Enhancing Student Performance Prediction in E-Learning Environments: Advanced Ensemble Techniques and Robust Feature Selection

By means of a thorough investigation of ensemble methodologies and feature selection approaches, this work explores enhancing predictive modelling in e-learning contexts. The setting is in the growing significance of data-driven decision-making in education and tailored learning programs. The main concern is how to fairly forecast student performance in environments of digital learning. This work intends to solve gaps by investigating new ensemble models and robust feature selection techniques based on already published research. Using cutting-edge analytical techniques including hybrid BR2-2T models and the Chi-square test, the study produces remarkable accuracy surpassing known limits. The results underline the need of feature selection and ensemble methods in improving forecast accuracy and dependability. Finally, this study marks a major step in the field of e-learning predictive modelling since it helps to improve educational results and enable data-driven interventions.