Using machine learning algorithms, we give a thorough experimental investigation of three ensemble feature selection techniques for task classification on educational data. All three categories—filter, wrapper, and embedded feature selection—are covered by the individual techniques. Advanced ensemble learning approaches, such as stacking, bagging, and boosting, have been shown to increase classification performance on a dataset of course-related variables in experiments conducted on the EDX 2013 dataset. Pre-processing procedures include encoding categorical features and using feature selection methods such as embedded CART, embedded LightGBM, wrapper recursive feature elimination, wrapper permutation, ANOVA F, and mutual information. To attain high accuracy (up to 96.45%), stacking combines predictions from basic models (such as logistic regression and decision trees) with a meta-model. By training several base estimators concurrently, bagging highlights resilience against overfitting and leverages models like as logistic regression and decision trees, with accuracies as high as 96.45%. With accuracies of up to 96.06%, iterative boosting employs Ada-Boost and Gradient Boosting to enhance poor learners. Several criteria, including accuracy, precision, recall, and F1-score, are used to assess these apparent approaches. In order to improve predicted performance in classification tasks, the study emphasizes the value of mixing several learning models and feature selection techniques.