Prediction of Learner Performance Based on Self-esteem Using Machine Learning Techniques: Comparative Analysis

Self-esteem is a pivotal factor influencing students’ academic outcomes, fostering resilience, and cultivating a positive attitude toward learning. It promotes active classroom participation, question-asking, and collaboration, driving goal achievement. This study builds upon our previous work with refinements to our approach and objectives. Here, we extend prior findings by further developing predictive traits for learners’ success. We rigorously assess the accuracy of various machine-learning models in predicting these traits, offering insights into their effectiveness. Our comparative analysis not only elucidates algorithm performance but also establishes a benchmark for selecting optimal technologies in the creation of performance-focused adaptive learning systems. This research contributes to the advancement of predictive models for academic success.