Use of Data Analytics to Spot Educational Discrimination – A Focus on Standardized Testing and Selective Enrollment Schools

We utilized data analytics to investigate potential inequitable practices and policies within the Chicago Public Schools (CPS) system that may negatively affect underrepresented and disadvantaged students. We analyzed large datasets to determine if CPS policies and practices contribute to low standardized testing performance across different schools in the district. We focused on factors that contribute to school rankings, standardized testing, and admissions within selective enrollment schools. We compiled, organized, and analyzed school performance and census tract datasets acquired through Freedom of Information Act (FOIA) requests. We compared individual schools’ standardized testing growth rates to other district schools’ rates and national average growth rates. Low student performance is more common among students from low socioeconomic backgrounds, students of color, and students with disabilities. We also investigated equity concerns regarding CPS’s tier system used to determine admissions to selective enrollment schools and have found flaws in the method used to calculate tiers, suggesting that a better model can be constructed. Our results highlight several areas of concern involving standardized testing results across multiple schools in the district. Ultimately, we have identified large numbers of schools with data anomalies that raise concerns and call attention to the need for reform in CPS.