The first paper, “Navigating heavy (metal) data: Lead exposure and educational outcomes” discusses the process through which a harmonized education dataset was created. This ‘big data’ dataset includes highly sensitive information and therefore extreme care was taken in terms of community engagement and data security. While the final dataset will be deidentified, authors focused on creating the dataset with ongoing communication with community stakeholders. In their presentation, the authors will emphasize the importance of establishing a strong relationship with key stakeholders as a key aspect of big data within education research in prevention science.
The second paper, “Challenges in using data from a large urban school district to answer questions about the impacts of children entering kindergarten not socially-behaviorally ready to learn,” uses a large administrative dataset from an urban school district to understand the longer term impacts of a lack of school readiness on key educational outcomes. Use of this large dataset leads to interesting methodological challenges, which are not limited to these types of educational data, including data quality issues and missing data concerns. Results are presented within the context of these challenges and possible solutions are discussed.
The third paper, “Using Big Data to Inform Prevention Science in Maryland,” uses data from a large statewide repository, with a focus on the role of poverty in key outcomes among public school students including standardized test scores, school drop-out, and college enrollment. Along with discussing the analytic and ethical challenges faced when utilizing this large dataset, authors will discuss how findings from big data analyses have informed policy and practice.
Finally, a discussant will highlight commonalities among the papers, discuss implications for prevention, and moderate a discussion between the presenters and the audience.