Exploring Educational Equity: A Machine Learning Approach to Unravel
  Achievement Disparities in Georgia
Yichen Ma Dima Nazzal
The COVID-19 pandemic has significantly exacerbated existing educational
disparities in Georgia's K-12 system, particularly in terms of racial and
ethnic achievement gaps. Utilizing machine learning methods, the study conducts
a comprehensive analysis of student achievement rates across different
demographics, regions, and subjects. The findings highlight a significant
decline in proficiency in English and Math during the pandemic, with a
noticeable contraction in score distribution and a greater impact on
economically disadvantaged and Black students. Socio-economic status, as
represented by the Directly Certified Percentage -- the percentage of students
eligible for free lunch, emerges as the most crucial factor, with additional
insights drawn from faculty resources such as teacher salaries and expenditure
on instruction. The study also identifies disparities in achievement rates
between urban and rural settings, as well as variations across counties,
underscoring the influence of geographical and socio-economic factors. The data
suggests that targeted interventions and resource allocation, particularly in
schools with higher percentages of economically disadvantaged students, are
essential for mitigating educational disparities.