Exploring the Impact of Elevated Learning Methodology on Student Performance Prediction: An Empirical Analysis

This study delves into the impact of Elevated Learning Methodology on student performance prediction through an empirical analysis utilizing Elevated Learning for Student Performance Prediction (ELSPP). The ELSPP model, implemented using Python, integrates advanced techniques such as AutoEncoder and Long Short-Term Memory (LSTM) networks to augment the accuracy of the student performance prediction process. Leveraging a diverse dataset, the study demonstrates an accuracy of 97.75%, underscoring the effectiveness of the Elevated Learning (EL) approach in forecasting student outcomes. The Python implementation of the ELSPP model not only facilitates reproducibility but also enables educators and researchers to leverage and adapt the codebase for further exploration and refinement. The results support the adoption of sophisticated neural network architectures in educational analytics, offering a promising way for personalized learning interventions and the development of robust academic support systems.