Systematic Review: Enhancing Educational Performance Prediction using Machine Learning Algorithms

Abstract

Accurately forecasting student performance has received a lot of consideration in the world of education. For this reason, data mining techniques such as the Genetic Algorithm (GA) have emerged as viable tools. GA, which is inspired by natural selection and genetics, can discover patterns and construct prediction models from massive datasets. Benefits such as higher accuracy, scalability, and efficiency can be discovered through the inclusion of GA into educational performance prediction systems. The ability of GA to discern complex patterns, manage incredible datasets, and refine model parameters has the potential to advance educational performance prediction and improve student academic outcomes.