Utilizing Machine Learning to Forecast a Student’s Performance

Platforms for online learning are flexible, yet problems like low engagement still exist. Predictive models (Rf, Svm, KNN, Extra Tree Classifier) are used to identify pupils who are at-risk, allowing teachers to intervene in a timely manner. The prediction model is trained and evaluated using ML and DL techniques, with algorithm selection determined by performance metrics. Based on the best outcomes for f-score, recall, accuracy, precision, and support, a selected approach is applied over a range of course lengths. Early detection of at-risk students enables instructors to take immediate action, strengthening efforts to avert difficulties in the classroom and encouraging greater participation, which eventually improves the general performance of students in the online learning environment.