Enhancing Student Performance Predictions Using Ensemble Models and Hyperparameter Tuning

In educational data mining, prediction of student performance is a critical function because it enables instructors to identify kids that require intervention and act on the insight before the child falls behind. As student data spreads, it is now essential to apply state-of-the-art machine learning techniques to increase data complexity and improve error rates of prediction. This work applies artificial intelligence to student performance by using ensemble modeling and fine-tuning hyperparameters in additive models. In this study, complex patterns and correlations with behavioral, demographic, and academic characteristics are sought by applying Gradient Boosting, Random Forest, and Voting Classifiers which are advanced approaches in data analysis. To bolster the strength of these kinds of model’s parameter tuning is applied. The outcomes concluded with a peak of 90% and showed that using state-of-the-art machine learning techniques for analyzing educational information is quite feasible. This study provides educators with valuable knowledge and resources to quickly and effectively change students’ grades by illustrating real-world strategies to help them identify at-risk learners.