Use of Data Analytics to Spot Educational Discrimination
Session Number
Project ID: BHVSO 14
Advisor(s)
Dr. Angel Alvarez; Northwestern University Feinberg School of Medicine
Discipline
Behavioral and Social Sciences
Start Date
22-4-2020 9:10 AM
End Date
22-4-2020 9:25 AM
Abstract
We utilized data analytics to investigate potential inequitable practices and policies within the Chicago Public Schools (CPS) system that may negatively affect disadvantaged students. We analyzed large datasets to determine if CPS policies and practices have contributed to low performance across different schools in the district. Low student performance tends to be more common among students from low socioeconomic backgrounds, students of color, along with students with disabilities. We focused on factors that contribute to school rankings, standardized testing, and selective enrollment school admissions. We compiled, organized, and analyzed large datasets acquired through Freedom of Information Act (FOIA) requests available online. Unfortunately, CPS refused to comply with our records request, entailing appeals to the Office of the Attorney General. Apart from that, our results highlight several areas of concern involving attendance, standardized testing results, and admission policies for selective enrollment schools in the district. These results complement a just-released investigation concerning potential fraud in the administration of standardized tests by CPS officials. Specifically, we identified a larger number of schools with data anomalies that raise questions of fraud and calls attention to the need for reform in CPS.
Use of Data Analytics to Spot Educational Discrimination
We utilized data analytics to investigate potential inequitable practices and policies within the Chicago Public Schools (CPS) system that may negatively affect disadvantaged students. We analyzed large datasets to determine if CPS policies and practices have contributed to low performance across different schools in the district. Low student performance tends to be more common among students from low socioeconomic backgrounds, students of color, along with students with disabilities. We focused on factors that contribute to school rankings, standardized testing, and selective enrollment school admissions. We compiled, organized, and analyzed large datasets acquired through Freedom of Information Act (FOIA) requests available online. Unfortunately, CPS refused to comply with our records request, entailing appeals to the Office of the Attorney General. Apart from that, our results highlight several areas of concern involving attendance, standardized testing results, and admission policies for selective enrollment schools in the district. These results complement a just-released investigation concerning potential fraud in the administration of standardized tests by CPS officials. Specifically, we identified a larger number of schools with data anomalies that raise questions of fraud and calls attention to the need for reform in CPS.