Use of Data Analytics to Spot Educational Discrimination – Part III - A Focus on Demographics and COVID-19 Data
Advisor(s)
Dr. Angel Alvarez; Northwestern University, Feinberg School of Medicine
Discipline
Behavioral and Social Sciences
Start Date
21-4-2021 9:30 AM
End Date
21-4-2021 9:45 AM
Abstract
Chicago Public Schools (CPS) is the nation’s third largest school district and serves over 350,000 students within the city limits of Chicago. Yet, CPS draws scrutiny over its treatment of marginalized minority students as well as vast discrepancies in overall school performances. In order to spot potential inequities within the CPS system, we performed statistical analyses on publicly-available data and information obtained through FOIA requests on test scores, selective enrollment schools, discipline data, demographic changes, school reopenings, and other pertinent subjects. Analyses on median family income data, when compared to the CPS’ six-factor socioeconomic tier system, revealed that home ownership rates and the percentage of foreign language-speaking families were poor at determining the financial state of neighborhoods. A comparison of the percentages of students on free or reduced lunch across a four year span also echo growing trends of gentrification within Chicago. Finally, schools from more affluent, white-majority areas of the city saw much higher percentages of students returning to school following the pandemic compared to less affluent, Black/Latinx-majority areas. These trends mirror the lower vaccination percentages and higher death rates in less affluent regions, showing CPS’ and Chicago’s inability to effectively protect disenfranchised students.
Use of Data Analytics to Spot Educational Discrimination – Part III - A Focus on Demographics and COVID-19 Data
Chicago Public Schools (CPS) is the nation’s third largest school district and serves over 350,000 students within the city limits of Chicago. Yet, CPS draws scrutiny over its treatment of marginalized minority students as well as vast discrepancies in overall school performances. In order to spot potential inequities within the CPS system, we performed statistical analyses on publicly-available data and information obtained through FOIA requests on test scores, selective enrollment schools, discipline data, demographic changes, school reopenings, and other pertinent subjects. Analyses on median family income data, when compared to the CPS’ six-factor socioeconomic tier system, revealed that home ownership rates and the percentage of foreign language-speaking families were poor at determining the financial state of neighborhoods. A comparison of the percentages of students on free or reduced lunch across a four year span also echo growing trends of gentrification within Chicago. Finally, schools from more affluent, white-majority areas of the city saw much higher percentages of students returning to school following the pandemic compared to less affluent, Black/Latinx-majority areas. These trends mirror the lower vaccination percentages and higher death rates in less affluent regions, showing CPS’ and Chicago’s inability to effectively protect disenfranchised students.