Predictive Analytics in Educational Outcomes

Abstract

Using a large dataset that includes students' grades, demographic information, and other educational variables from three American high schools, this research work investigates the predictive modeling of students' mathematical performance. Gender, race/ethnicity, parental education, lunch subsidy status, standardized test results (math, reading, and writing), and course enrollment in test preparation are all part of the dataset. The purpose of this study is to examine the relationship between students' socioeconomic status and their mathematical achievement and to discover important predictors of this achievement using sophisticated machine learning algorithms such as ensemble methods, decision trees, and linear regression. A more complex picture of the factors that lead to mathematical achievement can be gained from the study, which uncovers illuminating relationships across demographic variables, educational interventions, and academic results. The results highlight the promise of predictive analytics for developing individualized plans to improve students' educational experiences. Educators, legislators, and future researchers can benefit from data-driven methods of educational planning and decision-making, which is highlighted in the paper's examination of the findings' ramifications.