February 10, 2014 – If big-city school systems had a clear picture of the risks that put their students most in danger of falling behind academically, educators and policy makers could build locally targeted solutions for the achievement gap. Last week, Educational Researcher published a University of Pennsylvania Graduate School of Education (Penn GSE) study that moves that idea closer to reality. Led by Dr. John Fantuzzo, the research team focused on using integrated administrative data to more fully understand complicating factors for children at risk – and the impact of multiple risks on the broader educational environment.
In schools with high concentrations of students with risk factors, such as homelessness and low maternal education, the performance of all students—not just those experiencing these risks—was negatively impacted. The study, which looked at third grade students in Philadelphia, demonstrates that race and poverty are not telling the whole story in examining educational well-being, and that problem-solving focused on certain children can benefit the entire school.
In the study, Fantuzzo and his co-authors used an integrated data system, which they helped to develop. This system, while protecting student privacy, combined records stored by the schools and social service agencies for thousands of third-graders from across Philadelphia to study the relations between risks and educational outcomes.
Third graders were chosen because third grade is the first time that children take state-mandated achievement tests, giving the researchers a consistent measure of academic performance. The data, which had been collected by public agencies over the children’s lifetimes and even extended back before their births to the time of their mothers’ pregnancies, enabled the researchers to use sophisticated analytic techniques to find the association between academic performance and various risks and protective factors over time.
Here is a summary of the peer-reviewed article:
“An Investigation of the Relations Between School Concentrations of Student Risk Factors and Student Educational Well-Being,” by John W. Fantuzzo, Whitney A. LeBoeuf, and Heather L. Rouse, investigates the relationship between school concentrations of student risk factors such as homelessness, maltreatment, and low maternal education, and measures of reading, mathematics, and attendance. The authors, examining an entire cohort of third-grade students in the School District of Philadelphia, document the negative impact of high concentrations of students with risk factors on the other children who attend school with these peers but are, themselves, not experiencing these risk factors. Large concentrations of students with low maternal education, homelessness, and child maltreatment were found to be among the most harmful to overall student performance, after accounting for student-level risks and demographics. The findings show that poverty and race do not tell the whole story when it comes to educational well-being.
The integrated data system in Philadelphia, which began in 2002, is part of Actionable Intelligence for Social Policy (AISP), an initiative funded by the John D. and Catherine T. MacArthur Foundation through a grant to Penn professors Dennis Culhane, School of Social Policy and Practice, and John Fantuzzo, Graduate School of Education.While harnessing the power of large amounts of public data to find solutions to civic problems is a recent trend, AISP has been in the big data field for many years. AISP was ahead of the curve, using research and relationship-building to tackle the complicated challenges of integrating disparate data systems in order to reveal previously unseen patterns, patterns that can teach us about the impact of homelessness on reading skills, send emergency services to those most in need during a crisis, or help medical professionals care for rural patients more effectively. AISP is building a nationwide network of agencies that use integrated data systems to answer big questions; create efficiencies; and solve the conundrums of technical disparities, legal roadblocks, and privacy challenges along the way – essentially to use data for the common good.