Prediction of Student Performance in E-Learning Environment using Machine Learning Techniques

The increasing importance of education has led researchers and educators to investigate new methods to improve student performance and identify factors that affect academic success. The algorithm for predicting student performance described in this article uses machine learning techniques to predict student performance based on a variety of factors including behavior, history of education, and the public. The Student Performance Plan (SPP) uses real data collected from various schools to provide accurate and complete analysis. The plan begins with data preparation, which includes cleaning and organizing the data for analysis. This process includes checking for missing values, weighting the data, and making changes as necessary. The repeated factor elimination (RFE) selection method was used to identify the most important features in the model predictions of students. When tested, various types of machine learning were found to perform best in both cases. This study demonstrates the ability of machine learning algorithms to predict student performance and identify key factors affecting academic success. Educators and academic institutions can utilize this information to develop personalized interventions, learning experiences, and recommendations for students, potentially leading to improved educational outcomes.