Machine Learning Techniques for Evaluating Student Performance

In various sectors, particularly in education, artificial intelligence has significantly influenced outcomes by deploying AI algorithms to gain valuable insights into student learning processes, emphasizing the importance of assessing students’ knowledge for understanding their learning levels and improving educational strategies. Traditional assessment methods have limitations due to biases and time constraints. With advancements in artificial intelligence and machine learning, this study employs machine learning algorithms to create a predictive model identifying students with academic challenges. By utilizing diverse features, this research identifies key factors influencing academic outcomes, achieving an impressive classification accuracy of 90.91%. Logistic Regression outperformed other models like XGBoost and Gradient Boost. Integrating AI and ML techniques revolutionizes assessment methods, offering objective insights for timely interventions. This approach not only enhances students’ academic journey but also creates a conducive learning environment, promoting sustainable academic success in higher education.