Prediction of Student Performance in Learning Online Courses using Ensemble Learning Algorithms

Understanding and forecasting student performance has become essential for educators and administrators due to the widespread use of online learning platforms. This research study examines how well ensemble learning algorithms predict the performance of students enrolled in distance learning programs. Ensemble learning, which combines several base learners to increase generalization and prediction accuracy, presents a viable method for handling the challenging nature of student performance prediction problems. This research examines an ensemble learning method that analyses student data from online learning platforms using Hidden Markov Models (HMM) and XGBoost. The data gathered from multiple web sources was trained using the Decision Trees (DT) training technique. The comprehensive feature collection, comprising engagement metrics, demographic data, and past academic achievements, was obtained from student interactions through Ensemble feature extraction. The ensemble models use these features as inputs and are trained to predict student outcomes like retention, grades, and course completion. Through thorough testing and assessment of real-world datasets, it has been shown that ensemble learning successfully predicts student performance in various online courses and academic fields. When comparing the performance of ensemble models to conventional single-model methods, it is possible to see considerable gains in prediction resilience and accuracy. Lastly, the interpretability of ensemble models to learn more about the elements influencing how well students succeed in virtual learning environments. Several functional patterns and connections are identified that can guide intervention tactics, instructional design, and personalized learning experiences by examining feature importance and model explanations.