究人员还将研究在何种程度上确定的关系可以可视化的方式，有效用的各种利益相关者，包括学校工作人员和那些谁在青年服务组织的工作。本研究将围绕以下三个问题展开：1）。SNAP交易数据和学生成绩数据可以通过哪些方式来探索SES的时间变化与STEM学习成果之间的关系？2）。利益相关者如何理解学生随时间变化的社会经济地位，以及他们如何根据数据密集型分析和可视化来构建行动的含义？（3）.利益攸关方需要哪些支持，以构建行动的影响并参与协作数据分析？从北卡罗来纳州卫生与公众服务部提供的食品券数据将与从杜克大学北卡罗来纳州教育研究数据中心获得的学生数据相结合。这些数据将通过纵向数据分析模型、可视化数据分析和多层次建模技术进行分析。来自北卡罗来纳州达勒姆学区的利益相关者将包括来自该地区K-12学校和青年服务组织的教师、行政人员和辅导员。这项研究与实践者的合作将确定有关学生的新数据和新数据可视化支持解释的方式，以增强利益相关者与学生在STEM中成功相关的决策。

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.